Abstract
In the car-sequencing problem of mixed-model assembly lines, a series of cars with different model types will be put into the assembly line in a certain order considering a variety of goals and constraints. In this paper, a multi-objective cuckoo search algorithm based on the record matrix is proposed to solve this problem. In this algorithm, the factors, including the variation of parts usage rates, variation of workstation workload, idle time, overload time, and model switching cost are considered. The record matrix proposed in this paper is utilized to record the characteristic information of the optimal solutions and historical solutions. Meanwhile, two search strategies based on the record matrix are proposed to enhance the ability of local search and global search in the algorithm. The proposed algorithm is verified by a real case. The results show that the proposed model and algorithm have good results, and they have the potential to address other similar problems.
Highlights
Nowadays, it is an important challenge for automobile manufacturers to produce various cars with low costs
A multi-objective cuckoo search algorithm based on the record matrix is proposed
Based on the characteristics of mixed-model assembly processes, a multi-objective cuckoo search algorithm based on the record matrix is proposed to solve the car-sequencing problem
Summary
It is an important challenge for automobile manufacturers to produce various cars with low costs. Reference [21] presented a simulated annealing meta-heuristic algorithm to address the mixed-model assembly line sequencing problem, and three objectives were optimized: idle cost, setup cost, and production rate variation cost. In the aspect of the level scheduling, this paper considers the variation of parts usage rates, variation of station workload, idle time, overload time, and model switching cost to design the sequencing plan of mixed-model car assembly lines. For the mixed-model car assembly line of the assembly workshop, this paper comprehensively considers the variation of parts usage rates, variation of workstation workload, idle time, overload time, and model switching cost to solve the car-sequencing problem. In equation (1), the first item is the ideal usage for part type e during top z stages, and the second item is the actual usage for part type e during top z stages
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