Comparer des méthodes

	Ordonnancement d'ateliers	Planification Dégagée
Domaine	Gestion des opérations	Gestion des opérations
Famille	Machine learning	Machine learning
Année d'origine	2016	1992
Auteur d'origine	Pinedo, M. L.	Wallace, T. F.
Type	Combinatorial scheduling problem	Demand-supply planning framework
Source fondatrice	Pinedo, M. L. (2016). Scheduling: Theory, algorithms, and systems (5th ed.). Cham: Springer.	Wallace, T. F. (1992). Sales & Operations Planning: The how-to handbook. Cincinnati: APICS Publications.
Alias	job scheduling, machine scheduling	sales and operations planning, production planning
Apparentées	5	5
Résumé	Job shop scheduling is the problem of assigning a set of jobs (tasks) to a set of machines (resources) over time, subject to precedence and capacity constraints, with the goal of optimizing performance metrics such as makespan (total completion time), lateness, or cost. The job shop problem is a classic combinatorial optimization problem in operations research, addressed through heuristics (greedy dispatching rules, simulated annealing, genetic algorithms) and exact algorithms (branch-and-bound, constraint programming). It is fundamental to manufacturing, project management, and computational scheduling.	Aggregate Planning (or Sales & Operations Planning, S&OP) is a collaborative, iterative process that balances demand and supply at a high level—typically grouping products into families and planning over a 3–18 month horizon. Developed formally by Tom Wallace and popularized through APICS, aggregate planning helps organizations align sales forecasts, production capacity, inventory, and workforce to meet demand efficiently while managing costs. It serves as the bridge between strategic business plans and detailed operational execution.