[SCENARIO]
Family: F5 (Feasibility and Model Repair)
Archetype: retail_f5_ultimate_stress
Scenario ID: retail_f5_ultimate_stress_v0

[BUSINESS DESCRIPTION]
Business narrative:
This is a composite stress test that combines three stressors: tight storage capacity, a mid-horizon production failure during a peak period, and the absence of any substitution between products. Demand is local and exogenous at each location. The scenario is designed as a boss-level test where the only viable strategy is to accept significant lost sales while respecting all physical constraints.

Structure cues:
- The archetype combines the storage bottleneck of retail_f3_storage_bottleneck (30% capacity), the peak supply failure of retail_f4_peak_failure (production=0 for periods 8-11), and the no-substitution structure of retail_f2_no_substitution (sub_edges=[]).
- Shelf life: Each product has a shelf life in periods. Inventory must be tracked by REMAINING LIFE.

  VARIABLE DEFINITION: I[p,l,t,r] = inventory at START of period t with r periods remaining.
  Convention: r=1 is OLDEST (sell first FIFO), r=shelf_life[p] is FRESHEST.

  KEY EQUATIONS - implement EXACTLY as written, do NOT add or remove terms:

  (1) Fresh inflow: I[p,l,t,SL] = Q[p,t] * demand_share[l]
      - This is ONLY the inflow from production. Do NOT subtract sales here!
      - Q is total production, demand_share distributes it to locations.

  (2) Aging: I[p,l,t+1,r] = I[p,l,t,r+1] - sales[p,l,t,r+1] for r=1..SL-1
      - Inventory tomorrow with r remaining = inventory today with r+1 remaining - sales

  (3) Waste: W[p,l,t] = I[p,l,t,1] - sales[p,l,t,1]
      - Items with r=1 that aren't sold become waste

  (4) Sales availability: sales[p,l,t,r] <= I[p,l,t,r]
      - Can only sell what you have in inventory

  (5) Inventory holding cost: charged on (I[p,l,t,r] - sales[p,l,t,r]) for r >= 2
      - Charge on END-of-period inventory (after sales), exclude expiring items (r=1)
- Storage capacity: sum over products of (cold_usage[p] * total_inventory[p,l,t]) <= cold_capacity[l]. Storage capacities are scaled to 30% of normal values and must be enforced as binding constraints.
- Production capacities are set to zero during a mid-horizon peak window (periods 8-11), so no new units can be produced in those periods.
- The substitution graph is empty (sub_edges = []), so each product's demand can only be served by its own inventory at the location.
- Inventory at each product, location, and period remains non-negative and reflects previous stock, allowed production, sales, and waste. Unmet demand is explicitly treated as lost sales.
- There is no transshipment and lead times are zero. Labor capacity remains generous.
- The objective is to minimize total cost (aggregating purchasing, holding, waste, and lost sales) while handling simultaneous capacity, supply, and assortment stress without violating feasibility.

[DATA SCHEMA]
{
  "name": str,                          # scenario identifier
  "periods": int,                       # number of time periods
  "products": [str, ...],               # list of product IDs
  "locations": [str, ...],              # list of location IDs

  "shelf_life": {p: int},               # shelf life in periods per product
  "lead_time": {p: int},                # order lead time per product (0 = same-period arrival)

  "demand_curve": {p: [float, ...]},    # demand per product per period (0-indexed list)
  "demand_share": {l: float},           # fraction of total demand at each location

  "production_cap": {p: [float, ...]},  # max production per product per period (0-indexed list)
  "cold_capacity": {l: float},          # storage capacity per location
  "cold_usage": {p: float},             # storage units per unit of product

  "labor_cap": {l: [float, ...]},       # labor hours per location per period (0-indexed list)
  "labor_usage": {p: float},            # labor hours per unit sold
  "return_rate": {p: float},            # fraction of sales returned next period

  "costs": {
    "purchasing": {p: float},           # cost per unit ordered
    "inventory": {p: float},            # holding cost per unit per period
    "waste": {p: float},                # cost per unit expired
    "lost_sales": {p: float},           # penalty per unit of unmet demand
    "fixed_order": float,               # fixed cost per order placed
    "transshipment": float              # cost per unit transshipped
  },

  "constraints": {
    "moq": float,                       # minimum order quantity (0 = no MOQ)
    "pack_size": int,                   # order must be multiple of this (1 = no constraint)
    "budget_per_period": float|null,    # max purchasing cost per period
    "waste_limit_pct": float|null       # max waste as fraction of total demand
  },

  "network": {
    "sub_edges": [[p_from, p_to], ...], # substitution: p_from's demand can be served by p_to
    "trans_edges": [[l_from, l_to], ...]# transshipment: can ship from l_from to l_to
  }
}

[DATA ACCESS]
- The variable `data` is pre-loaded. Do NOT use file I/O.
- Lists are 0-indexed (period t in model uses index [t-1] in data arrays)

CRITICAL - Network edges require tuple conversion for Gurobi:
  sub_edges = [tuple(e) for e in data.get('network', {}).get('sub_edges', [])]
  trans_edges = [tuple(e) for e in data.get('network', {}).get('trans_edges', [])]

[OUTPUT FORMAT]
- Import: import gurobipy as gp; from gurobipy import GRB
- Set Gurobi params: m.Params.OutputFlag = 0; m.Params.Threads = 1; m.Params.Seed = 0
- Print at end:
  print(f"status: {{m.Status}}")
  if m.Status == 2:
      print(f"objective: {{m.ObjVal}}")
- Output ONLY executable Python code. No markdown, no explanations.

[TASK]
Write a GurobiPy script that models and solves this optimization problem.

[SCENARIO]
Family: F5 (Feasibility and Model Repair)
Archetype: retail_f5_ultimate_stress
Scenario ID: retail_f5_ultimate_stress_v0

[BUSINESS DESCRIPTION]
Business narrative:
This is a composite stress test that combines three stressors: tight storage capacity, a mid-horizon production failure during a peak period, and the absence of any substitution between products. Demand is local and exogenous at each location. The scenario is designed as a boss-level test where the only viable strategy is to accept significant lost sales while respecting all physical constraints.

Structure cues:
- The archetype combines the storage bottleneck of retail_f3_storage_bottleneck (30% capacity), the peak supply failure of retail_f4_peak_failure (production=0 for periods 8-11), and the no-substitution structure of retail_f2_no_substitution (sub_edges=[]).
- Shelf life: Each product has a shelf life in periods. Inventory must be tracked by REMAINING LIFE.

  VARIABLE DEFINITION: I[p,l,t,r] = inventory at START of period t with r periods remaining.
  Convention: r=1 is OLDEST (sell first FIFO), r=shelf_life[p] is FRESHEST.

  KEY EQUATIONS - implement EXACTLY as written, do NOT add or remove terms:

  (1) Fresh inflow: I[p,l,t,SL] = Q[p,t] * demand_share[l]
      - This is ONLY the inflow from production. Do NOT subtract sales here!
      - Q is total production, demand_share distributes it to locations.

  (2) Aging: I[p,l,t+1,r] = I[p,l,t,r+1] - sales[p,l,t,r+1] for r=1..SL-1
      - Inventory tomorrow with r remaining = inventory today with r+1 remaining - sales

  (3) Waste: W[p,l,t] = I[p,l,t,1] - sales[p,l,t,1]
      - Items with r=1 that aren't sold become waste

  (4) Sales availability: sales[p,l,t,r] <= I[p,l,t,r]
      - Can only sell what you have in inventory

  (5) Inventory holding cost: charged on (I[p,l,t,r] - sales[p,l,t,r]) for r >= 2
      - Charge on END-of-period inventory (after sales), exclude expiring items (r=1)
- Storage capacity: sum over products of (cold_usage[p] * total_inventory[p,l,t]) <= cold_capacity[l]. Storage capacities are scaled to 30% of normal values and must be enforced as binding constraints.
- Production capacities are set to zero during a mid-horizon peak window (periods 8-11), so no new units can be produced in those periods.
- The substitution graph is empty (sub_edges = []), so each product's demand can only be served by its own inventory at the location.
- Inventory at each product, location, and period remains non-negative and reflects previous stock, allowed production, sales, and waste. Unmet demand is explicitly treated as lost sales.
- There is no transshipment and lead times are zero. Labor capacity remains generous.
- The objective is to minimize total cost (aggregating purchasing, holding, waste, and lost sales) while handling simultaneous capacity, supply, and assortment stress without violating feasibility.

[DATA]
The following JSON contains all instance data. Parse it directly in your code.

```json
{
  "name": "retail_f5_ultimate_stress_v0",
  "description": "Standard seasonal retail scenario.",
  "periods": 20,
  "products": [
    "SKU_Basic",
    "SKU_Premium",
    "SKU_ShortLife"
  ],
  "locations": [
    "DC1",
    "DC2",
    "DC3",
    "DC4",
    "DC5"
  ],
  "shelf_life": {
    "SKU_Basic": 10,
    "SKU_Premium": 8,
    "SKU_ShortLife": 4
  },
  "lead_time": {
    "SKU_Basic": 0,
    "SKU_Premium": 0,
    "SKU_ShortLife": 0
  },
  "cold_capacity": {
    "DC1": 1200.0,
    "DC2": 1050.0,
    "DC3": 900.0,
    "DC4": 900.0,
    "DC5": 750.0
  },
  "cold_usage": {
    "SKU_Basic": 1.0,
    "SKU_Premium": 3.0,
    "SKU_ShortLife": 1.2
  },
  "production_cap": {
    "SKU_Basic": [
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      0,
      0,
      0,
      0,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800
    ],
    "SKU_Premium": [
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      0,
      0,
      0,
      0,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400
    ],
    "SKU_ShortLife": [
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      0,
      0,
      0,
      0,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500
    ]
  },
  "labor_cap": {
    "DC1": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC2": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC3": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC4": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC5": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ]
  },
  "labor_usage": {
    "SKU_Basic": 0.0,
    "SKU_Premium": 0.0,
    "SKU_ShortLife": 0.0
  },
  "return_rate": {
    "SKU_Basic": 0.0,
    "SKU_Premium": 0.0,
    "SKU_ShortLife": 0.0
  },
  "demand_curve": {
    "SKU_Basic": [
      303,
      311,
      328,
      365,
      435,
      549,
      711,
      906,
      1100,
      1245,
      1300,
      1245,
      1100,
      906,
      711,
      549,
      435,
      365,
      328,
      311
    ],
    "SKU_Premium": [
      151,
      155,
      164,
      182,
      217,
      274,
      355,
      453,
      550,
      622,
      650,
      622,
      550,
      453,
      355,
      274,
      217,
      182,
      164,
      155
    ],
    "SKU_ShortLife": [
      121,
      124,
      131,
      146,
      174,
      219,
      284,
      362,
      440,
      498,
      520,
      498,
      440,
      362,
      284,
      219,
      174,
      146,
      131,
      124
    ]
  },
  "demand_share": {
    "DC1": 0.25,
    "DC2": 0.2,
    "DC3": 0.2,
    "DC4": 0.2,
    "DC5": 0.15
  },
  "costs": {
    "lost_sales": {
      "SKU_Basic": 50.0,
      "SKU_Premium": 80.0,
      "SKU_ShortLife": 40.0
    },
    "inventory": {
      "SKU_Basic": 1.0,
      "SKU_Premium": 1.5,
      "SKU_ShortLife": 1.0
    },
    "waste": {
      "SKU_Basic": 2.0,
      "SKU_Premium": 3.0,
      "SKU_ShortLife": 2.0
    },
    "fixed_order": 0.0,
    "transshipment": 0.5,
    "purchasing": {
      "SKU_Basic": 10.0,
      "SKU_Premium": 20.0,
      "SKU_ShortLife": 15.0
    }
  },
  "constraints": {
    "moq": 0,
    "pack_size": 1,
    "budget_per_period": null,
    "waste_limit_pct": null
  },
  "network": {
    "sub_edges": [],
    "trans_edges": []
  }
}
```

[OUTPUT FORMAT]
- Import: import gurobipy as gp; from gurobipy import GRB; import json
- Set Gurobi params: m.Params.OutputFlag = 0; m.Params.Threads = 1; m.Params.Seed = 0
- Print at end:
  print(f"status: {{m.Status}}")
  if m.Status == 2:
      print(f"objective: {{m.ObjVal}}")
- Output ONLY executable Python code. No markdown, no explanations.

