Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Planificación de Talleres (Job Shop Scheduling)× | Diseño de Planta (SLP)× | |
|---|---|---|
| Campo | Gestión de operaciones | Gestión de operaciones |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2016 | 1973 |
| Autor original≠ | Pinedo, M. L. | Muther, R. |
| Tipo≠ | Combinatorial scheduling problem | Layout design methodology |
| Fuente seminal≠ | Pinedo, M. L. (2016). Scheduling: Theory, algorithms, and systems (5th ed.). Cham: Springer. DOI ↗ | Muther, R. (1973). Systematic layout planning (2nd ed.). Boston: Cahners Books. link ↗ |
| Alias | job scheduling, machine scheduling | SLP, plant layout |
| Relacionados | 5 | 5 |
| Resumen≠ | 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. | Systematic Layout Planning (SLP) is a structured methodology developed by Richard Muther in the 1960s–1970s for designing optimal plant and facility layouts. The approach systematizes the consideration of material flow, personnel movement, equipment relationships, and space constraints to minimize material handling costs, improve safety, and enhance flexibility. SLP has become the foundational framework for facility design in manufacturing, warehousing, and service environments. |
| ScholarGateConjunto de datos ↗ |
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