MCP Server Supply Chain Optimisation - Doc String Interprete Results

docstring_interprete_results.py

"""
  HOW TO READ THIS RUN (based on the sample JSON)
    -----------------------------------------------
    - Objective = cost: the model opens 4 plants (INDIA-LOW, JAPAN-HIGH, BRAZIL-HIGH, INDIA-HIGH),
      heavily exporting from INDIA and BRAZIL to the USA, while JAPAN supplies itself.
    - Unit economics: unit_cost ≈ €116.10; total_costs ≈ €5.683M (divide by 1e6 for M€).
    - Market economics: “JAPAN” is the most expensive market; “INDIA” the cheapest.
    - Localization ratio: local_prod / total_prod = 18,050 / 48,950 ≈ 36.87% local, 63.13% export.
    - Footprint per unit: e.g., unit_co2 ≈ 35.55 kgCO₂e/unit. To approximate total CO₂:
        unit_co2 * total_units ≈ 35.55 * 48,950 ≈ 1,740,100 kgCO₂e (≈ 1,740 tCO₂e).QUICK SANITY CHECKS
    -------------------
    - Demand balance: sum_i flow(i→j) == demand(j) for each market j.
    - Capacity: sum_j flow(i→j) ≤ sum_s CAP(i,s) * open(i,s) for each i.
    - Unit-cost check: total_costs / total_units == unit_cost.
    - If infeasible: your per-unit caps (max_water/energy/waste/CO₂) may be too tight.
    TYPICAL USES
    ------------
    - Baseline vs. sustainability: run once with objective="Production Cost", then with
      objective="CO2 Emissions" (or Water/Energy) using the same caps to quantify the
      trade-off (Δcost, Δunit_CO₂, change in plant openings/flows).
    - Narrative for execs: report top flows (e.g., INDIA→USA=15.5k, BRAZIL→USA=12.5k),
      open sites, unit cost, and per-unit footprints. Convert costs to M€ with unit_monetary.
    EXAMPLES
    --------
      # Min cost baseline
      run_network(LaunchParamsNetwork(objective="Production Cost"))
      # Minimize CO₂ with a water cap
      run_network(LaunchParamsNetwork(objective="CO2 Emissions", max_water=3500))
      # Minimize Water with an energy cap
      run_network(LaunchParamsNetwork(objective="Water Usage", max_energy=780))
    """