[TASK]
Write a GurobiPy script that:
1. Parses the JSON data above (use json.loads on the string)
2. Models and solves the optimization problem
3. Prints status and objective value

Standard seasonal retail scenario.


[SCENARIO]
Family: F5 (Feasibility and Model Repair)
Archetype: retail_f5_ultimate_stress
Scenario ID: retail_f5_ultimate_stress_v1

[BUSINESS DESCRIPTION]
Business narrative:
This is a composite stress test that combines three stressors: tight storage capacity, a mid-horizon production failure during a peak period, and the absence of any substitution between products. Demand is local and exogenous at each location. The scenario is designed as a boss-level test where the only viable strategy is to accept significant lost sales while respecting all physical constraints.

Structure cues:
- The archetype combines the storage bottleneck of retail_f3_storage_bottleneck (30% capacity), the peak supply failure of retail_f4_peak_failure (production=0 for periods 8-11), and the no-substitution structure of retail_f2_no_substitution (sub_edges=[]).
- Shelf life: Each product has a shelf life in periods. Inventory must be tracked by REMAINING LIFE.

  VARIABLE DEFINITION: I[p,l,t,r] = inventory at START of period t with r periods remaining.
  Convention: r=1 is OLDEST (sell first FIFO), r=shelf_life[p] is FRESHEST.

  KEY EQUATIONS - implement EXACTLY as written, do NOT add or remove terms:

  (1) Fresh inflow: I[p,l,t,SL] = Q[p,t] * demand_share[l]
      - This is ONLY the inflow from production. Do NOT subtract sales here!
      - Q is total production, demand_share distributes it to locations.

  (2) Aging: I[p,l,t+1,r] = I[p,l,t,r+1] - sales[p,l,t,r+1] for r=1..SL-1
      - Inventory tomorrow with r remaining = inventory today with r+1 remaining - sales

  (3) Waste: W[p,l,t] = I[p,l,t,1] - sales[p,l,t,1]
      - Items with r=1 that aren't sold become waste

  (4) Sales availability: sales[p,l,t,r] <= I[p,l,t,r]
      - Can only sell what you have in inventory

  (5) Inventory holding cost: charged on (I[p,l,t,r] - sales[p,l,t,r]) for r >= 2
      - Charge on END-of-period inventory (after sales), exclude expiring items (r=1)
- Storage capacity: sum over products of (cold_usage[p] * total_inventory[p,l,t]) <= cold_capacity[l]. Storage capacities are scaled to 30% of normal values and must be enforced as binding constraints.
- Production capacities are set to zero during a mid-horizon peak window (periods 8-11), so no new units can be produced in those periods.
- The substitution graph is empty (sub_edges = []), so each product's demand can only be served by its own inventory at the location.
- Inventory at each product, location, and period remains non-negative and reflects previous stock, allowed production, sales, and waste. Unmet demand is explicitly treated as lost sales.
- There is no transshipment and lead times are zero. Labor capacity remains generous.
- The objective is to minimize total cost (aggregating purchasing, holding, waste, and lost sales) while handling simultaneous capacity, supply, and assortment stress without violating feasibility.

[DATA SCHEMA]
{
  "name": str,                          # scenario identifier
  "periods": int,                       # number of time periods
  "products": [str, ...],               # list of product IDs
  "locations": [str, ...],              # list of location IDs

  "shelf_life": {p: int},               # shelf life in periods per product
  "lead_time": {p: int},                # order lead time per product (0 = same-period arrival)

  "demand_curve": {p: [float, ...]},    # demand per product per period (0-indexed list)
  "demand_share": {l: float},           # fraction of total demand at each location

  "production_cap": {p: [float, ...]},  # max production per product per period (0-indexed list)
  "cold_capacity": {l: float},          # storage capacity per location
  "cold_usage": {p: float},             # storage units per unit of product

  "labor_cap": {l: [float, ...]},       # labor hours per location per period (0-indexed list)
  "labor_usage": {p: float},            # labor hours per unit sold
  "return_rate": {p: float},            # fraction of sales returned next period

  "costs": {
    "purchasing": {p: float},           # cost per unit ordered
    "inventory": {p: float},            # holding cost per unit per period
    "waste": {p: float},                # cost per unit expired
    "lost_sales": {p: float},           # penalty per unit of unmet demand
    "fixed_order": float,               # fixed cost per order placed
    "transshipment": float              # cost per unit transshipped
  },

  "constraints": {
    "moq": float,                       # minimum order quantity (0 = no MOQ)
    "pack_size": int,                   # order must be multiple of this (1 = no constraint)
    "budget_per_period": float|null,    # max purchasing cost per period
    "waste_limit_pct": float|null       # max waste as fraction of total demand
  },

  "network": {
    "sub_edges": [[p_from, p_to], ...], # substitution: p_from's demand can be served by p_to
    "trans_edges": [[l_from, l_to], ...]# transshipment: can ship from l_from to l_to
  }
}

[DATA ACCESS]
- The variable `data` is pre-loaded. Do NOT use file I/O.
- Lists are 0-indexed (period t in model uses index [t-1] in data arrays)

CRITICAL - Network edges require tuple conversion for Gurobi:
  sub_edges = [tuple(e) for e in data.get('network', {}).get('sub_edges', [])]
  trans_edges = [tuple(e) for e in data.get('network', {}).get('trans_edges', [])]

[OUTPUT FORMAT]
- Import: import gurobipy as gp; from gurobipy import GRB
- Set Gurobi params: m.Params.OutputFlag = 0; m.Params.Threads = 1; m.Params.Seed = 0
- Print at end:
  print(f"status: {{m.Status}}")
  if m.Status == 2:
      print(f"objective: {{m.ObjVal}}")
- Output ONLY executable Python code. No markdown, no explanations.

[TASK]
Write a GurobiPy script that models and solves this optimization problem.

[SCENARIO]
Family: F5 (Feasibility and Model Repair)
Archetype: retail_f5_ultimate_stress
Scenario ID: retail_f5_ultimate_stress_v1

[BUSINESS DESCRIPTION]
Business narrative:
This is a composite stress test that combines three stressors: tight storage capacity, a mid-horizon production failure during a peak period, and the absence of any substitution between products. Demand is local and exogenous at each location. The scenario is designed as a boss-level test where the only viable strategy is to accept significant lost sales while respecting all physical constraints.

Structure cues:
- The archetype combines the storage bottleneck of retail_f3_storage_bottleneck (30% capacity), the peak supply failure of retail_f4_peak_failure (production=0 for periods 8-11), and the no-substitution structure of retail_f2_no_substitution (sub_edges=[]).
- Shelf life: Each product has a shelf life in periods. Inventory must be tracked by REMAINING LIFE.

  VARIABLE DEFINITION: I[p,l,t,r] = inventory at START of period t with r periods remaining.
  Convention: r=1 is OLDEST (sell first FIFO), r=shelf_life[p] is FRESHEST.

  KEY EQUATIONS - implement EXACTLY as written, do NOT add or remove terms:

  (1) Fresh inflow: I[p,l,t,SL] = Q[p,t] * demand_share[l]
      - This is ONLY the inflow from production. Do NOT subtract sales here!
      - Q is total production, demand_share distributes it to locations.

  (2) Aging: I[p,l,t+1,r] = I[p,l,t,r+1] - sales[p,l,t,r+1] for r=1..SL-1
      - Inventory tomorrow with r remaining = inventory today with r+1 remaining - sales

  (3) Waste: W[p,l,t] = I[p,l,t,1] - sales[p,l,t,1]
      - Items with r=1 that aren't sold become waste

  (4) Sales availability: sales[p,l,t,r] <= I[p,l,t,r]
      - Can only sell what you have in inventory

  (5) Inventory holding cost: charged on (I[p,l,t,r] - sales[p,l,t,r]) for r >= 2
      - Charge on END-of-period inventory (after sales), exclude expiring items (r=1)
- Storage capacity: sum over products of (cold_usage[p] * total_inventory[p,l,t]) <= cold_capacity[l]. Storage capacities are scaled to 30% of normal values and must be enforced as binding constraints.
- Production capacities are set to zero during a mid-horizon peak window (periods 8-11), so no new units can be produced in those periods.
- The substitution graph is empty (sub_edges = []), so each product's demand can only be served by its own inventory at the location.
- Inventory at each product, location, and period remains non-negative and reflects previous stock, allowed production, sales, and waste. Unmet demand is explicitly treated as lost sales.
- There is no transshipment and lead times are zero. Labor capacity remains generous.
- The objective is to minimize total cost (aggregating purchasing, holding, waste, and lost sales) while handling simultaneous capacity, supply, and assortment stress without violating feasibility.

