Mathematical modelling and a discrete cuckoo search particle swarm optimization algorithm for mixed model sequencing problem with interval task times

Jiahua Zhang (), Xuemei Liu and Beikun Zhang
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Jiahua Zhang: Wuxi Vocational Institute of Arts and Technology
Xuemei Liu: Tongji University
Beikun Zhang: BYD Auto Industry Company Limited

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 8, No 13, 3837-3856

Abstract: Abstract This paper addresses a sequencing problem with uncertain task times in mixed model assembly lines. In this problem, task times are not known exactly but are given by intervals of their possible values. A mixed integer non-linear programming model is developed to minimize the utility work time, which is converted into a mixed integer linear programming (MILP) model to deal with small-sized instances optimally. Due to the NP-hardness of the problem, a discrete cuckoo search particle swarm optimization (DCSPSO) algorithm is developed. In the proposed algorithm, a particle position is updated by crossover and mutation operators in the discrete domain and discrete Levy flight is used to improve the solution quality further. Numerical experiments are conducted on the designed instances. The results indicate that the DCSPSO algorithm outperforms the exact method and the other three meta-heuristic algorithms. A case study of engine cylinder heads sequencing problem shows the proposed approach can obtain multiple solutions for decision-makers to choose according to the actual situation.

Keywords: Mixed-model sequencing; Uncertain task times; Robust optimization; Discrete cuckoo search particle swarm optimization algorithm (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10845-023-02300-3

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