lot-sizing-problems

Production lot sizing optimization minimizes total setup, holding, and shortage costs by balancing order frequency against inventory volume. You can solve multi-period planning problems using heuristics like Silver-Meal or optimal algorithms like Wagner-Whitin.

Silver-Meal is a practical lot-sizing heuristic that estimates optimal order quantities by minimizing cost per period, while Wagner-Whitin is an exact dynamic programming algorithm that guarantees the mathematically optimal production plan for deterministic demand.

Capacitated lot-sizing with production limits is solved using Mixed-Integer Programming via PuLP. This approach enforces capacity constraints while minimizing setup and holding costs across multiple periods.

Yes, PuLP supports multi-period inventory planning and production scheduling by formulating capacitated lot-sizing problems as Mixed-Integer Programming models. It handles deterministic demand scenarios while respecting production capacity constraints.

Available lot-sizing heuristics for deterministic demand include Lot-for-Lot, Fixed Order Quantity (FOQ), Silver-Meal, and Least Unit Cost (LUC). These heuristics help approximate optimal production quantities without running full optimization algorithms.

Yes, this lot-sizing approach explicitly handles shortage costs alongside setup and holding costs. It minimizes the total cost function across multi-period horizons, allowing you to balance unmet demand penalties against inventory and production expenses.