[DATA]
The following JSON contains all instance data. Parse it directly in your code.

```json
{
  "name": "retail_f5_ultimate_stress_v1",
  "description": "Standard seasonal retail scenario.",
  "periods": 20,
  "products": [
    "SKU_Basic",
    "SKU_Premium",
    "SKU_ShortLife"
  ],
  "locations": [
    "DC1",
    "DC2",
    "DC3",
    "DC4",
    "DC5"
  ],
  "shelf_life": {
    "SKU_Basic": 10,
    "SKU_Premium": 8,
    "SKU_ShortLife": 4
  },
  "lead_time": {
    "SKU_Basic": 0,
    "SKU_Premium": 0,
    "SKU_ShortLife": 0
  },
  "cold_capacity": {
    "DC1": 1372.9210620783708,
    "DC2": 1163.0419578918033,
    "DC3": 990.2992385996611,
    "DC4": 999.7078177268721,
    "DC5": 858.2639098144072
  },
  "cold_usage": {
    "SKU_Basic": 1.0,
    "SKU_Premium": 3.0,
    "SKU_ShortLife": 1.2
  },
  "production_cap": {
    "SKU_Basic": [
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      0,
      0,
      0,
      0,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800
    ],
    "SKU_Premium": [
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      0,
      0,
      0,
      0,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400
    ],
    "SKU_ShortLife": [
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      0,
      0,
      0,
      0,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500
    ]
  },
  "labor_cap": {
    "DC1": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC2": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC3": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC4": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC5": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ]
  },
  "labor_usage": {
    "SKU_Basic": 0.0,
    "SKU_Premium": 0.0,
    "SKU_ShortLife": 0.0
  },
  "return_rate": {
    "SKU_Basic": 0.0,
    "SKU_Premium": 0.0,
    "SKU_ShortLife": 0.0
  },
  "demand_curve": {
    "SKU_Basic": [
      293,
      351,
      334,
      399,
      394,
      488,
      765,
      915,
      1064,
      1119,
      1467,
      1179,
      1196,
      1011,
      616,
      549,
      486,
      418,
      318,
      335
    ],
    "SKU_Premium": [
      152,
      169,
      148,
      208,
      219,
      236,
      311,
      423,
      542,
      541,
      734,
      596,
      504,
      461,
      391,
      279,
      242,
      176,
      139,
      146
    ],
    "SKU_ShortLife": [
      103,
      123,
      139,
      138,
      151,
      193,
      283,
      320,
      478,
      460,
      582,
      554,
      459,
      390,
      297,
      235,
      179,
      135,
      123,
      121
    ]
  },
  "demand_share": {
    "DC1": 0.25,
    "DC2": 0.2,
    "DC3": 0.2,
    "DC4": 0.2,
    "DC5": 0.15
  },
  "costs": {
    "lost_sales": {
      "SKU_Basic": 50.0,
      "SKU_Premium": 80.0,
      "SKU_ShortLife": 40.0
    },
    "inventory": {
      "SKU_Basic": 1.0,
      "SKU_Premium": 1.5,
      "SKU_ShortLife": 1.0
    },
    "waste": {
      "SKU_Basic": 2.0,
      "SKU_Premium": 3.0,
      "SKU_ShortLife": 2.0
    },
    "fixed_order": 0.0,
    "transshipment": 0.5,
    "purchasing": {
      "SKU_Basic": 10.0,
      "SKU_Premium": 20.0,
      "SKU_ShortLife": 15.0
    }
  },
  "constraints": {
    "moq": 0,
    "pack_size": 1,
    "budget_per_period": null,
    "waste_limit_pct": null
  },
  "network": {
    "sub_edges": [],
    "trans_edges": []
  }
}
```

[OUTPUT FORMAT]
- Import: import gurobipy as gp; from gurobipy import GRB; import json
- Set Gurobi params: m.Params.OutputFlag = 0; m.Params.Threads = 1; m.Params.Seed = 0
- Print at end:
  print(f"status: {{m.Status}}")
  if m.Status == 2:
      print(f"objective: {{m.ObjVal}}")
- Output ONLY executable Python code. No markdown, no explanations.

[TASK]
Write a GurobiPy script that:
1. Parses the JSON data above (use json.loads on the string)
2. Models and solves the optimization problem
3. Prints status and objective value

Standard seasonal retail scenario.


[SCENARIO]
Family: F5 (Feasibility and Model Repair)
Archetype: retail_f5_ultimate_stress
Scenario ID: retail_f5_ultimate_stress_v2

[BUSINESS DESCRIPTION]
Business narrative:
This is a composite stress test that combines three stressors: tight storage capacity, a mid-horizon production failure during a peak period, and the absence of any substitution between products. Demand is local and exogenous at each location. The scenario is designed as a boss-level test where the only viable strategy is to accept significant lost sales while respecting all physical constraints.

Structure cues:
- The archetype combines the storage bottleneck of retail_f3_storage_bottleneck (30% capacity), the peak supply failure of retail_f4_peak_failure (production=0 for periods 8-11), and the no-substitution structure of retail_f2_no_substitution (sub_edges=[]).
- Shelf life: Each product has a shelf life in periods. Inventory must be tracked by REMAINING LIFE.

  VARIABLE DEFINITION: I[p,l,t,r] = inventory at START of period t with r periods remaining.
  Convention: r=1 is OLDEST (sell first FIFO), r=shelf_life[p] is FRESHEST.

  KEY EQUATIONS - implement EXACTLY as written, do NOT add or remove terms:

  (1) Fresh inflow: I[p,l,t,SL] = Q[p,t] * demand_share[l]
      - This is ONLY the inflow from production. Do NOT subtract sales here!
      - Q is total production, demand_share distributes it to locations.

  (2) Aging: I[p,l,t+1,r] = I[p,l,t,r+1] - sales[p,l,t,r+1] for r=1..SL-1
      - Inventory tomorrow with r remaining = inventory today with r+1 remaining - sales

  (3) Waste: W[p,l,t] = I[p,l,t,1] - sales[p,l,t,1]
      - Items with r=1 that aren't sold become waste

  (4) Sales availability: sales[p,l,t,r] <= I[p,l,t,r]
      - Can only sell what you have in inventory

  (5) Inventory holding cost: charged on (I[p,l,t,r] - sales[p,l,t,r]) for r >= 2
      - Charge on END-of-period inventory (after sales), exclude expiring items (r=1)
- Storage capacity: sum over products of (cold_usage[p] * total_inventory[p,l,t]) <= cold_capacity[l]. Storage capacities are scaled to 30% of normal values and must be enforced as binding constraints.
- Production capacities are set to zero during a mid-horizon peak window (periods 8-11), so no new units can be produced in those periods.
- The substitution graph is empty (sub_edges = []), so each product's demand can only be served by its own inventory at the location.
- Inventory at each product, location, and period remains non-negative and reflects previous stock, allowed production, sales, and waste. Unmet demand is explicitly treated as lost sales.
- There is no transshipment and lead times are zero. Labor capacity remains generous.
- The objective is to minimize total cost (aggregating purchasing, holding, waste, and lost sales) while handling simultaneous capacity, supply, and assortment stress without violating feasibility.

[DATA SCHEMA]
{
  "name": str,                          # scenario identifier
  "periods": int,                       # number of time periods
  "products": [str, ...],               # list of product IDs
  "locations": [str, ...],              # list of location IDs

  "shelf_life": {p: int},               # shelf life in periods per product
  "lead_time": {p: int},                # order lead time per product (0 = same-period arrival)

  "demand_curve": {p: [float, ...]},    # demand per product per period (0-indexed list)
  "demand_share": {l: float},           # fraction of total demand at each location

  "production_cap": {p: [float, ...]},  # max production per product per period (0-indexed list)
  "cold_capacity": {l: float},          # storage capacity per location
  "cold_usage": {p: float},             # storage units per unit of product

  "labor_cap": {l: [float, ...]},       # labor hours per location per period (0-indexed list)
  "labor_usage": {p: float},            # labor hours per unit sold
  "return_rate": {p: float},            # fraction of sales returned next period

  "costs": {
    "purchasing": {p: float},           # cost per unit ordered
    "inventory": {p: float},            # holding cost per unit per period
    "waste": {p: float},                # cost per unit expired
    "lost_sales": {p: float},           # penalty per unit of unmet demand
    "fixed_order": float,               # fixed cost per order placed
    "transshipment": float              # cost per unit transshipped
  },

  "constraints": {
    "moq": float,                       # minimum order quantity (0 = no MOQ)
    "pack_size": int,                   # order must be multiple of this (1 = no constraint)
    "budget_per_period": float|null,    # max purchasing cost per period
    "waste_limit_pct": float|null       # max waste as fraction of total demand
  },

  "network": {
    "sub_edges": [[p_from, p_to], ...], # substitution: p_from's demand can be served by p_to
    "trans_edges": [[l_from, l_to], ...]# transshipment: can ship from l_from to l_to
  }
}

[DATA ACCESS]
- The variable `data` is pre-loaded. Do NOT use file I/O.
- Lists are 0-indexed (period t in model uses index [t-1] in data arrays)

CRITICAL - Network edges require tuple conversion for Gurobi:
  sub_edges = [tuple(e) for e in data.get('network', {}).get('sub_edges', [])]
  trans_edges = [tuple(e) for e in data.get('network', {}).get('trans_edges', [])]

[OUTPUT FORMAT]
- Import: import gurobipy as gp; from gurobipy import GRB
- Set Gurobi params: m.Params.OutputFlag = 0; m.Params.Threads = 1; m.Params.Seed = 0
- Print at end:
  print(f"status: {{m.Status}}")
  if m.Status == 2:
      print(f"objective: {{m.ObjVal}}")
- Output ONLY executable Python code. No markdown, no explanations.

[TASK]
Write a GurobiPy script that models and solves this optimization problem.

[SCENARIO]
Family: F5 (Feasibility and Model Repair)
Archetype: retail_f5_ultimate_stress
Scenario ID: retail_f5_ultimate_stress_v2

[BUSINESS DESCRIPTION]
Business narrative:
This is a composite stress test that combines three stressors: tight storage capacity, a mid-horizon production failure during a peak period, and the absence of any substitution between products. Demand is local and exogenous at each location. The scenario is designed as a boss-level test where the only viable strategy is to accept significant lost sales while respecting all physical constraints.

Structure cues:
- The archetype combines the storage bottleneck of retail_f3_storage_bottleneck (30% capacity), the peak supply failure of retail_f4_peak_failure (production=0 for periods 8-11), and the no-substitution structure of retail_f2_no_substitution (sub_edges=[]).
- Shelf life: Each product has a shelf life in periods. Inventory must be tracked by REMAINING LIFE.

  VARIABLE DEFINITION: I[p,l,t,r] = inventory at START of period t with r periods remaining.
  Convention: r=1 is OLDEST (sell first FIFO), r=shelf_life[p] is FRESHEST.

  KEY EQUATIONS - implement EXACTLY as written, do NOT add or remove terms:

  (1) Fresh inflow: I[p,l,t,SL] = Q[p,t] * demand_share[l]
      - This is ONLY the inflow from production. Do NOT subtract sales here!
      - Q is total production, demand_share distributes it to locations.

  (2) Aging: I[p,l,t+1,r] = I[p,l,t,r+1] - sales[p,l,t,r+1] for r=1..SL-1
      - Inventory tomorrow with r remaining = inventory today with r+1 remaining - sales

  (3) Waste: W[p,l,t] = I[p,l,t,1] - sales[p,l,t,1]
      - Items with r=1 that aren't sold become waste

  (4) Sales availability: sales[p,l,t,r] <= I[p,l,t,r]
      - Can only sell what you have in inventory

  (5) Inventory holding cost: charged on (I[p,l,t,r] - sales[p,l,t,r]) for r >= 2
      - Charge on END-of-period inventory (after sales), exclude expiring items (r=1)
- Storage capacity: sum over products of (cold_usage[p] * total_inventory[p,l,t]) <= cold_capacity[l]. Storage capacities are scaled to 30% of normal values and must be enforced as binding constraints.
- Production capacities are set to zero during a mid-horizon peak window (periods 8-11), so no new units can be produced in those periods.
- The substitution graph is empty (sub_edges = []), so each product's demand can only be served by its own inventory at the location.
- Inventory at each product, location, and period remains non-negative and reflects previous stock, allowed production, sales, and waste. Unmet demand is explicitly treated as lost sales.
- There is no transshipment and lead times are zero. Labor capacity remains generous.
- The objective is to minimize total cost (aggregating purchasing, holding, waste, and lost sales) while handling simultaneous capacity, supply, and assortment stress without violating feasibility.

