Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель SCOR× | Планирование производственных заказов× | |
|---|---|---|
| Область | Операционный менеджмент | Операционный менеджмент |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1996 | 2016 |
| Автор метода≠ | Pittiglio, Rabin, Todd & McGrath | Pinedo, M. L. |
| Тип≠ | Supply chain reference framework | Combinatorial scheduling problem |
| Основополагающий источник≠ | Stewart, G. (1997). Supply chain operations reference model: SCOR, logistics information management, Vol. 10 No. 5, pp. 62-74. link ↗ | Pinedo, M. L. (2016). Scheduling: Theory, algorithms, and systems (5th ed.). Cham: Springer. DOI ↗ |
| Другие названия≠ | — | job scheduling, machine scheduling |
| Связанные | 5 | 5 |
| Сводка≠ | The Supply Chain Operations Reference Model is a standardized framework for supply chain management developed by the Supply Chain Council (now APICS) in 1996. SCOR provides a structured approach to identify, evaluate, and improve supply chain processes across organizations, regardless of industry. It integrates planning, sourcing, manufacturing, delivery, and returns into a coherent operational model. | 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. |
| ScholarGateНабор данных ↗ |
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