dark_store.py

import numpy as np
def allocate_dark_stores(stores, products, demand_forecast):
    allocations={}
    for store in stores:
        store_alloc={}
        remaining_cap=store["capacity_units"]
        ranked=sorted(products,key=lambda p:-demand_forecast.get((store["name"],p["sku"]),0)*p["margin"])
        for prod in ranked:
            demand=demand_forecast.get((store["name"],prod["sku"]),0)
            qty=min(int(demand*1.5+5),remaining_cap,prod.get("max_per_store",100))
            if qty>0:
                store_alloc[prod["sku"]]=qty; remaining_cap-=qty
            if remaining_cap<=0: break
        allocations[store["name"]]={"products":store_alloc,"utilization":round((store["capacity_units"]-remaining_cap)/store["capacity_units"]*100,1)}
    return allocations
if __name__=="__main__":
    stores=[{"name":"DS-North","capacity_units":500},{"name":"DS-South","capacity_units":400}]
    products=[{"sku":"SKU-A","margin":5},{"sku":"SKU-B","margin":8},{"sku":"SKU-C","margin":3}]
    forecast={("DS-North","SKU-A"):30,("DS-North","SKU-B"):50,("DS-North","SKU-C"):20,
              ("DS-South","SKU-A"):40,("DS-South","SKU-B"):25,("DS-South","SKU-C"):35}
    for store,data in allocate_dark_stores(stores,products,forecast).items(): print(f"{store}: {data}")