generic-max-supply - SKILL.md Agent Skill

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.py first, then decide relevant CSV files from the prompt, then read only those CSVs, then run baseline model.py, then copy to model_whatif.py, apply a targeted edit, and run model_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.py first (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:
```
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:
```
python model.py
```
Fork the working model:
```
cp model.py model_whatif.py
```
Use edit_file to make only the targeted change in model_whatif.py.

Run the what-if model and compare against baseline:

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_items
final_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_families
    for 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.