An efficient hybrid genetic algorithm to solve assembly line balancing problem with sequence-dependent setup times

• Published in: Computers & industrial engineering Vol. 62; no. 4; pp. 936 - 945
• Main Authors: Yolmeh, Abdolmajid, Kianfar, Farhad
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.05.2012; Pergamon Press Inc
• Subjects: Assembly line balancing; Assembly lines; Assignment problem; Design of experiments; Dynamic programming; Genetic algorithm; Genetic algorithms; Job shops; Production scheduling; Sequence-dependent setup time; Studies; Sequence-dependent setup time; Assembly line balancing; Dynamic programming; Genetic algorithm; Design of experiments
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2011.12.017

► We proposed an efficient genetic algorithm (GA) to solve setup assembly line balancing and scheduling problem (SUALBSP). ► We selected the appropriate values for operators and parameters of GA through DOE method. ► The resulting calibrated GA outperforms all of the other algorithms presented to solve SUALBSP. ► We examined the performance of the proposed GA for different characteristics of the problem. ► Efficiency of the GA decreases when order strength increases. In this paper the setup assembly line balancing and scheduling problem (SUALBSP) is considered. Since this problem is NP-hard, a hybrid genetic algorithm (GA) is proposed to solve the problem. This problem involves assigning the tasks to the stations and scheduling them inside each station. A simple permutation is used to determine the sequence of tasks. To determine the assignment of tasks to stations, the algorithm is hybridized using a dynamic programming procedure. Using dynamic programming, at any time a chromosome can be converted to an optimal solution (subject to the chromosome sequence). Since population diversity is very important to prevent from being trapped in local optimum solutions some diversity maintaining schemes are used to overcome this issue. Operators and parameters of the algorithm is calibrated using design of experiments (DOEs) method. The computational results show that the proposed GA outperforms all of the algorithms presented to solve SUALBSP so far.