Skill v1.0.1
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version: "1.0.1" name: generic-max-supply description: > Multi-period supply chain planning model: data files, BOM structure, variable/constraint reference for the max-supply base model. usage: >
- Use for formulating or modifying the max-supply MILP (constraints, objective, BOM).
Read data file map and variable/constraint reference before coding. Scripts and data under this skill (scripts/, scripts/data/).
- One task only; do not split (e.g. change model, run, compare in one step).
Generic Max-Supply Planning
Core
- Execution order (mandatory): read
scripts/model.pyfirst, then decide relevant CSV files from the prompt, then read only those CSVs, then run baselinemodel.py, then copy tomodel_whatif.py, apply a targeted edit, and runmodel_whatif.py. - Source data and reference model: scripts/ for Python (
data.py,model.py); scripts/data/ for CSV inputs.load_data(data_dir, num_periods)reads from that data directory; optional cost/backlog files are read separately if needed.
Workspace layout
| Location | Contents | |
|---|---|---|
| scripts/ | Python: data.py (load_data), model.py (build/solve). Run from repository root (or your run directory) with python scripts/model.py. | |
| scripts/data/ | Required and optional CSV files (items, families, processes, supply, demand, etc.). All files from the data file map live here. |
Using in the working directory
Do not modify the skill files. To run or change the model: (1) Copy skills/generic-max-supply/scripts/ into your run directory. (2) Edit only the copy. (3) Run from that run directory or repository root. Do not assume a fixed sandbox cwd.
cp -r skills/generic-max-supply/scripts . && python3 scripts/model.py
(From repository root, this copies skills/generic-max-supply/scripts/ to ./scripts/.)
What-If Variants
To run a what-if scenario (e.g. change opening inventory, tighten a supply cap, modify a cost):
- Read
model.pyfirst (required), identify which model block changes, and infer which CSV files are relevant to the requested change.
- Read only relevant CSV files for the prompt. Do not read all CSVs unless explicitly requested.
- Copy the scripts to the working directory and fix the data path:
``bash cp skills/generic-max-supply/scripts/data.py data.py cp skills/generic-max-supply/scripts/model.py model.py ` Then update DATA_DIR in the copied model.py` to match your CSV location.
- Run baseline first and capture base objective:
``bash python model.py ``
- Fork the working model:
``bash cp model.py model_whatif.py ``
- Use
edit_fileto make only the targeted change inmodel_whatif.py.
- Run the what-if model and compare against baseline:
``bash python model_whatif.py # ← this is the what-if result ``
Do not rewrite the model from scratch. One targeted edit keeps the change isolated and the comparison meaningful.
Problem overview
Type: MILP (Mixed-Integer Linear Program) Sense: MAXIMIZE Horizon: configurable num_periods (default 10)
The planner decides how many units of each manufacturing process to run on each resource in each period. The goal is to maximise the weighted sum of finished-good inventory at the end of the last period. Constraints capture multi-level BOM structure, lead-time offsets, integer yield truncation, material supply caps, and machine-hour capacity.
Data file map
All files live in the same directory (scripts/data/). load_data(data_dir, num_periods) reads all required CSVs from that directory; the three optional cost/backlog files are read separately if needed.
| File | Key columns | What it populates | |
|---|---|---|---|
items.csv | item_id, name, family_id | all_items, item_family lookup | |
families.csv | family_id, name, is_constrained | constrained_families, unconstrained_families | |
processes.csv | process_id, name, lead_time, hours_per_unit | all_processes, process_lead_time, process_hours | |
process_inputs.csv | process_id, item_id, quantity | process_input_qty[(p,i)], item_consuming_processes | |
process_outputs.csv | process_id, item_id, quantity | process_output_qty[(p,i)], item_producing_processes, derives produced_items | |
process_resources.csv | process_id, resource_id | process_resource_pairs, process_to_resources, resource_to_processes | |
resources.csv | resource_id, name, period, available_hours | all_resources, resource_capacity[(r,t)] | |
supply.csv | item_id, period, quantity | supply_qty[(i,t)] — upper bound on procurement per period | |
demand.csv | item_id, period, quantity, priority_weight | final_items, demand_weight[i] (only priority_weight is used in the base model) |
Optional files — NOT loaded by `data.py`; read separately if needed:
| File | Key columns | Purpose | |
|---|---|---|---|
item_costs.csv | item_id, unit_cost, holding_cost | Per-unit purchase cot and per-period holding cost | |
resource_costs.csv | resource_id, production_cost_per_hour | Cost per machine-hour by resource | |
backlog_params.csv | parameter, value | Single row: backlog_cost_rate |
Key sets and derivation
produced_items = set(process_outputs_df["item_id"])procured_items = all_items - produced_itemsfinal_items = set(demand_df["item_id"])constrained_families = {f where is_constrained == True}unconstrained_families = {f where is_constrained == False}# A process is constrained if ANY of its output items is in a constrained family.process_is_constrained[p] = any(item_family[i] in constrained_familiesfor i in outputs_of(p))
In the sample dataset: FAM_FG, FAM_SA, FAM_RM1 are constrained; FAM_RM2 is not. RM3 (family FAM_RM2) is procured but unconstrained — no supply cap applied. All four processes (PROC1–PROC4) are constrained because they produce items in constrained families.
