A population-based algorithm for the bi-objective assembly line worker assignment and balancing problem

A multi-objective evolutionary algorithm (MOEA) is presented for the solution of the bi-criteria assembly line worker assignment and balancing problem (ALWABP). This problem consists of determining the best assignment of the assembly tasks to workers as well as the workers to workstations in accorda...

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Veröffentlicht in:Engineering applications of artificial intelligence Jg. 49; S. 1 - 9
Hauptverfasser: Zacharia, P. Th, Nearchou, Andreas C.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.03.2016
Schlagworte:
Artificial intelligence
Assembly
Assembly line worker assignment and balancing
Assembly lines
Cycle time
Expert systems
Genetic algorithms
Multi-objective optimization
Optimization
Pareto solutions
Smoothness index
Tasks
Workload
Pareto solutions
Multi-objective optimization
Assembly line worker assignment and balancing
Smoothness index
Genetic algorithms
ISSN:0952-1976, 1873-6769
Online-Zugang:Volltext
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Zusammenfassung:A multi-objective evolutionary algorithm (MOEA) is presented for the solution of the bi-criteria assembly line worker assignment and balancing problem (ALWABP). This problem consists of determining the best assignment of the assembly tasks to workers as well as the workers to workstations in accordance with some desired objectives. Task times differ depending on worker skills. Two optimization criteria are considered to be minimized, the cycle time and the smoothness index of the workload of the line. The efficiency of the proposed MOEA is evaluated over a set of benchmarks test problems taken from the open literature. A suitable performance analysis is deployed concerning the quality of the Pareto solutions. The results demonstrate a very satisfactory performance in terms of solution quality.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2015.11.007