generic-max-supply

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):

1. Read model.py first (required), identify which model block changes, and infer which CSV files are relevant to the requested change.

2. Read only relevant CSV files for the prompt. Do not read all CSVs unless explicitly requested.

3. 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.

4. Run baseline first and capture base objective:

   python model.py
   

5. Fork the working model:

   cp model.py model_whatif.py
   

6. Use edit_file to make only the targeted change in model_whatif.py.

7. 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:

1. 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.
2. Check item count in the Loading data... output matches your expectation (e.g., adding RM4 should increase the item count by 1).
3. 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.