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| 物料需求计划× | 作业车间调度× | |
|---|---|---|
| 领域 | 运营管理 | 运营管理 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1975 | 2016 |
| 提出者≠ | Joseph Orlicky | Pinedo, M. L. |
| 类型≠ | Material planning algorithm | Combinatorial scheduling problem |
| 开创性文献≠ | Orlicky, J. (1975). Material requirements planning: The new way of life in production and inventory management. New York: McGraw-Hill. link ↗ | Pinedo, M. L. (2016). Scheduling: Theory, algorithms, and systems (5th ed.). Cham: Springer. DOI ↗ |
| 别名 | MRP, MRP I | job scheduling, machine scheduling |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | 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. |
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