A Mixed Algorithm for Integrated Scheduling Optimization in AS/RS and Hybrid Flowshop

Jiansha Lu, Lili Xu, Jinghao Jin and Yiping Shao ()
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Jiansha Lu: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Lili Xu: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Jinghao Jin: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Yiping Shao: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China

Energies, 2022, vol. 15, issue 20, 1-17

Abstract: The integrated scheduling problem in automated storage and retrieval systems (AS/RS) and the hybrid flowshop is critical for the realization of lean logistics and just-in-time distribution in manufacturing systems. The bi-objective model that minimizes the operation time in AS/RS and the makespan in the hybrid flowshop is established to optimize the problem. A mixed algorithm, named GA-MBO algorithm, is proposed to solve the model, which combines the advantages of the strong global optimization ability of genetic algorithm (GA) and the strong local search ability of migratory birds optimization (MBO). To avoid useless solutions, different cross operations of storage and retrieval tasks are designed. Compared with three algorithms, including improved genetic algorithm, improved particle swam optimization, and a hybrid algorithm of GA and particle swam optimization, the experimental results showed that the GA-MBO algorithm improves the operation efficiency by 9.48%, 19.54%, and 5.12% and the algorithm robustness by 35.16%, 54.42%, and 39.38%, respectively, which further verified the effectiveness of the proposed algorithm. The comparative analysis of the bi-objective experimental results fully reflects the superiority of integrated scheduling optimization.

Keywords: automated storage and retrieval system; hybrid flowshop; genetic algorithm; migratory birds optimization algorithm; GA-MBO (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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