[DATA]
The following JSON contains all instance data. Parse it directly in your code.

```json
{
  "name": "retail_f5_ultimate_stress_v2",
  "description": "Standard seasonal retail scenario.",
  "periods": 20,
  "products": [
    "SKU_Basic",
    "SKU_Premium",
    "SKU_ShortLife"
  ],
  "locations": [
    "DC1",
    "DC2",
    "DC3",
    "DC4",
    "DC5"
  ],
  "shelf_life": {
    "SKU_Basic": 10,
    "SKU_Premium": 8,
    "SKU_ShortLife": 4
  },
  "lead_time": {
    "SKU_Basic": 0,
    "SKU_Premium": 0,
    "SKU_ShortLife": 0
  },
  "cold_capacity": {
    "DC1": 1069.9348214422798,
    "DC2": 893.6696785538822,
    "DC3": 909.1694074955392,
    "DC4": 859.3260314030408,
    "DC5": 750.0918792230719
  },
  "cold_usage": {
    "SKU_Basic": 1.0,
    "SKU_Premium": 3.0,
    "SKU_ShortLife": 1.2
  },
  "production_cap": {
    "SKU_Basic": [
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      0,
      0,
      0,
      0,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800
    ],
    "SKU_Premium": [
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      0,
      0,
      0,
      0,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400
    ],
    "SKU_ShortLife": [
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      0,
      0,
      0,
      0,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500
    ]
  },
  "labor_cap": {
    "DC1": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC2": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC3": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC4": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC5": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ]
  },
  "labor_usage": {
    "SKU_Basic": 0.0,
    "SKU_Premium": 0.0,
    "SKU_ShortLife": 0.0
  },
  "return_rate": {
    "SKU_Basic": 0.0,
    "SKU_Premium": 0.0,
    "SKU_ShortLife": 0.0
  },
  "demand_curve": {
    "SKU_Basic": [
      277,
      278,
      337,
      321,
      475,
      603,
      658,
      1026,
      1187,
      1187,
      1482,
      1398,
      1111,
      1006,
      767,
      524,
      384,
      402,
      353,
      321
    ],
    "SKU_Premium": [
      169,
      172,
      144,
      167,
      243,
      307,
      368,
      403,
      505,
      615,
      653,
      567,
      485,
      409,
      399,
      234,
      195,
      167,
      152,
      152
    ],
    "SKU_ShortLife": [
      110,
      120,
      149,
      149,
      199,
      194,
      286,
      392,
      397,
      436,
      556,
      473,
      456,
      354,
      250,
      194,
      200,
      161,
      123,
      130
    ]
  },
  "demand_share": {
    "DC1": 0.25,
    "DC2": 0.2,
    "DC3": 0.2,
    "DC4": 0.2,
    "DC5": 0.15
  },
  "costs": {
    "lost_sales": {
      "SKU_Basic": 50.0,
      "SKU_Premium": 80.0,
      "SKU_ShortLife": 40.0
    },
    "inventory": {
      "SKU_Basic": 1.0,
      "SKU_Premium": 1.5,
      "SKU_ShortLife": 1.0
    },
    "waste": {
      "SKU_Basic": 2.0,
      "SKU_Premium": 3.0,
      "SKU_ShortLife": 2.0
    },
    "fixed_order": 0.0,
    "transshipment": 0.5,
    "purchasing": {
      "SKU_Basic": 10.0,
      "SKU_Premium": 20.0,
      "SKU_ShortLife": 15.0
    }
  },
  "constraints": {
    "moq": 0,
    "pack_size": 1,
    "budget_per_period": null,
    "waste_limit_pct": null
  },
  "network": {
    "sub_edges": [],
    "trans_edges": []
  }
}
```

[OUTPUT FORMAT]
- Import: import gurobipy as gp; from gurobipy import GRB; import json
- Set Gurobi params: m.Params.OutputFlag = 0; m.Params.Threads = 1; m.Params.Seed = 0
- Print at end:
  print(f"status: {{m.Status}}")
  if m.Status == 2:
      print(f"objective: {{m.ObjVal}}")
- Output ONLY executable Python code. No markdown, no explanations.

[TASK]
Write a GurobiPy script that:
1. Parses the JSON data above (use json.loads on the string)
2. Models and solves the optimization problem
3. Prints status and objective value

Standard seasonal retail scenario.


[SCENARIO]
Family: F5 (Feasibility and Model Repair)
Archetype: retail_f5_ultimate_stress
Scenario ID: retail_f5_ultimate_stress_v3

[BUSINESS DESCRIPTION]
Business narrative:
This is a composite stress test that combines three stressors: tight storage capacity, a mid-horizon production failure during a peak period, and the absence of any substitution between products. Demand is local and exogenous at each location. The scenario is designed as a boss-level test where the only viable strategy is to accept significant lost sales while respecting all physical constraints.

Structure cues:
- The archetype combines the storage bottleneck of retail_f3_storage_bottleneck (30% capacity), the peak supply failure of retail_f4_peak_failure (production=0 for periods 8-11), and the no-substitution structure of retail_f2_no_substitution (sub_edges=[]).
- Shelf life: Each product has a shelf life in periods. Inventory must be tracked by REMAINING LIFE.

  VARIABLE DEFINITION: I[p,l,t,r] = inventory at START of period t with r periods remaining.
  Convention: r=1 is OLDEST (sell first FIFO), r=shelf_life[p] is FRESHEST.

  KEY EQUATIONS - implement EXACTLY as written, do NOT add or remove terms:

  (1) Fresh inflow: I[p,l,t,SL] = Q[p,t] * demand_share[l]
      - This is ONLY the inflow from production. Do NOT subtract sales here!
      - Q is total production, demand_share distributes it to locations.

  (2) Aging: I[p,l,t+1,r] = I[p,l,t,r+1] - sales[p,l,t,r+1] for r=1..SL-1
      - Inventory tomorrow with r remaining = inventory today with r+1 remaining - sales

  (3) Waste: W[p,l,t] = I[p,l,t,1] - sales[p,l,t,1]
      - Items with r=1 that aren't sold become waste

  (4) Sales availability: sales[p,l,t,r] <= I[p,l,t,r]
      - Can only sell what you have in inventory

  (5) Inventory holding cost: charged on (I[p,l,t,r] - sales[p,l,t,r]) for r >= 2
      - Charge on END-of-period inventory (after sales), exclude expiring items (r=1)
- Storage capacity: sum over products of (cold_usage[p] * total_inventory[p,l,t]) <= cold_capacity[l]. Storage capacities are scaled to 30% of normal values and must be enforced as binding constraints.
- Production capacities are set to zero during a mid-horizon peak window (periods 8-11), so no new units can be produced in those periods.
- The substitution graph is empty (sub_edges = []), so each product's demand can only be served by its own inventory at the location.
- Inventory at each product, location, and period remains non-negative and reflects previous stock, allowed production, sales, and waste. Unmet demand is explicitly treated as lost sales.
- There is no transshipment and lead times are zero. Labor capacity remains generous.
- The objective is to minimize total cost (aggregating purchasing, holding, waste, and lost sales) while handling simultaneous capacity, supply, and assortment stress without violating feasibility.

[DATA SCHEMA]
{
  "name": str,                          # scenario identifier
  "periods": int,                       # number of time periods
  "products": [str, ...],               # list of product IDs
  "locations": [str, ...],              # list of location IDs

  "shelf_life": {p: int},               # shelf life in periods per product
  "lead_time": {p: int},                # order lead time per product (0 = same-period arrival)

  "demand_curve": {p: [float, ...]},    # demand per product per period (0-indexed list)
  "demand_share": {l: float},           # fraction of total demand at each location

  "production_cap": {p: [float, ...]},  # max production per product per period (0-indexed list)
  "cold_capacity": {l: float},          # storage capacity per location
  "cold_usage": {p: float},             # storage units per unit of product

  "labor_cap": {l: [float, ...]},       # labor hours per location per period (0-indexed list)
  "labor_usage": {p: float},            # labor hours per unit sold
  "return_rate": {p: float},            # fraction of sales returned next period

  "costs": {
    "purchasing": {p: float},           # cost per unit ordered
    "inventory": {p: float},            # holding cost per unit per period
    "waste": {p: float},                # cost per unit expired
    "lost_sales": {p: float},           # penalty per unit of unmet demand
    "fixed_order": float,               # fixed cost per order placed
    "transshipment": float              # cost per unit transshipped
  },

  "constraints": {
    "moq": float,                       # minimum order quantity (0 = no MOQ)
    "pack_size": int,                   # order must be multiple of this (1 = no constraint)
    "budget_per_period": float|null,    # max purchasing cost per period
    "waste_limit_pct": float|null       # max waste as fraction of total demand
  },

  "network": {
    "sub_edges": [[p_from, p_to], ...], # substitution: p_from's demand can be served by p_to
    "trans_edges": [[l_from, l_to], ...]# transshipment: can ship from l_from to l_to
  }
}

[DATA ACCESS]
- The variable `data` is pre-loaded. Do NOT use file I/O.
- Lists are 0-indexed (period t in model uses index [t-1] in data arrays)

CRITICAL - Network edges require tuple conversion for Gurobi:
  sub_edges = [tuple(e) for e in data.get('network', {}).get('sub_edges', [])]
  trans_edges = [tuple(e) for e in data.get('network', {}).get('trans_edges', [])]

[OUTPUT FORMAT]
- Import: import gurobipy as gp; from gurobipy import GRB
- Set Gurobi params: m.Params.OutputFlag = 0; m.Params.Threads = 1; m.Params.Seed = 0
- Print at end:
  print(f"status: {{m.Status}}")
  if m.Status == 2:
      print(f"objective: {{m.ObjVal}}")
- Output ONLY executable Python code. No markdown, no explanations.

[TASK]
Write a GurobiPy script that models and solves this optimization problem.

[SCENARIO]
Family: F5 (Feasibility and Model Repair)
Archetype: retail_f5_ultimate_stress
Scenario ID: retail_f5_ultimate_stress_v3

[BUSINESS DESCRIPTION]
Business narrative:
This is a composite stress test that combines three stressors: tight storage capacity, a mid-horizon production failure during a peak period, and the absence of any substitution between products. Demand is local and exogenous at each location. The scenario is designed as a boss-level test where the only viable strategy is to accept significant lost sales while respecting all physical constraints.

