A tabu list-based algorithm for capacitated multilevel lot-sizing with alternate bills of materials and co-production environments
The definition of lot sizes represents one of the most important decisions in production planning. Lot-sizing turns into an increasingly complex set of decisions that requires efficient solution approaches, in response to the time-consuming exact methods (LP, MIP). This paper aims to propose a Tabu...
- Autores:
-
Romero-Conrado, Alfonso R.
Coronado-Hernandez, Jairo R.
Rius-Sorolla, Gregorio
Garcia-Sabater, Jose P.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/4173
- Acceso en línea:
- https://hdl.handle.net/11323/4173
https://repositorio.cuc.edu.co/
- Palabra clave:
- Materials requirements planning
Lot sizing
Flexible manufacturing systems
Heuristic algorithms
Operations research
Tabu list
GMOP
Alternate bill of materials
Coproduction
Planificación de necesidades de materiales
Tamaño de lote
Sistemas de fabricación flexibles
Heurístico algoritmos
la investigación de operaciones
Lista tabu
Lista de materiales alternativos
Coproducción
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-sa/4.0/
Summary: | The definition of lot sizes represents one of the most important decisions in production planning. Lot-sizing turns into an increasingly complex set of decisions that requires efficient solution approaches, in response to the time-consuming exact methods (LP, MIP). This paper aims to propose a Tabu list-based algorithm (TLBA) as an alternative to the Generic Materials and Operations Planning (GMOP) model. The algorithm considers a multi-level, multi-item planning structure. It is initialized using a lot-for-lot (LxL) method and candidate solutions are evaluated through an iterative Material Requirements Planning (MRP) procedure. Three different sizes of test instances are defined and better results are obtained in the large and medium-size problems, with minimum average gaps close to 10.5% |
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