Optimal Balancing of Multi-Objective Assembly Lines via Metaheuristic Approach SUPAPORN SUWANNARONGSRI * and DEACHA PUANGDOWNREONG ** * Department of Industrial Engineering, Faculty of Engineering South-East Asia University 19/1 Petchkasem Road, Nongkham District, Bangkok, 10160 THAILAND supaporn-eng@sau.ac.th http://www.sau.ac.th ** Department of Electrical Engineering, Faculty of Engineering South-East Asia University 19/1 Petchkasem Road, Nongkham District, Bangkok, 10160 THAILAND dp@sau.ac.th http://www.sau.ac.th Abstract: - The article proposes the metaheuristic approach for solving the assembly line balancing (ALB) problems. The adaptive tabu search (ATS) method and the practicing heuristic (PH) technique are combined to provide optimal solutions of the ALB problems. The ATS is used to address the number of tasks assigned for each workstation, while the PH is conducted to assign the sequence of tasks for each workstation according to precedence constraints. The workload variance, the idle time and the line efficiency are performed as the multiple objective function. The proposed approach is tested against six benchmark ALB problems and one real-world ALB problem. Obtained results are compared with those obtained by the single objective approach. As results, the proposed metaheuristic approach based on the ATS and the PH associated with the multi- objective function is capable of producing solutions superior to the single objective function. Key-Words: - assembly line balancing, metaheuristic approach, adaptive tabu search, multi-objective function 1 Introduction Over six decades, the assembly line balancing (ALB) problem has been one of the most interesting topics among industrial researchers. Considered as the class of NP-hard combinatorial optimization problems [1], the ALB problem is one of the classic problems in industrial engineering. By literatures, several methods to provide the optimal solutions of the ALB problems were launched, for example, heuristic approaches [2],[3],[4], artificial intelligent (AI) search techniques such as the genetic algorithm (GA) [5] and the tabu search (TS) [6], and hybrid AI methods [7],[8],[9],[10]. Based on the optimization context, the multiple objective optimizations can probably give better solutions than the single objective approach. With this idea, many researches have moved to use multiple objective approach to solve the ALB problems [11],[12]. In 2004, the modified version of the TS method named the adaptive tabu search (ATS) method was launched [13]. The ATS contains two distinctive mechanisms denoted as back-tracking (BT) and adaptive radius (AR) mechanisms, respectively. The ATS can be regarded as one of the most powerful AI search techniques. Convergence proof and performance evaluation of the ATS have been reported [13],[14]. Moreover, the ATS has been widely applied to various real-world engineering problems. In 2008, the ATS associated with the partial random permutation (PRP) technique was developed to solve the ALB problems [15]. As previous results, it was found that such the approach could provide satisfy solutions. However, it spent amount of search time, when applied to solve the ALB problems containing a lot of tasks. In this paper, the metaheuristic approach consisting of the ATS and the practicing heuristic (PH) technique are proposed to provide optimal solutions of the ALB problems. The ATS is used to address the number of tasks assigned for each workstation, while the proposed practicing heuristic (PH) technique is conducted to arrange the sequence of tasks according to the precedent constraints. The workload variance, the idle time and the line efficiency are performed together as the multiple objective functions. The proposed approach is tested against six benchmark ALB problems suggested by Scholl [16] and one real-world ALB problem from a survey of literature [17]. Obtained results will be compared with those obtained by the single Proceedings of the 10th WSEAS International Conference on EVOLUTIONARY COMPUTING ISSN: 1790-5109 52 ISBN: 978-960-474-067-3