Structure cues:
- The archetype combines the storage bottleneck of retail_f3_storage_bottleneck (30% capacity), the peak supply failure of retail_f4_peak_failure (production=0 for periods 8-11), and the no-substitution structure of retail_f2_no_substitution (sub_edges=[]).
- Shelf life: Each product has a shelf life in periods. Inventory must be tracked by REMAINING LIFE.

  VARIABLE DEFINITION: I[p,l,t,r] = inventory at START of period t with r periods remaining.
  Convention: r=1 is OLDEST (sell first FIFO), r=shelf_life[p] is FRESHEST.

  KEY EQUATIONS - implement EXACTLY as written, do NOT add or remove terms:

  (1) Fresh inflow: I[p,l,t,SL] = Q[p,t] * demand_share[l]
      - This is ONLY the inflow from production. Do NOT subtract sales here!
      - Q is total production, demand_share distributes it to locations.

  (2) Aging: I[p,l,t+1,r] = I[p,l,t,r+1] - sales[p,l,t,r+1] for r=1..SL-1
      - Inventory tomorrow with r remaining = inventory today with r+1 remaining - sales

  (3) Waste: W[p,l,t] = I[p,l,t,1] - sales[p,l,t,1]
      - Items with r=1 that aren't sold become waste

  (4) Sales availability: sales[p,l,t,r] <= I[p,l,t,r]
      - Can only sell what you have in inventory

  (5) Inventory holding cost: charged on (I[p,l,t,r] - sales[p,l,t,r]) for r >= 2
      - Charge on END-of-period inventory (after sales), exclude expiring items (r=1)
- Storage capacity: sum over products of (cold_usage[p] * total_inventory[p,l,t]) <= cold_capacity[l]. Storage capacities are scaled to 30% of normal values and must be enforced as binding constraints.
- Production capacities are set to zero during a mid-horizon peak window (periods 8-11), so no new units can be produced in those periods.
- The substitution graph is empty (sub_edges = []), so each product's demand can only be served by its own inventory at the location.
- Inventory at each product, location, and period remains non-negative and reflects previous stock, allowed production, sales, and waste. Unmet demand is explicitly treated as lost sales.
- There is no transshipment and lead times are zero. Labor capacity remains generous.
- The objective is to minimize total cost (aggregating purchasing, holding, waste, and lost sales) while handling simultaneous capacity, supply, and assortment stress without violating feasibility.

[DATA]
The following JSON contains all instance data. Parse it directly in your code.

```json
{
  "name": "retail_f5_ultimate_stress_v3",
  "description": "Standard seasonal retail scenario.",
  "periods": 20,
  "products": [
    "SKU_Basic",
    "SKU_Premium",
    "SKU_ShortLife"
  ],
  "locations": [
    "DC1",
    "DC2",
    "DC3",
    "DC4",
    "DC5"
  ],
  "shelf_life": {
    "SKU_Basic": 10,
    "SKU_Premium": 8,
    "SKU_ShortLife": 4
  },
  "lead_time": {
    "SKU_Basic": 0,
    "SKU_Premium": 0,
    "SKU_ShortLife": 0
  },
  "cold_capacity": {
    "DC1": 1061.4575176016783,
    "DC2": 1075.3730828514472,
    "DC3": 886.7196053557647,
    "DC4": 1015.8158515583032,
    "DC5": 861.2759756685144
  },
  "cold_usage": {
    "SKU_Basic": 1.0,
    "SKU_Premium": 3.0,
    "SKU_ShortLife": 1.2
  },
  "production_cap": {
    "SKU_Basic": [
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      0,
      0,
      0,
      0,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800
    ],
    "SKU_Premium": [
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      0,
      0,
      0,
      0,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400
    ],
    "SKU_ShortLife": [
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      0,
      0,
      0,
      0,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500
    ]
  },
  "labor_cap": {
    "DC1": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC2": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC3": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC4": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC5": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ]
  },
  "labor_usage": {
    "SKU_Basic": 0.0,
    "SKU_Premium": 0.0,
    "SKU_ShortLife": 0.0
  },
  "return_rate": {
    "SKU_Basic": 0.0,
    "SKU_Premium": 0.0,
    "SKU_ShortLife": 0.0
  },
  "demand_curve": {
    "SKU_Basic": [
      328,
      354,
      336,
      336,
      451,
      483,
      806,
      867,
      1108,
      1094,
      1252,
      1197,
      1184,
      987,
      623,
      529,
      412,
      392,
      360,
      300
    ],
    "SKU_Premium": [
      136,
      165,
      188,
      168,
      191,
      313,
      357,
      393,
      550,
      573,
      597,
      609,
      570,
      391,
      368,
      268,
      237,
      188,
      141,
      166
    ],
    "SKU_ShortLife": [
      123,
      124,
      135,
      139,
      167,
      202,
      257,
      308,
      430,
      430,
      448,
      510,
      504,
      321,
      312,
      231,
      160,
      125,
      128,
      107
    ]
  },
  "demand_share": {
    "DC1": 0.25,
    "DC2": 0.2,
    "DC3": 0.2,
    "DC4": 0.2,
    "DC5": 0.15
  },
  "costs": {
    "lost_sales": {
      "SKU_Basic": 50.0,
      "SKU_Premium": 80.0,
      "SKU_ShortLife": 40.0
    },
    "inventory": {
      "SKU_Basic": 1.0,
      "SKU_Premium": 1.5,
      "SKU_ShortLife": 1.0
    },
    "waste": {
      "SKU_Basic": 2.0,
      "SKU_Premium": 3.0,
      "SKU_ShortLife": 2.0
    },
    "fixed_order": 0.0,
    "transshipment": 0.5,
    "purchasing": {
      "SKU_Basic": 10.0,
      "SKU_Premium": 20.0,
      "SKU_ShortLife": 15.0
    }
  },
  "constraints": {
    "moq": 0,
    "pack_size": 1,
    "budget_per_period": null,
    "waste_limit_pct": null
  },
  "network": {
    "sub_edges": [],
    "trans_edges": []
  }
}
```

[OUTPUT FORMAT]
- Import: import gurobipy as gp; from gurobipy import GRB; import json
- Set Gurobi params: m.Params.OutputFlag = 0; m.Params.Threads = 1; m.Params.Seed = 0
- Print at end:
  print(f"status: {{m.Status}}")
  if m.Status == 2:
      print(f"objective: {{m.ObjVal}}")
- Output ONLY executable Python code. No markdown, no explanations.

[TASK]
Write a GurobiPy script that:
1. Parses the JSON data above (use json.loads on the string)
2. Models and solves the optimization problem
3. Prints status and objective value

Standard seasonal retail scenario.


[SCENARIO]
Family: F5 (Feasibility and Model Repair)
Archetype: retail_f5_ultimate_stress
Scenario ID: retail_f5_ultimate_stress_v4

[BUSINESS DESCRIPTION]
Business narrative:
This is a composite stress test that combines three stressors: tight storage capacity, a mid-horizon production failure during a peak period, and the absence of any substitution between products. Demand is local and exogenous at each location. The scenario is designed as a boss-level test where the only viable strategy is to accept significant lost sales while respecting all physical constraints.

Structure cues:
- The archetype combines the storage bottleneck of retail_f3_storage_bottleneck (30% capacity), the peak supply failure of retail_f4_peak_failure (production=0 for periods 8-11), and the no-substitution structure of retail_f2_no_substitution (sub_edges=[]).
- Shelf life: Each product has a shelf life in periods. Inventory must be tracked by REMAINING LIFE.

  VARIABLE DEFINITION: I[p,l,t,r] = inventory at START of period t with r periods remaining.
  Convention: r=1 is OLDEST (sell first FIFO), r=shelf_life[p] is FRESHEST.

  KEY EQUATIONS - implement EXACTLY as written, do NOT add or remove terms:

  (1) Fresh inflow: I[p,l,t,SL] = Q[p,t] * demand_share[l]
      - This is ONLY the inflow from production. Do NOT subtract sales here!
      - Q is total production, demand_share distributes it to locations.

  (2) Aging: I[p,l,t+1,r] = I[p,l,t,r+1] - sales[p,l,t,r+1] for r=1..SL-1
      - Inventory tomorrow with r remaining = inventory today with r+1 remaining - sales

  (3) Waste: W[p,l,t] = I[p,l,t,1] - sales[p,l,t,1]
      - Items with r=1 that aren't sold become waste

  (4) Sales availability: sales[p,l,t,r] <= I[p,l,t,r]
      - Can only sell what you have in inventory

  (5) Inventory holding cost: charged on (I[p,l,t,r] - sales[p,l,t,r]) for r >= 2
      - Charge on END-of-period inventory (after sales), exclude expiring items (r=1)
- Storage capacity: sum over products of (cold_usage[p] * total_inventory[p,l,t]) <= cold_capacity[l]. Storage capacities are scaled to 30% of normal values and must be enforced as binding constraints.
- Production capacities are set to zero during a mid-horizon peak window (periods 8-11), so no new units can be produced in those periods.
- The substitution graph is empty (sub_edges = []), so each product's demand can only be served by its own inventory at the location.
- Inventory at each product, location, and period remains non-negative and reflects previous stock, allowed production, sales, and waste. Unmet demand is explicitly treated as lost sales.
- There is no transshipment and lead times are zero. Labor capacity remains generous.
- The objective is to minimize total cost (aggregating purchasing, holding, waste, and lost sales) while handling simultaneous capacity, supply, and assortment stress without violating feasibility.

[DATA SCHEMA]
{
  "name": str,                          # scenario identifier
  "periods": int,                       # number of time periods
  "products": [str, ...],               # list of product IDs
  "locations": [str, ...],              # list of location IDs

  "shelf_life": {p: int},               # shelf life in periods per product
  "lead_time": {p: int},                # order lead time per product (0 = same-period arrival)

  "demand_curve": {p: [float, ...]},    # demand per product per period (0-indexed list)
  "demand_share": {l: float},           # fraction of total demand at each location

  "production_cap": {p: [float, ...]},  # max production per product per period (0-indexed list)
  "cold_capacity": {l: float},          # storage capacity per location
  "cold_usage": {p: float},             # storage units per unit of product

  "labor_cap": {l: [float, ...]},       # labor hours per location per period (0-indexed list)
  "labor_usage": {p: float},            # labor hours per unit sold
  "return_rate": {p: float},            # fraction of sales returned next period

  "costs": {
    "purchasing": {p: float},           # cost per unit ordered
    "inventory": {p: float},            # holding cost per unit per period
    "waste": {p: float},                # cost per unit expired
    "lost_sales": {p: float},           # penalty per unit of unmet demand
    "fixed_order": float,               # fixed cost per order placed
    "transshipment": float              # cost per unit transshipped
  },

  "constraints": {
    "moq": float,                       # minimum order quantity (0 = no MOQ)
    "pack_size": int,                   # order must be multiple of this (1 = no constraint)
    "budget_per_period": float|null,    # max purchasing cost per period
    "waste_limit_pct": float|null       # max waste as fraction of total demand
  },

  "network": {
    "sub_edges": [[p_from, p_to], ...], # substitution: p_from's demand can be served by p_to
    "trans_edges": [[l_from, l_to], ...]# transshipment: can ship from l_from to l_to
  }
}

[DATA ACCESS]
- The variable `data` is pre-loaded. Do NOT use file I/O.
- Lists are 0-indexed (period t in model uses index [t-1] in data arrays)

CRITICAL - Network edges require tuple conversion for Gurobi:
  sub_edges = [tuple(e) for e in data.get('network', {}).get('sub_edges', [])]
  trans_edges = [tuple(e) for e in data.get('network', {}).get('trans_edges', [])]

[OUTPUT FORMAT]
- Import: import gurobipy as gp; from gurobipy import GRB
- Set Gurobi params: m.Params.OutputFlag = 0; m.Params.Threads = 1; m.Params.Seed = 0
- Print at end:
  print(f"status: {{m.Status}}")
  if m.Status == 2:
      print(f"objective: {{m.ObjVal}}")
- Output ONLY executable Python code. No markdown, no explanations.

