A mathematical model and artificial bee colony algorithm for the lexicographic bottleneck mixed-model assembly line balancing problem

• Published in: Journal of intelligent manufacturing Vol. 30; no. 8; pp. 2913 - 2925
• Main Authors: Kucukkoc, Ibrahim, Buyukozkan, Kadir, Satoglu, Sule Itir, Zhang, David Z.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2019; Springer Nature B.V
• Subjects: Algorithms; Assembly lines; Balancing; Business and Management; Control; Cycle time; Machines; Manufacturing; Mathematical models; Mechatronics; Optimization algorithms; Processes; Production; Robotics; Search algorithms; Sequential scheduling; Swarm intelligence; Work stations; Workload; Workstations; Artificial bee colony algorithm; Lexicographic bottleneck; Assembly line balancing; Mathematical model; Mixed-model lines
• ISSN: 0956-5515; 1572-8145
• DOI: 10.1007/s10845-015-1150-5

Typically, the total number of required workstations are minimised for a given cycle time (this problem is referred to as type-1), or cycle time is minimised for a given number of workstations (this problem is referred to as type-2) in traditional balancing of assembly lines. However, variation in workload distributions of workstations is an important indicator of the quality of the obtained line balance. This needs to be taken into account to improve the reliability of an assembly line against unforeseeable circumstances, such as breakdowns or other failures. For this aim, a new problem, called lexicographic bottleneck mixed-model assembly line balancing problem (LB-MALBP), is presented and formalised. The lexicographic bottleneck objective, which was recently proposed for the simple single-model assembly line system in the literature, is considered for a mixed-model assembly line system. The mathematical model of the LB-MALBP is developed for the first time in the literature and coded in GAMS solver, and optimal solutions are presented for some small scale test problems available in the literature. As it is not possible to get optimal solutions for the large-scale instances, an artificial bee colony algorithm is also implemented for the solution of the LB-MALBP. The solution procedures of the algorithm are explored illustratively. The performance of the algorithm is also assessed using derived well-known test problems in this domain and promising results are observed in reasonable CPU times.