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| Harmonogramowanie produkcji gniazdowej× | Planowanie agregatowe× | |
|---|---|---|
| Dziedzina | Zarządzanie operacyjne | Zarządzanie operacyjne |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2016 | 1992 |
| Twórca≠ | Pinedo, M. L. | Wallace, T. F. |
| Typ≠ | Combinatorial scheduling problem | Demand-supply planning framework |
| Źródło pierwotne≠ | Pinedo, M. L. (2016). Scheduling: Theory, algorithms, and systems (5th ed.). Cham: Springer. DOI ↗ | Wallace, T. F. (1992). Sales & Operations Planning: The how-to handbook. Cincinnati: APICS Publications. link ↗ |
| Inne nazwy | job scheduling, machine scheduling | sales and operations planning, production planning |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | 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. |
| ScholarGateZbiór danych ↗ |
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