[TASK]
Write a GurobiPy script that models and solves this optimization problem.

[SCENARIO]
Family: F5 (Feasibility and Model Repair)
Archetype: retail_f5_ultimate_stress
Scenario ID: retail_f5_ultimate_stress_v4

[BUSINESS DESCRIPTION]
Business narrative:
This is a composite stress test that combines three stressors: tight storage capacity, a mid-horizon production failure during a peak period, and the absence of any substitution between products. Demand is local and exogenous at each location. The scenario is designed as a boss-level test where the only viable strategy is to accept significant lost sales while respecting all physical constraints.

Structure cues:
- The archetype combines the storage bottleneck of retail_f3_storage_bottleneck (30% capacity), the peak supply failure of retail_f4_peak_failure (production=0 for periods 8-11), and the no-substitution structure of retail_f2_no_substitution (sub_edges=[]).
- Shelf life: Each product has a shelf life in periods. Inventory must be tracked by REMAINING LIFE.

  VARIABLE DEFINITION: I[p,l,t,r] = inventory at START of period t with r periods remaining.
  Convention: r=1 is OLDEST (sell first FIFO), r=shelf_life[p] is FRESHEST.

  KEY EQUATIONS - implement EXACTLY as written, do NOT add or remove terms:

  (1) Fresh inflow: I[p,l,t,SL] = Q[p,t] * demand_share[l]
      - This is ONLY the inflow from production. Do NOT subtract sales here!
      - Q is total production, demand_share distributes it to locations.

  (2) Aging: I[p,l,t+1,r] = I[p,l,t,r+1] - sales[p,l,t,r+1] for r=1..SL-1
      - Inventory tomorrow with r remaining = inventory today with r+1 remaining - sales

  (3) Waste: W[p,l,t] = I[p,l,t,1] - sales[p,l,t,1]
      - Items with r=1 that aren't sold become waste

  (4) Sales availability: sales[p,l,t,r] <= I[p,l,t,r]
      - Can only sell what you have in inventory

  (5) Inventory holding cost: charged on (I[p,l,t,r] - sales[p,l,t,r]) for r >= 2
      - Charge on END-of-period inventory (after sales), exclude expiring items (r=1)
- Storage capacity: sum over products of (cold_usage[p] * total_inventory[p,l,t]) <= cold_capacity[l]. Storage capacities are scaled to 30% of normal values and must be enforced as binding constraints.
- Production capacities are set to zero during a mid-horizon peak window (periods 8-11), so no new units can be produced in those periods.
- The substitution graph is empty (sub_edges = []), so each product's demand can only be served by its own inventory at the location.
- Inventory at each product, location, and period remains non-negative and reflects previous stock, allowed production, sales, and waste. Unmet demand is explicitly treated as lost sales.
- There is no transshipment and lead times are zero. Labor capacity remains generous.
- The objective is to minimize total cost (aggregating purchasing, holding, waste, and lost sales) while handling simultaneous capacity, supply, and assortment stress without violating feasibility.

[DATA]
The following JSON contains all instance data. Parse it directly in your code.

```json
{
  "name": "retail_f5_ultimate_stress_v4",
  "description": "Standard seasonal retail scenario.",
  "periods": 20,
  "products": [
    "SKU_Basic",
    "SKU_Premium",
    "SKU_ShortLife"
  ],
  "locations": [
    "DC1",
    "DC2",
    "DC3",
    "DC4",
    "DC5"
  ],
  "shelf_life": {
    "SKU_Basic": 10,
    "SKU_Premium": 8,
    "SKU_ShortLife": 4
  },
  "lead_time": {
    "SKU_Basic": 0,
    "SKU_Premium": 0,
    "SKU_ShortLife": 0
  },
  "cold_capacity": {
    "DC1": 1245.7612591232007,
    "DC2": 934.2263218142887,
    "DC3": 776.9531098553176,
    "DC4": 906.3082692867368,
    "DC5": 653.3097517197237
  },
  "cold_usage": {
    "SKU_Basic": 1.0,
    "SKU_Premium": 3.0,
    "SKU_ShortLife": 1.2
  },
  "production_cap": {
    "SKU_Basic": [
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      0,
      0,
      0,
      0,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800
    ],
    "SKU_Premium": [
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      0,
      0,
      0,
      0,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400
    ],
    "SKU_ShortLife": [
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      0,
      0,
      0,
      0,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500
    ]
  },
  "labor_cap": {
    "DC1": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC2": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC3": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC4": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC5": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ]
  },
  "labor_usage": {
    "SKU_Basic": 0.0,
    "SKU_Premium": 0.0,
    "SKU_ShortLife": 0.0
  },
  "return_rate": {
    "SKU_Basic": 0.0,
    "SKU_Premium": 0.0,
    "SKU_ShortLife": 0.0
  },
  "demand_curve": {
    "SKU_Basic": [
      265,
      277,
      294,
      320,
      425,
      550,
      806,
      951,
      1211,
      1327,
      1333,
      1392,
      1218,
      982,
      744,
      530,
      473,
      318,
      322,
      286
    ],
    "SKU_Premium": [
      149,
      139,
      180,
      205,
      226,
      308,
      333,
      456,
      489,
      659,
      721,
      640,
      555,
      440,
      325,
      260,
      225,
      197,
      163,
      167
    ],
    "SKU_ShortLife": [
      114,
      119,
      114,
      139,
      179,
      210,
      266,
      330,
      441,
      566,
      525,
      494,
      418,
      311,
      273,
      224,
      151,
      167,
      124,
      106
    ]
  },
  "demand_share": {
    "DC1": 0.25,
    "DC2": 0.2,
    "DC3": 0.2,
    "DC4": 0.2,
    "DC5": 0.15
  },
  "costs": {
    "lost_sales": {
      "SKU_Basic": 50.0,
      "SKU_Premium": 80.0,
      "SKU_ShortLife": 40.0
    },
    "inventory": {
      "SKU_Basic": 1.0,
      "SKU_Premium": 1.5,
      "SKU_ShortLife": 1.0
    },
    "waste": {
      "SKU_Basic": 2.0,
      "SKU_Premium": 3.0,
      "SKU_ShortLife": 2.0
    },
    "fixed_order": 0.0,
    "transshipment": 0.5,
    "purchasing": {
      "SKU_Basic": 10.0,
      "SKU_Premium": 20.0,
      "SKU_ShortLife": 15.0
    }
  },
  "constraints": {
    "moq": 0,
    "pack_size": 1,
    "budget_per_period": null,
    "waste_limit_pct": null
  },
  "network": {
    "sub_edges": [],
    "trans_edges": []
  }
}
```

[OUTPUT FORMAT]
- Import: import gurobipy as gp; from gurobipy import GRB; import json
- Set Gurobi params: m.Params.OutputFlag = 0; m.Params.Threads = 1; m.Params.Seed = 0
- Print at end:
  print(f"status: {{m.Status}}")
  if m.Status == 2:
      print(f"objective: {{m.ObjVal}}")
- Output ONLY executable Python code. No markdown, no explanations.

[TASK]
Write a GurobiPy script that:
1. Parses the JSON data above (use json.loads on the string)
2. Models and solves the optimization problem
3. Prints status and objective value

Standard seasonal retail scenario.


[SCENARIO]
Family: F6 (Logistics and Integer Procurement)
Archetype: retail_f6_fixed_order_cost
Scenario ID: retail_f6_fixed_order_cost_v0

[BUSINESS DESCRIPTION]
Business narrative:
Placing an order in a period incurs a fixed cost regardless of how large the order is. This encourages batching orders into fewer, larger replenishments instead of many small ones. The retailer still faces local demand, storage limits, and lost sales for unmet demand, but now each decision to place any positive order carries an additional setup-like cost.

Structure cues:
- The structure is single-echelon with ordering decisions, storage capacity, and lost sales.
- Shelf life: Each product has a shelf life in periods. Inventory must be tracked by REMAINING LIFE.

  VARIABLE DEFINITION: I[p,l,t,r] = inventory at START of period t with r periods remaining.
  Convention: r=1 is OLDEST (sell first FIFO), r=shelf_life[p] is FRESHEST.

  KEY EQUATIONS - implement EXACTLY as written, do NOT add or remove terms:

  (1) Fresh inflow: I[p,l,t,SL] = Q[p,t] * demand_share[l]
      - This is ONLY the inflow from production. Do NOT subtract sales here!
      - Q is total production, demand_share distributes it to locations.

  (2) Aging: I[p,l,t+1,r] = I[p,l,t,r+1] - sales[p,l,t,r+1] for r=1..SL-1
      - Inventory tomorrow with r remaining = inventory today with r+1 remaining - sales

  (3) Waste: W[p,l,t] = I[p,l,t,1] - sales[p,l,t,1]
      - Items with r=1 that aren't sold become waste

  (4) Sales availability: sales[p,l,t,r] <= I[p,l,t,r]
      - Can only sell what you have in inventory

  (5) Inventory holding cost: charged on (I[p,l,t,r] - sales[p,l,t,r]) for r >= 2
      - Charge on END-of-period inventory (after sales), exclude expiring items (r=1)
- Storage capacity: sum over products of (cold_usage[p] * total_inventory[p,l,t]) <= cold_capacity[l]. These limits must be respected.
- The JSON includes fixed_order=5000 in the "costs" object. Whenever a strictly positive order is placed for a product at a location in a period, this fixed cost must be charged once for that product-location-period in addition to variable purchasing costs.
- The model must ensure that the fixed cost is applied if and only if the total order quantity for a product at a location in a period is positive; it should not be possible to evade the fixed cost with arbitrarily small orders.
- Inventory balance under zero lead time is standard: on-hand stock is updated from previous stock and current orders, minus sales and waste, and cannot drop below zero.
- There is no transshipment, lead times are zero, and substitution behavior is unchanged.
- The objective is to minimize total cost including purchasing, fixed ordering, holding, waste, and lost sales costs.

