An effective hybrid honey bee mating optimization algorithm for balancing mixed-model two-sided assembly lines. (January 2015)

• Record Type: Journal Article
• Authors: Yuan, Biao; Zhang, Chaoyong; Shao, Xinyu; Jiang, Zhibin
• Is Part Of: Computers & operations research. Volume 53(2015)
• Page Start: 32
• Page End: 41
• Publication Date: 2015-01
• Subjects: Mixed-model -- Two-sided assembly line -- Honey bee mating optimization -- Simulated annealing; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2014.07.011
• Languages: English
• ISSNs: 0305-0548

Abstract

Abstract: Mixed-model two-sided assembly lines are widely used in a range of industries for their abilities of increasing the flexibility to meet a high variety of customer demands. Balancing assembly lines is a vital design issue for industries. However, the mixed-model two-sided assembly line balancing (MTALB) problem is NP-hard and difficult to solve in a reasonable computational time. So it is necessary for researchers to find some efficient approaches to address this problem. Honey bee mating optimization (HBMO) algorithm is a population-based algorithm inspired by the mating process in the real colony and has been applied to solve many combinatorial optimization problems successfully. In this paper, a hybrid HBMO algorithm is presented to solve the MTALB problem with the objective of minimizing the number of mated-stations and total number of stations for a given cycle time. Compared with the conventional HBMO algorithm, the proposed algorithm employs the simulated annealing (SA) algorithm with three different neighborhood structures as workers to improve broods, which could achieve a good balance between intensification and diversification during the search. In addition, a new encoding and decoding scheme, including the adjustment of the final mated-station, is devised to fit the MTALB problem. The proposed algorithm is tested on several sets of instances and compared with Mixed Integer Programming (MIP) and SA. The superior results of these instances validate the effectiveness of the proposed algorithm. Graphical abstract: