Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo SCOR× | Escalonamento em Oficina Mecânica× | |
|---|---|---|
| Área | Gestão de operações | Gestão de operações |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 1996 | 2016 |
| Autor original≠ | Pittiglio, Rabin, Todd & McGrath | Pinedo, M. L. |
| Tipo≠ | Supply chain reference framework | Combinatorial scheduling problem |
| Fonte seminal≠ | 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 ↗ |
| Outros nomes≠ | — | job scheduling, machine scheduling |
| Relacionados | 5 | 5 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
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