[DATA SCHEMA]
{
  "name": str,                          # scenario identifier
  "periods": int,                       # number of time periods
  "products": [str, ...],               # list of product IDs
  "locations": [str, ...],              # list of location IDs

  "shelf_life": {p: int},               # shelf life in periods per product
  "lead_time": {p: int},                # order lead time per product (0 = same-period arrival)

  "demand_curve": {p: [float, ...]},    # demand per product per period (0-indexed list)
  "demand_share": {l: float},           # fraction of total demand at each location

  "production_cap": {p: [float, ...]},  # max production per product per period (0-indexed list)
  "cold_capacity": {l: float},          # storage capacity per location
  "cold_usage": {p: float},             # storage units per unit of product

  "labor_cap": {l: [float, ...]},       # labor hours per location per period (0-indexed list)
  "labor_usage": {p: float},            # labor hours per unit sold
  "return_rate": {p: float},            # fraction of sales returned next period

  "costs": {
    "purchasing": {p: float},           # cost per unit ordered
    "inventory": {p: float},            # holding cost per unit per period
    "waste": {p: float},                # cost per unit expired
    "lost_sales": {p: float},           # penalty per unit of unmet demand
    "fixed_order": float,               # fixed cost per order placed
    "transshipment": float              # cost per unit transshipped
  },

  "constraints": {
    "moq": float,                       # minimum order quantity (0 = no MOQ)
    "pack_size": int,                   # order must be multiple of this (1 = no constraint)
    "budget_per_period": float|null,    # max purchasing cost per period
    "waste_limit_pct": float|null       # max waste as fraction of total demand
  },

  "network": {
    "sub_edges": [[p_from, p_to], ...], # substitution: p_from's demand can be served by p_to
    "trans_edges": [[l_from, l_to], ...]# transshipment: can ship from l_from to l_to
  }
}

[DATA ACCESS]
- The variable `data` is pre-loaded. Do NOT use file I/O.
- Lists are 0-indexed (period t in model uses index [t-1] in data arrays)

CRITICAL - Network edges require tuple conversion for Gurobi:
  sub_edges = [tuple(e) for e in data.get('network', {}).get('sub_edges', [])]
  trans_edges = [tuple(e) for e in data.get('network', {}).get('trans_edges', [])]

[OUTPUT FORMAT]
- Import: import gurobipy as gp; from gurobipy import GRB
- Set Gurobi params: m.Params.OutputFlag = 0; m.Params.Threads = 1; m.Params.Seed = 0
- Print at end:
  print(f"status: {{m.Status}}")
  if m.Status == 2:
      print(f"objective: {{m.ObjVal}}")
- Output ONLY executable Python code. No markdown, no explanations.

[TASK]
Write a GurobiPy script that models and solves this optimization problem.

[SCENARIO]
Family: F6 (Logistics and Integer Procurement)
Archetype: retail_f6_fixed_order_cost
Scenario ID: retail_f6_fixed_order_cost_v0

[BUSINESS DESCRIPTION]
Business narrative:
Placing an order in a period incurs a fixed cost regardless of how large the order is. This encourages batching orders into fewer, larger replenishments instead of many small ones. The retailer still faces local demand, storage limits, and lost sales for unmet demand, but now each decision to place any positive order carries an additional setup-like cost.

Structure cues:
- The structure is single-echelon with ordering decisions, storage capacity, and lost sales.
- Shelf life: Each product has a shelf life in periods. Inventory must be tracked by REMAINING LIFE.

  VARIABLE DEFINITION: I[p,l,t,r] = inventory at START of period t with r periods remaining.
  Convention: r=1 is OLDEST (sell first FIFO), r=shelf_life[p] is FRESHEST.

  KEY EQUATIONS - implement EXACTLY as written, do NOT add or remove terms:

  (1) Fresh inflow: I[p,l,t,SL] = Q[p,t] * demand_share[l]
      - This is ONLY the inflow from production. Do NOT subtract sales here!
      - Q is total production, demand_share distributes it to locations.

  (2) Aging: I[p,l,t+1,r] = I[p,l,t,r+1] - sales[p,l,t,r+1] for r=1..SL-1
      - Inventory tomorrow with r remaining = inventory today with r+1 remaining - sales

  (3) Waste: W[p,l,t] = I[p,l,t,1] - sales[p,l,t,1]
      - Items with r=1 that aren't sold become waste

  (4) Sales availability: sales[p,l,t,r] <= I[p,l,t,r]
      - Can only sell what you have in inventory

  (5) Inventory holding cost: charged on (I[p,l,t,r] - sales[p,l,t,r]) for r >= 2
      - Charge on END-of-period inventory (after sales), exclude expiring items (r=1)
- Storage capacity: sum over products of (cold_usage[p] * total_inventory[p,l,t]) <= cold_capacity[l]. These limits must be respected.
- The JSON includes fixed_order=5000 in the "costs" object. Whenever a strictly positive order is placed for a product at a location in a period, this fixed cost must be charged once for that product-location-period in addition to variable purchasing costs.
- The model must ensure that the fixed cost is applied if and only if the total order quantity for a product at a location in a period is positive; it should not be possible to evade the fixed cost with arbitrarily small orders.
- Inventory balance under zero lead time is standard: on-hand stock is updated from previous stock and current orders, minus sales and waste, and cannot drop below zero.
- There is no transshipment, lead times are zero, and substitution behavior is unchanged.
- The objective is to minimize total cost including purchasing, fixed ordering, holding, waste, and lost sales costs.

[DATA]
The following JSON contains all instance data. Parse it directly in your code.

```json
{
  "name": "retail_f6_fixed_order_cost_v0",
  "description": "Standard seasonal retail scenario.",
  "periods": 20,
  "products": [
    "SKU_Basic",
    "SKU_Premium",
    "SKU_ShortLife"
  ],
  "locations": [
    "DC1",
    "DC2",
    "DC3",
    "DC4",
    "DC5"
  ],
  "shelf_life": {
    "SKU_Basic": 10,
    "SKU_Premium": 8,
    "SKU_ShortLife": 4
  },
  "lead_time": {
    "SKU_Basic": 0,
    "SKU_Premium": 0,
    "SKU_ShortLife": 0
  },
  "cold_capacity": {
    "DC1": 4000,
    "DC2": 3500,
    "DC3": 3000,
    "DC4": 3000,
    "DC5": 2500
  },
  "cold_usage": {
    "SKU_Basic": 1.0,
    "SKU_Premium": 3.0,
    "SKU_ShortLife": 1.2
  },
  "production_cap": {
    "SKU_Basic": [
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800
    ],
    "SKU_Premium": [
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400
    ],
    "SKU_ShortLife": [
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500
    ]
  },
  "labor_cap": {
    "DC1": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC2": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC3": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC4": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC5": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ]
  },
  "labor_usage": {
    "SKU_Basic": 0.0,
    "SKU_Premium": 0.0,
    "SKU_ShortLife": 0.0
  },
  "return_rate": {
    "SKU_Basic": 0.0,
    "SKU_Premium": 0.0,
    "SKU_ShortLife": 0.0
  },
  "demand_curve": {
    "SKU_Basic": [
      303,
      311,
      328,
      365,
      435,
      549,
      711,
      906,
      1100,
      1245,
      1300,
      1245,
      1100,
      906,
      711,
      549,
      435,
      365,
      328,
      311
    ],
    "SKU_Premium": [
      151,
      155,
      164,
      182,
      217,
      274,
      355,
      453,
      550,
      622,
      650,
      622,
      550,
      453,
      355,
      274,
      217,
      182,
      164,
      155
    ],
    "SKU_ShortLife": [
      121,
      124,
      131,
      146,
      174,
      219,
      284,
      362,
      440,
      498,
      520,
      498,
      440,
      362,
      284,
      219,
      174,
      146,
      131,
      124
    ]
  },
  "demand_share": {
    "DC1": 0.25,
    "DC2": 0.2,
    "DC3": 0.2,
    "DC4": 0.2,
    "DC5": 0.15
  },
  "costs": {
    "lost_sales": {
      "SKU_Basic": 50.0,
      "SKU_Premium": 80.0,
      "SKU_ShortLife": 40.0
    },
    "inventory": {
      "SKU_Basic": 1.0,
      "SKU_Premium": 1.5,
      "SKU_ShortLife": 1.0
    },
    "waste": {
      "SKU_Basic": 2.0,
      "SKU_Premium": 3.0,
      "SKU_ShortLife": 2.0
    },
    "fixed_order": 5000.0,
    "transshipment": 0.5,
    "purchasing": {
      "SKU_Basic": 10.0,
      "SKU_Premium": 20.0,
      "SKU_ShortLife": 15.0
    }
  },
  "constraints": {
    "moq": 0,
    "pack_size": 1,
    "budget_per_period": null,
    "waste_limit_pct": null
  },
  "network": {
    "sub_edges": [
      [
        "SKU_Basic",
        "SKU_Premium"
      ]
    ],
    "trans_edges": []
  }
}
```

[OUTPUT FORMAT]
- Import: import gurobipy as gp; from gurobipy import GRB; import json
- Set Gurobi params: m.Params.OutputFlag = 0; m.Params.Threads = 1; m.Params.Seed = 0
- Print at end:
  print(f"status: {{m.Status}}")
  if m.Status == 2:
      print(f"objective: {{m.ObjVal}}")
- Output ONLY executable Python code. No markdown, no explanations.

[TASK]
Write a GurobiPy script that:
1. Parses the JSON data above (use json.loads on the string)
2. Models and solves the optimization problem
3. Prints status and objective value

Standard seasonal retail scenario.


[SCENARIO]
Family: F6 (Logistics and Integer Procurement)
Archetype: retail_f6_fixed_order_cost
Scenario ID: retail_f6_fixed_order_cost_v1

[BUSINESS DESCRIPTION]
Business narrative:
Placing an order in a period incurs a fixed cost regardless of how large the order is. This encourages batching orders into fewer, larger replenishments instead of many small ones. The retailer still faces local demand, storage limits, and lost sales for unmet demand, but now each decision to place any positive order carries an additional setup-like cost.

Structure cues:
- The structure is single-echelon with ordering decisions, storage capacity, and lost sales.
- Shelf life: Each product has a shelf life in periods. Inventory must be tracked by REMAINING LIFE.

  VARIABLE DEFINITION: I[p,l,t,r] = inventory at START of period t with r periods remaining.
  Convention: r=1 is OLDEST (sell first FIFO), r=shelf_life[p] is FRESHEST.

  KEY EQUATIONS - implement EXACTLY as written, do NOT add or remove terms:

  (1) Fresh inflow: I[p,l,t,SL] = Q[p,t] * demand_share[l]
      - This is ONLY the inflow from production. Do NOT subtract sales here!
      - Q is total production, demand_share distributes it to locations.

