方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 作业车间调度× | 综合计划× | |
|---|---|---|
| 领域 | 运营管理 | 运营管理 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2016 | 1992 |
| 提出者≠ | Pinedo, M. L. | Wallace, T. F. |
| 类型≠ | Combinatorial scheduling problem | Demand-supply planning framework |
| 开创性文献≠ | 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 ↗ |
| 别名 | job scheduling, machine scheduling | sales and operations planning, production planning |
| 相关 | 5 | 5 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|