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A self-adaptive estimation of distribution algorithm with differential evolution strategy for supermarket location problem

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Abstract

In modern production systems, an ever-rising product variety has imposed great challenges for in-plant part supply systems used to feed mixed-model assembly lines with required parts. In recent years, many automotive manufacturers have identified the supermarket concept as an efficient part feeding strategy to enable JIT (Just-in-time) deliveries at low costs. This paper studies a discrete supermarket location problem which considers the utilization rate and capacity constraint of the supermarkets simultaneously. Firstly, a mathematical model is developed with the objective of minimizing the total system cost consisting of operating cost and transportation cost. Then, a self-adaptive estimation of distribution algorithm with differential evolution strategy, named DE/AEDA, is proposed to solve the problem. Finally, computational experiments are carried out to analyze the performance of the proposed algorithm compared with the benchmark algorithm by using a non-parametric test method. The results indicate that the proposed algorithm is valid and efficient.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 71471135.

Funding

The authors appreciate the supports to this research from the National Natural Science Foundation of China under Grant No. 71471135.

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  1. School of Mechanical Engineering, Tongji University, Shanghai, 201804, People’s Republic of China

    Bing-Hai Zhou & Fen Tan

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  1. Bing-Hai Zhou
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  2. Fen Tan
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Correspondence to Bing-Hai Zhou.

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Zhou, BH., Tan, F. A self-adaptive estimation of distribution algorithm with differential evolution strategy for supermarket location problem. Neural Comput & Applic 32, 5791–5804 (2020). https://doi.org/10.1007/s00521-019-04052-9

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