A mathematical model and a genetic algorithm for two-sided assembly line balancing

A two-sided assembly line is a type of production line where tasks are performed in parallel at both sides of the line. The line is often found in producing large products such as trucks and buses. This paper presents a mathematical model and a genetic algorithm (GA) for two-sided assembly line bala...

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Veröffentlicht in:Computers & operations research Jg. 36; H. 3; S. 853 - 865
Hauptverfasser: Kim, Yeo Keun, Song, Won Seop, Kim, Jun Hyuk
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.03.2009
Elsevier
Pergamon Press Inc
Schlagworte:
Applied sciences
Assembly lines
Comparative analysis
Exact sciences and technology
Genetic algorithm
Genetic algorithms
Heuristic
Inventory control, production control. Distribution
Localized evolution
Mathematical model
Mathematical models
Operational research and scientific management
Operational research. Management science
Studies
Two-sided assembly line balancing
Two-sided assembly line balancing
Mathematical model
Genetic algorithm
Localized evolution
Bus
Assembly line
Balancing
Production line
Decoding
Modeling
Steady state
Production management
Lorry
Coding
Heuristic method
ISSN:0305-0548, 1873-765X, 0305-0548
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Zusammenfassung:A two-sided assembly line is a type of production line where tasks are performed in parallel at both sides of the line. The line is often found in producing large products such as trucks and buses. This paper presents a mathematical model and a genetic algorithm (GA) for two-sided assembly line balancing (two-ALB). The mathematical model can be used as a foundation for further practical development in the design of two-sided assembly lines. In the GA, we adopt the strategy of localized evolution and steady-state reproduction to promote population diversity and search efficiency. When designing the GA components, including encoding and decoding schemes, procedures of forming the initial population, and genetic operators, we take account of the features specific to two-ALB. Through computational experiments, the performance of the proposed GA is compared with that of a heuristic and an existing GA with various problem instances. The experimental results show that the proposed GA outperforms the heuristic and the compared GA.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2007.11.003