A modified ant colony optimization algorithm for multi-objective assembly line balancing
- 302 Downloads
In this paper, a novel ant colony optimization algorithm called modified ant colony optimization algorithm (MACO) is proposed for multi-objective single-model assembly line balancing problem (SALBP). The proposed MACO presents a novel heuristic information combined with subsequent task number and deviation time that can guide ants to find better solutions for SALBP. The proposed MACO also adopts three assignment methods (i.e., forward, backward and local rebalancing assignment methods) and stratified sequential algorithm combined with Pareto-optimal front as multi-objective decision. The objectives of SALBP are to minimize the number of workstations, maximize assembly line efficiency and minimize workload variation among workstations. In the latter part of the paper, the proposed MACO has been applied to solve Scholl benchmark problems which include both small-size and large-size problems. The performance of the proposed MACO has been compared with the multi-objective genetic algorithm and the multiple assignment genetic algorithm and has obtained improved results in many test problems.
KeywordsAnt colony optimization algorithm Assembly line balancing Multi-objective Heuristic information
This work is supported by the National Natural Science Foundation of China (No. 51275104) and supported in part by the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20132304120021).
Compliance with ethical standards
Conflict of interest
The authors Yu-guang Zhong and Bo Ai declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
- Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann ArborGoogle Scholar
- Ogiela L, Ogiela MR (2009) Cognitive techniques in visual data interpretation. In: Studies in computational intelligence, vol 228. Springer, Berlin, HeidelbergGoogle Scholar
- Petropoulos DI, Nearchou AC (2011) A particle swarm optimization algorithm for balancing assembly lines. Assem Autom 31:118–129Google Scholar
- Scholl A (1993) Data of assembly line balancing problems. Publications of Darmstadt Technical University Institute for Business StudiesGoogle Scholar
- Scholl A, Klein R (2007) Assembly line balancing. http://alb.mansci.de/