Scheduling Is a Constraint Problem Before It Is an AI Problem

Описание и применение

Проблема планирования производства сводится к точному моделированию ограничений, а не к созданию ИИ‑модели. Используя CP‑SAT или MILP, можно быстро генерировать реалистичные планы, учитывая реальные ограничения оборудования и ресурсов.

Задача

A developer receives a ticket to make a factory schedule smarter, but the main challenge is accurately modeling constraints rather than building an AI model.

Результат

By focusing on accurate constraint modeling and finite capacity, the scheduling system produces realistic, executable plans quickly, improving planning efficiency and reducing reliance on undocumented planner knowledge.

Рабочий процесс

• Extract and encode all plant‑specific rules from planners’ knowledge.
• Build a finite‑capacity model that places operations only where resources have room.
• Run a CP‑SAT or MILP solver to generate multiple feasible plans.
• Compare plans to trade off changeovers, overtime, and late orders.
• Select an approved plan and export it back to the ERP/MES with audit trail.

Доказательства

Production scheduling is mostly a constraint satisfaction problem, and the hard part is getting the constraints right.

Источник

The solver underneath is a constraint-programming and mixed-integer model (CP‑SAT or MILP).

Источник

In that dairy case, a week of plans comes back in 90 seconds.

Источник

The most valuable artifact in these projects is often not code. It is the written list of rules that until now lived in a senior planner's head.

Источник