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A hybrid genetic algorithm approach for solving an extension of assembly line balancing problem

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The lively field of assembly line configuration and adjustment often have a significant impact on the performance of manufacturing systems. In this context, assembly line balancing problems (ALBPs) are widely cited in the literature. An ALBP consists of distributing the total product manufacturing workload among the stations along the manufacturing line. Previous research has focused on developing effective and fast solution methods for solving simple assembly line balancing problems (SALBP) and their various extensions. Each extension is motivated by several real-life applications and the need for solving precise practical problems. In this article, another interesting extension of SALBP (named in this work ‘Task Restrictions Assembly Line Balancing Problem’ of type 2 (TRALBP-2)) is focused on. In this situation, the number of stations is known and the objective is to minimise cycle time where both precedence and zoning constraints between tasks must be satisfied. For the resolution of such problem, an innovative hybrid genetic algorithm (HGA) scheme hybridised with a local search procedure is implemented. This genetic algorithm consists of a new representation scheme and a special genetic operator. The effectiveness of the proposed HGA is evaluated through various sets of instances which are (1) theoretically and randomly generated, (2) collected from the literature and (3) based on a real case study of an automotive cable manufacturer. Comparison of the proposed HGA results with CPLEX software for the TRALBP-2 demonstrates that, in a reasonable time, the proposed HGA generates consistent solutions that are very close to their optimal ones. Therefore, the proposed HGA approach is very effective and competitive.
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Keywords: assembly line balancing problem; cycle time; genetic algorithm; local search procedures; precedence and zoning constraints

Document Type: Research Article

Affiliations: 1: Laboratory of Mechanic, Modeling and Production (LA2MP), Engineering School of Sfax, Sfax, Tunisia 2: Dammam College of Technology, Dammam, Kingdom Saudi Arabia 3: Laboratory of Mechanic, Modeling and Production (U2MP), Engineering School of Sfax, Sfax, Tunisia

Publication date: May 3, 2016

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