  (2) Aging: I[p,l,t+1,r] = I[p,l,t,r+1] - sales[p,l,t,r+1] for r=1..SL-1
      - Inventory tomorrow with r remaining = inventory today with r+1 remaining - sales

  (3) Waste: W[p,l,t] = I[p,l,t,1] - sales[p,l,t,1]
      - Items with r=1 that aren't sold become waste

  (4) Sales availability: sales[p,l,t,r] <= I[p,l,t,r]
      - Can only sell what you have in inventory

  (5) Inventory holding cost: charged on (I[p,l,t,r] - sales[p,l,t,r]) for r >= 2
      - Charge on END-of-period inventory (after sales), exclude expiring items (r=1)
- Storage capacity: sum over products of (cold_usage[p] * total_inventory[p,l,t]) <= cold_capacity[l]. These limits must be respected.
- The JSON includes fixed_order=5000 in the "costs" object. Whenever a strictly positive order is placed for a product at a location in a period, this fixed cost must be charged once for that product-location-period in addition to variable purchasing costs.
- The model must ensure that the fixed cost is applied if and only if the total order quantity for a product at a location in a period is positive; it should not be possible to evade the fixed cost with arbitrarily small orders.
- Inventory balance under zero lead time is standard: on-hand stock is updated from previous stock and current orders, minus sales and waste, and cannot drop below zero.
- There is no transshipment, lead times are zero, and substitution behavior is unchanged.
- The objective is to minimize total cost including purchasing, fixed ordering, holding, waste, and lost sales costs.

[DATA SCHEMA]
{
  "name": str,                          # scenario identifier
  "periods": int,                       # number of time periods
  "products": [str, ...],               # list of product IDs
  "locations": [str, ...],              # list of location IDs

  "shelf_life": {p: int},               # shelf life in periods per product
  "lead_time": {p: int},                # order lead time per product (0 = same-period arrival)

  "demand_curve": {p: [float, ...]},    # demand per product per period (0-indexed list)
  "demand_share": {l: float},           # fraction of total demand at each location

  "production_cap": {p: [float, ...]},  # max production per product per period (0-indexed list)
  "cold_capacity": {l: float},          # storage capacity per location
  "cold_usage": {p: float},             # storage units per unit of product

  "labor_cap": {l: [float, ...]},       # labor hours per location per period (0-indexed list)
  "labor_usage": {p: float},            # labor hours per unit sold
  "return_rate": {p: float},            # fraction of sales returned next period

  "costs": {
    "purchasing": {p: float},           # cost per unit ordered
    "inventory": {p: float},            # holding cost per unit per period
    "waste": {p: float},                # cost per unit expired
    "lost_sales": {p: float},           # penalty per unit of unmet demand
    "fixed_order": float,               # fixed cost per order placed
    "transshipment": float              # cost per unit transshipped
  },

  "constraints": {
    "moq": float,                       # minimum order quantity (0 = no MOQ)
    "pack_size": int,                   # order must be multiple of this (1 = no constraint)
    "budget_per_period": float|null,    # max purchasing cost per period
    "waste_limit_pct": float|null       # max waste as fraction of total demand
  },

  "network": {
    "sub_edges": [[p_from, p_to], ...], # substitution: p_from's demand can be served by p_to
    "trans_edges": [[l_from, l_to], ...]# transshipment: can ship from l_from to l_to
  }
}

[DATA ACCESS]
- The variable `data` is pre-loaded. Do NOT use file I/O.
- Lists are 0-indexed (period t in model uses index [t-1] in data arrays)

CRITICAL - Network edges require tuple conversion for Gurobi:
  sub_edges = [tuple(e) for e in data.get('network', {}).get('sub_edges', [])]
  trans_edges = [tuple(e) for e in data.get('network', {}).get('trans_edges', [])]

[OUTPUT FORMAT]
- Import: import gurobipy as gp; from gurobipy import GRB
- Set Gurobi params: m.Params.OutputFlag = 0; m.Params.Threads = 1; m.Params.Seed = 0
- Print at end:
  print(f"status: {{m.Status}}")
  if m.Status == 2:
      print(f"objective: {{m.ObjVal}}")
- Output ONLY executable Python code. No markdown, no explanations.

[TASK]
Write a GurobiPy script that models and solves this optimization problem.

[SCENARIO]
Family: F6 (Logistics and Integer Procurement)
Archetype: retail_f6_fixed_order_cost
Scenario ID: retail_f6_fixed_order_cost_v1

[BUSINESS DESCRIPTION]
Business narrative:
Placing an order in a period incurs a fixed cost regardless of how large the order is. This encourages batching orders into fewer, larger replenishments instead of many small ones. The retailer still faces local demand, storage limits, and lost sales for unmet demand, but now each decision to place any positive order carries an additional setup-like cost.

Structure cues:
- The structure is single-echelon with ordering decisions, storage capacity, and lost sales.
- Shelf life: Each product has a shelf life in periods. Inventory must be tracked by REMAINING LIFE.

  VARIABLE DEFINITION: I[p,l,t,r] = inventory at START of period t with r periods remaining.
  Convention: r=1 is OLDEST (sell first FIFO), r=shelf_life[p] is FRESHEST.

  KEY EQUATIONS - implement EXACTLY as written, do NOT add or remove terms:

  (1) Fresh inflow: I[p,l,t,SL] = Q[p,t] * demand_share[l]
      - This is ONLY the inflow from production. Do NOT subtract sales here!
      - Q is total production, demand_share distributes it to locations.

  (2) Aging: I[p,l,t+1,r] = I[p,l,t,r+1] - sales[p,l,t,r+1] for r=1..SL-1
      - Inventory tomorrow with r remaining = inventory today with r+1 remaining - sales

  (3) Waste: W[p,l,t] = I[p,l,t,1] - sales[p,l,t,1]
      - Items with r=1 that aren't sold become waste

  (4) Sales availability: sales[p,l,t,r] <= I[p,l,t,r]
      - Can only sell what you have in inventory

  (5) Inventory holding cost: charged on (I[p,l,t,r] - sales[p,l,t,r]) for r >= 2
      - Charge on END-of-period inventory (after sales), exclude expiring items (r=1)
- Storage capacity: sum over products of (cold_usage[p] * total_inventory[p,l,t]) <= cold_capacity[l]. These limits must be respected.
- The JSON includes fixed_order=5000 in the "costs" object. Whenever a strictly positive order is placed for a product at a location in a period, this fixed cost must be charged once for that product-location-period in addition to variable purchasing costs.
- The model must ensure that the fixed cost is applied if and only if the total order quantity for a product at a location in a period is positive; it should not be possible to evade the fixed cost with arbitrarily small orders.
- Inventory balance under zero lead time is standard: on-hand stock is updated from previous stock and current orders, minus sales and waste, and cannot drop below zero.
- There is no transshipment, lead times are zero, and substitution behavior is unchanged.
- The objective is to minimize total cost including purchasing, fixed ordering, holding, waste, and lost sales costs.

[DATA]
The following JSON contains all instance data. Parse it directly in your code.

```json
{
  "name": "retail_f6_fixed_order_cost_v1",
  "description": "Standard seasonal retail scenario.",
  "periods": 20,
  "products": [
    "SKU_Basic",
    "SKU_Premium",
    "SKU_ShortLife"
  ],
  "locations": [
    "DC1",
    "DC2",
    "DC3",
    "DC4",
    "DC5"
  ],
  "shelf_life": {
    "SKU_Basic": 10,
    "SKU_Premium": 8,
    "SKU_ShortLife": 4
  },
  "lead_time": {
    "SKU_Basic": 0,
    "SKU_Premium": 0,
    "SKU_ShortLife": 0
  },
  "cold_capacity": {
    "DC1": 3698.4076989093614,
    "DC2": 3221.606370420646,
    "DC3": 2914.9829417453325,
    "DC4": 3196.1474214548466,
    "DC5": 2130.6427526847997
  },
  "cold_usage": {
    "SKU_Basic": 1.0,
    "SKU_Premium": 3.0,
    "SKU_ShortLife": 1.2
  },
  "production_cap": {
    "SKU_Basic": [
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800,
      800
    ],
    "SKU_Premium": [
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400,
      400
    ],
    "SKU_ShortLife": [
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500,
      500
    ]
  },
  "labor_cap": {
    "DC1": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC2": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC3": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC4": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ],
    "DC5": [
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0,
      99999.0
    ]
  },
  "labor_usage": {
    "SKU_Basic": 0.0,
    "SKU_Premium": 0.0,
    "SKU_ShortLife": 0.0
  },
  "return_rate": {
    "SKU_Basic": 0.0,
    "SKU_Premium": 0.0,
    "SKU_ShortLife": 0.0
  },
  "demand_curve": {
    "SKU_Basic": [
      264,
      356,
      331,
      341,
      488,
      573,
      732,
      932,
      1039,
      1419,
      1412,
      1071,
      1250,
      785,
      773,
      545,
      451,
      363,
      329,
      336
    ],
    "SKU_Premium": [
      149,
      148,
      172,
      178,
      212,
      252,
      314,
      509,
      474,
      654,
      585,
      662,
      629,
      489,
      361,
      286,
      235,
      165,
      142,
      146
    ],
    "SKU_ShortLife": [
      123,
      137,
      114,
      139,
      173,
      246,
      264,
      366,
      501,
      556,
      475,
      529,
      445,
      333,
      283,
      235,
      147,
      143,
      121,
      124
    ]
  },
  "demand_share": {
    "DC1": 0.25,
    "DC2": 0.2,
    "DC3": 0.2,
    "DC4": 0.2,
    "DC5": 0.15
  },
  "costs": {
    "lost_sales": {
      "SKU_Basic": 50.0,
      "SKU_Premium": 80.0,
      "SKU_ShortLife": 40.0
    },
    "inventory": {
      "SKU_Basic": 1.0,
      "SKU_Premium": 1.5,
      "SKU_ShortLife": 1.0
    },
    "waste": {
      "SKU_Basic": 2.0,
      "SKU_Premium": 3.0,
      "SKU_ShortLife": 2.0
    },
    "fixed_order": 5000.0,
    "transshipment": 0.5,
    "purchasing": {
      "SKU_Basic": 10.0,
      "SKU_Premium": 20.0,
      "SKU_ShortLife": 15.0
    }
  },
  "constraints": {
    "moq": 0,
    "pack_size": 1,
    "budget_per_period": null,
    "waste_limit_pct": null
  },
  "network": {
    "sub_edges": [
      [
        "SKU_Basic",
        "SKU_Premium"
      ]
    ],
    "trans_edges": []
  }
}
```

[OUTPUT FORMAT]
- Import: import gurobipy as gp; from gurobipy import GRB; import json
- Set Gurobi params: m.Params.OutputFlag = 0; m.Params.Threads = 1; m.Params.Seed = 0
- Print at end:
  print(f"status: {{m.Status}}")
  if m.Status == 2:
      print(f"objective: {{m.ObjVal}}")
- Output ONLY executable Python code. No markdown, no explanations.

[TASK]
Write a GurobiPy script that:
1. Parses the JSON data above (use json.loads on the string)
2. Models and solves the optimization problem
3. Prints status and objective value

Standard seasonal retail scenario.