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Algorithms for solving assembly sequence planning problems

  • S.I.: DPTA Conference 2019
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Assembly sequence planning is one of the key issues in DFA and computer-aided assembly process planning research for concurrent engineering. The purpose of this paper is to solve the problem of insufficient individual intelligence in evolutionary algorithms for assembly sequence planning, and a evolutionary algorithm for assembly sequence planning is designed. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the hybrid assembly sequence planning and assembly line balance problems. According to the assembly sequence problem, the number of assembly tool changes and the number of assembly orientation changes are transformed into the operation time of the assembly line. At the same time, the transportation of heavy parts in the assembly balance problem is considered. Then, by extracting the connection relationship and information of the parts, the disassembly method is used to inversely obtain the disassembly support matrix, and then, it is used to obtain the priority relationship diagram of the assembly operation tasks that indicate the order constraints of the job tasks on the assembly line. Aiming at the shortcoming that particle swarm optimization algorithm is easy to fall into local optimum, a various population strategy is adopted to shorten the evolution stagnation time, improve the evolution efficiency of particle swarm optimization algorithm, and enhance the optimization ability of the algorithm. Combined with the three evaluation indicators of assembly geometric feasibility, assembly process continuity, and assembly tool change times, a fitness function is constructed to achieve multi-objective optimization. Finally, experiments show that the multi-agent evolutionary algorithm is incorporated into the planning process to obtain an accurate solution through the various population strategy–particle swarm optimization algorithm, which proves the feasibility of the compound algorithm and has better performance in solving assembly sequence planning problems.

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Authors and Affiliations

  1. School of Mechanical Engineering, Shenyang University, Shenyang, 110044, Liaoning, China

    Yingying Su, Haixu Mao & Xianzhao Tang

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  1. Yingying Su
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  2. Haixu Mao
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  3. Xianzhao Tang
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Correspondence to Yingying Su.

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Su, Y., Mao, H. & Tang, X. Algorithms for solving assembly sequence planning problems. Neural Comput & Applic 33, 525–534 (2021). https://doi.org/10.1007/s00521-020-05048-6

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  • DOI: https://doi.org/10.1007/s00521-020-05048-6

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