Lexicographic bottleneck mixed-model assembly line balancing problem: Artificial bee colony and tabu search approaches with optimised parameters

Buyukozkan K., Kucukkoc I., Satoğlu Ş. I., Zhang D. Z.

EXPERT SYSTEMS WITH APPLICATIONS, vol.50, pp.151-166, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 50
  • Publication Date: 2016
  • Doi Number: 10.1016/j.eswa.2015.12.018
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.151-166
  • Keywords: Mixed-model assembly line balancing, Lexicographic bottleneck, Artificial bee colony, Tabu search, Parameter optimisation, Response surface methodology, ALGORITHM-BASED APPROACH, GENETIC ALGORITHM, PARALLEL WORKSTATIONS, CONFIGURATION, FORMULATION, TIME
  • Karadeniz Technical University Affiliated: Yes


The lexicographic bottleneck assembly line balancing problem is a recently introduced problem which aims at obtaining a smooth workload distribution among workstations. This is achieved hierarchically. The workload of the most heavily loaded workstation is minimised, followed by the workload of the second most heavily loaded workstation and so on. This study contributes to knowledge by examining the application of the lexicographic bottleneck objective on mixed-model lines, where more than one product model is produced in an inter-mixed sequence. The main characteristics of the lexicographic bottleneck mixed-model assembly line balancing problem are described with numerical examples. Another contribution of the study is the methodology used to deal with the complex structure of the problem. Two effective meta-heuristic approaches, namely artificial bee colony and tabu search, are proposed. The parameters of the proposed meta-heuristics are optimised using response surface methodology, which is a well-known design of experiments technique, as a unique contribution to the expert and intelligent systems literature. Different from the common tendency in the literature (which aims to optimise one parameter at a time), all parameters are optimised simultaneously. Therefore, it is shown how a complex production planning problem can be solved using sophisticated artificial intelligence techniques with optimised parameters. The methodology used for parameter setting can be applied to other metaheuristics for solving complex problems in practice. The performances of both algorithms are assessed using well-known test problems and it is observed that both algorithms find promising solutions. Artificial bee colony algorithm outperforms tabu search in minimising the number of workstations while tabu search shows a better performance in minimising the value of lexicographic bottleneck objective function. (C) 2015 Elsevier Ltd. All rights reserved.