Swarm intelligence
Intelligent selective disassembly using the ant colony algorithm
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A genetic algorithm for the optimisation of assembly sequences
Computers and Industrial Engineering - Special issue: Sustainability and globalization: Selected papers from the 32 nd ICC&IE
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
A three-stage integrated approach for assembly sequence planning using neural networks
Expert Systems with Applications: An International Journal
A hierarchical approach on assembly sequence planning and optimal sequences analyzing
Robotics and Computer-Integrated Manufacturing
Applications of particle swarm optimisation in integrated process planning and scheduling
Robotics and Computer-Integrated Manufacturing
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Nested partitions method for assembly sequences merging
Expert Systems with Applications: An International Journal
A hybrid genetic algorithm for multi-objective product plan selection problem with ASP and ALB
Expert Systems with Applications: An International Journal
Design optimization with chaos embedded great deluge algorithm
Applied Soft Computing
Mechanical assembly planning using ant colony optimization
Computer-Aided Design
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Assembly sequence planning of complex products is difficult to be tackled, because the size of the search space of assembly sequences is exponentially proportional to the number of parts or components of the products. Contrasted with the conventional methods, the intelligent optimization algorithms display their predominance in escaping from the vexatious trap. This paper proposes a chaotic particle swarm optimization (CPSO) approach to generate the optimal or near-optimal assembly sequences of products. Six kinds of assembly process constraints affecting the assembly cost are concerned and clarified at first. Then, the optimization model of assembly sequences is presented. The mapping rules between the optimization model and the traditional PSO model are given. The variable velocity in the traditional PSO algorithm is changed to the velocity operator (vo) which is used to rearrange the parts in the assembly sequences to generate the optimal or near-optimal assembly sequences. To improve the quality of the optimal assembly sequence and increase the convergence rate of the traditional PSO algorithm, the chaos method is proposed to provide the preferable assembly sequences of each particle in the current optimization time step. Then, the preferable assembly sequences are considered as the seeds to generate the optimal or near-optimal assembly sequences utilizing the traditional PSO algorithm. The proposed method is validated with an illustrative example and the results are compared with those obtained using the traditional PSO algorithm under the same assembly process constraints.