Location optimization of fresh food e-commerce front warehouse

Dezheng Zhang; Shuai Chen; Na Zhou; Pu Shi; Dezheng Zhang; Shuai Chen; Na Zhou; Pu Shi

doi:10.3934/mbe.2023667

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 8: 14899-14919. doi: 10.3934/mbe.2023667

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Location optimization of fresh food e-commerce front warehouse

School of Management Engineering and Business, Hebei University of Engineering, Handan 056038, China

Academic Editor: Giuseppe Aiello

Received: 22 March 2023 Revised: 08 June 2023 Accepted: 03 July 2023 Published: 11 July 2023

The ongoing emergence of COVID-19 and the maturation of cold chain technology, have aided in the rapid development of the fresh produce e-commerce industry. Taking into account the characteristics of consumers' demand for fresh products, this paper constructs a location allocation model of a front warehouse for fresh e-commerce with the objective of minimizing the total cost. An improved immune optimization algorithm is proposed in this paper, and the effectiveness of the proposed algorithm is demonstrated by a real case study. The results show that the improved immune optimization algorithm outperforms the traditional genetic algorithm in terms of solution accuracy; the proposed location model can effectively help fresh produce e-commerce enterprises open new front-end warehouses when demand is increasing, as well as provide optimal economic decision-making for front warehouse layout.
- front warehouse,
- location-allocation,
- immune algorithm,
- genetic algorithm
Citation: Dezheng Zhang, Shuai Chen, Na Zhou, Pu Shi. Location optimization of fresh food e-commerce front warehouse[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 14899-14919. doi: 10.3934/mbe.2023667

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Abstract

The ongoing emergence of COVID-19 and the maturation of cold chain technology, have aided in the rapid development of the fresh produce e-commerce industry. Taking into account the characteristics of consumers' demand for fresh products, this paper constructs a location allocation model of a front warehouse for fresh e-commerce with the objective of minimizing the total cost. An improved immune optimization algorithm is proposed in this paper, and the effectiveness of the proposed algorithm is demonstrated by a real case study. The results show that the improved immune optimization algorithm outperforms the traditional genetic algorithm in terms of solution accuracy; the proposed location model can effectively help fresh produce e-commerce enterprises open new front-end warehouses when demand is increasing, as well as provide optimal economic decision-making for front warehouse layout.

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