Multi-manned assembly line balancing problem with dependent task times: a heuristic based on solving a partition problem with constraints

This paper aims to study a variant of the multi-manned assembly line balancing problem (MALBP), which considers the possibility of multiple workers simultaneously performing different tasks at the same workstation. In most cases it is assumed that task times are deterministic. This paper takes into...

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Detalles Bibliográficos
Autores: Andreu Casas, Enric, García Villoria, Alberto|||0000-0003-4048-2465, Pastor Moreno, Rafael|||0000-0002-6188-4458
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/387748
Acceso en línea:https://hdl.handle.net/2117/387748
https://dx.doi.org/10.1016/j.ejor.2021.12.002
Access Level:acceso abierto
Palabra clave:Assembly-line methods
Combinatorial optimization
Assembly line balancing
Multi-manned workstations
Dependent task times
Ad-hoc heuristic
Relax-and-Fix
Treball en cadena
Àrees temàtiques de la UPC::Economia i organització d'empreses
Descripción
Sumario:This paper aims to study a variant of the multi-manned assembly line balancing problem (MALBP), which considers the possibility of multiple workers simultaneously performing different tasks at the same workstation. In most cases it is assumed that task times are deterministic. This paper takes into account possible interferences between workers and deals with the MALBP with task times depending on the number of workers at the station. Different procedures are developed: resolutions on the basis of a mathematical model, two Relax-and-Fix procedures, a heuristic based on solving a partition problem with constraints (named “HEUR_PART”) and a set of other variants of the HEUR_PART procedure. The computational experiments indicate that HEUR_PART and the HEUR_PART_SGL variant are the proposals that perform best. Additionally, we show that they obtain better results than the ones published in the literature.