Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель SCOR× | Планування виробничих завдань (Job Shop Scheduling)× | |
|---|---|---|
| Галузь | Операційний менеджмент | Операційний менеджмент |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 1996 | 2016 |
| Автор методу≠ | Pittiglio, Rabin, Todd & McGrath | Pinedo, M. L. |
| Тип≠ | Supply chain reference framework | Combinatorial scheduling problem |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви≠ | — | job scheduling, machine scheduling |
| Пов'язані | 5 | 5 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
|
|