An Assembly Sequence Planning Approach with a Multi-state Particle Swarm Optimization
Assembly sequence planning (ASP) becomes one of the major challenges in the product design and manufacturing. A good assembly sequence leads in reducing the cost and time of the manufacturing process. However, assembly sequence planning is known as a clas
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Abstract. Assembly sequence planning (ASP) becomes one of the major challenges in the product design and manufacturing. A good assembly sequence leads in reducing the cost and time of the manufacturing process. However, assembly sequence planning is known as a classical hard combinatorial optimization problem. Assembly sequence planning with more product components becomes more difficult to be solved. In this paper, an approach based on a new variant of Particle Swarm Optimization Algorithm (PSO) called the multi-state of Particle Swarm Optimization (MSPSO) is used to solve the assembly sequence planning problem. As in of Particle Swarm Optimization Algorithm, MSPSO incorporates the swarming behaviour of animals and human social behaviour, the best previous experience of each individual member of swarm, the best previous experience of all other members of swarm, and a rule which makes each assembly component of each individual solution of each individual member is occurred once based on precedence constraints and the best feasible sequence of assembly is then can be determined. To verify the feasibility and performance of the proposed approach, a case study has been performed and comparison has been conducted against other three approaches based on Simulated Annealing (SA), Genetic Algorithm (GA), and Binary Particle Swarm Optimization (BPSO). The experimental results show that the proposed approach has achieved significant improvement. Keywords: Combinatorial optimization problem Assembly sequence planning Meta-heuristic Multi-state particle swarm optimization algorithm
1 Introduction The cost of assembly processes are determined by assembly plans. Assembly sequence planning, which is an important part of assembly process planning, plays an essential role in the manufacturing industry. Given the product-assembly model of an assembly © Springer International Publishing Switzerland 2016 H. Fujita et al. (Eds.): IEA/AIE 2016, LNAI 9799, pp. 841–852, 2016. DOI: 10.1007/978-3-319-42007-3_71
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sequence planning (ASP), the shorter assembly time or reducing cost can be found after determining the sequences of components. This problem is regarded as a large-scale, highly constrained combinatorial optimization problem, and it is nearly impossible to generate and then evaluate all the assembly sequences in order to obtain the optimal one, either with human’s interaction or through computer programs. Historically, the typical combinatorial explosion problem needs experienced assembly technicians to determine assembly plans. Nonetheless, this manual assembly planning approach involves more time and makes quantitative assembly costs of the assembly solution cannot be achieved. Thus, many studies in the last two decades have intensely done based on geometric reasoning capability and full automatism to find more efficient algorithms for the automated ASP. Approaches used for representation of assembly sequence planning can be categorized into four groups. These groups are: 1. Graph based representation.
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