Variable reference
| Variable | Represents | Domain | Initialisation | |
|---|---|---|---|---|
x[p, r, t] | Units of process p executed on resource r starting in period t | Continuous ≥ 0 | addVariable(lb=0, vtype=CONTINUOUS) | |
produced[i, t] | Continuous (possibly fractional) output of item i in period t | Continuous ≥ 0 | addVariable(lb=0, vtype=CONTINUOUS) | |
used[i, t] | Usable (floor-truncated) integer output of item i in period t | Integer ≥ 0 | addVariable(lb=0, vtype=INTEGER) | |
buy[i, t] | Units of procured item i purchased in period t | Continuous ≥ 0 | addVariable(lb=0, vtype=CONTINUOUS) | |
inventory[i, t] | On-hand inventory of item i at end of period t | Continuous ≥ 0 | addVariable(lb=0, vtype=CONTINUOUS) | |
inventory[i, 0] | Opening balance (fixed to zero) | Continuous, lb=ub=0 | addVariable(lb=0, ub=0, vtype=CONTINUOUS) |
inventory[i, 0] is a fixed variable (lb = ub = 0). For a non-zero opening balance, set ub = lb = opening_balance.
Constraint map
| Label | Name pattern | What it enforces | Variables linked | |
|---|---|---|---|---|
| C1 | prod_def_{i}_{t} | produced[i,t] equals the sum of BOM-output quantities from all processes that started at t − lead_time | produced, x | |
| C2a | trunc_upper_{i}_{t} | used[i,t] ≤ produced[i,t] (integer cannot exceed real output) | used, produced | |
| C2b | trunc_lower_{i}_{t} | used[i,t] ≥ produced[i,t] − (1 − ε) with ε = 1e-4 (forces integer to be the floor) | used, produced | |
| C3a | bal_proc_{i}_{t} | Material balance for procured items: inv[i,t] = inv[i,t−1] + buy[i,t] − consumption | inventory, buy, x | |
| C3b | bal_prod_{i}_{t} | Material balance for produced items: inv[i,t] = inv[i,t−1] + used[i,t] − consumption | inventory, used, x | |
| C4 | supply_{i}_{t} | buy[i,t] ≤ supply_qty[(i,t)] — only applied to procured items in constrained families | buy | |
| C5 | cap_{r}_{t} | Σ hours_per_unit[p] · x[p,r,t] ≤ capacity[(r,t)] — only processes where process_is_constrained[p] is True | x |
Lead-time note: C1 uses start_t = t − lead_time[p]. If start_t < 1, the term is skipped (process cannot have started before period 1).
Objective
last_t = max(data.periods)obj = Σ demand_weight[i] * inventory[i, last_t] for i in final_items
Maximise the priority-weighted inventory of finished goods at the end of the planning horizon. demand_weight comes from the priority_weight column in demand.csv (the quantity column in demand.csv is present but not used by the base model).
Solver settings (default)
settings = SolverSettings()settings.set_parameter("mip_relative_gap", 0.01)prob.solve(settings)
mip_relative_gap = 0.01 (1% gap). Do not change solver precision (e.g. do not tighten to 0.1% or 0.0); use this default only.
Sanity-checking results
After solving, verify the model is correct:
- Check model size changed when you add items/constraints. The solver log prints
Solving a problem with N constraints, M variables. If you added a new item or constraint but N and M are unchanged, your data edits did not take effect — check that you edited the CSV files in the working directory. - Check item count in the
Loading data...output matches your expectation (e.g., adding RM4 should increase the item count by 1). - Check objective value actually changed meaningfully. Differences smaller than the MIP gap (~1%) may just be solver noise, not real model changes.
Quick links by task
| Task | Use | |
|---|---|---|
| Implement or extend the base model | Data file map, key sets, variable reference, constraint map, objective. | |
| Load or modify input data | Data file map; optional files read separately. | |
| Tune or run the solver | Solver settings (default); data in scripts/data/ by default. | |
| Verify results after changes | Sanity-checking results. |