Assembly Sequence Planning Based on Hybrid Artificial Bee Colony Algorithm
Intelligent algorithm provides a promising approach for solving the Assembly Sequence Planning (ASP) problem on complex products, but there is still challenge in finding best solutions efficiently. In this paper, the artificial bee colony algorithm is mod
- PDF / 835,036 Bytes
- 13 Pages / 439.37 x 666.142 pts Page_size
- 113 Downloads / 253 Views
Abstract. Intelligent algorithm provides a promising approach for solving the Assembly Sequence Planning (ASP) problem on complex products, but there is still challenge in finding best solutions efficiently. In this paper, the artificial bee colony algorithm is modified to deal with this challenge. The algorithm is modified from four aspects. First, for the phase that employed bee works, a simulated annealing operator is introduced to enrich the diversity of nectar sources and to enhance the local searching ability. Secondly, in order to prevent the swarm from falling into local optimal solutions quickly, a tournament selection mechanism is introduced for the onlooker bees to choose the food source. Thirdly, for the phase that scout bee works, a learning mechanism is introduced to improve the quality of new generated food sources and to increase the convergence speed of the algorithm. Finally, a fitness function based on the evaluation indexes of assemblies is proposed to evaluate and select nectar sources. The experimental results show that the modified algorithm is effective and efficient for the ASP problem. Keywords: Artificial bee colony algorithm Simulated annealing operators Assembly sequence planning Multi-objective optimization
1 Introduction Assembly sequence planning (ASP) aims to find a proper sequence of assembly operations under some operational constraints and precedence constraints. ASP is an important manufacturing process in that the quality of assembly has a direct effect on the performance of product. According to a statistical report, a good assembly sequence can reduce costs of manufacturing about 20 %–40 %, and can increase the productivity about 100 %–200 %. Traditional methods of assembly sequence planning are based on cut-sets [1] or assembly knowledge representation [2]. A shortcoming of these methods is that they have to face the combinatorial state explosion problem caused by the increasing of the number of parts [3]. During the past 20 years, many researchers tried to apply intelligent algorithms to solve the ASP problem [4–9] and got many good results. Artificial bee colony (ABC) algorithm is an artificial intelligence algorithm that was proposed for the multi-variable and multi-modal optimization of continuous functions. It simulates the emergent intelligent behaviour of foraging bees in three phases. At the © IFIP International Federation for Information Processing 2016 Published by Springer International Publishing AG 2016. All Rights Reserved Z. Shi et al. (Eds.): IIP 2016, IFIP AICT 486, pp. 59–71, 2016. DOI: 10.1007/978-3-319-48390-0_7
60
W. Yuan et al.
initial phase, the scout bees start to explore the environment randomly in order to find a nectar source. After finding a nectar source, a bee becomes an employed bee and starts to exploit the discovered source; it returns to the hive with the nectar and share information about the source site by performing a dance on the dance area. Onlooker bees waiting in the hive watch the dances and choose a nectar source through the information
Data Loading...
