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| Harmonogramowanie produkcji gniazdowej× | Planowanie Potrzeb Materiałowych× | |
|---|---|---|
| Dziedzina | Zarządzanie operacyjne | Zarządzanie operacyjne |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2016 | 1975 |
| Twórca≠ | Pinedo, M. L. | Joseph Orlicky |
| Typ≠ | Combinatorial scheduling problem | Material planning algorithm |
| Źródło pierwotne≠ | Pinedo, M. L. (2016). Scheduling: Theory, algorithms, and systems (5th ed.). Cham: Springer. DOI ↗ | Orlicky, J. (1975). Material requirements planning: The new way of life in production and inventory management. New York: McGraw-Hill. link ↗ |
| Inne nazwy | job scheduling, machine scheduling | MRP, MRP I |
| 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. | Material Requirements Planning (MRP) is a computerized system developed by Joseph Orlicky in the 1970s that calculates material requirements based on master production schedules and bill-of-materials data. MRP determines what materials to buy, how much to order, and when to order them to meet production demand while minimizing inventory carrying costs. It became a foundational technology for manufacturing planning and later evolved into manufacturing resource planning (MRP II) and enterprise resource planning (ERP) systems. |
| ScholarGateZbiór danych ↗ |
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