A multi-objective dynamic vehicle routing optimization for fresh product distribution: A case study of Shenzhen

Wenjie Wang; Suzhen Wen; Shen Gao; Pengyi Lin; Wenjie Wang; Suzhen Wen; Shen Gao; Pengyi Lin

doi:10.3934/era.2024132

Electronic Research Archive

2024, Volume 32, Issue 4: 2897-2920. doi: 10.3934/era.2024132

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A multi-objective dynamic vehicle routing optimization for fresh product distribution: A case study of Shenzhen

1.
Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China
2.
Meizhou Power Supply Bureau, Guangdong Power Grid Corporation, Meizhou 514021, China

Received: 31 December 2023 Revised: 25 March 2024 Accepted: 07 April 2024 Published: 15 April 2024

To improve the fast and efficient distribution of fresh products with dynamic customer orders, we constructed a multi-objective vehicle routing optimization model with the objectives of minimizing the distribution costs including freshness-loss cost, cold-chain-refrigeration cost, and delay-penalty cost, and maximizing customer time satisfaction. An improved multi-objective genetic algorithm (GA)-based particle swarm optimization (MOGAPSO) algorithm was designed to quickly solve the optimal solution for the distribution routes for fresh-product orders from regular customers. Furthermore, online real-time orders of fresh products were periodically inserted into the distribution routes with local optimization solutions by applying a dynamic inserting algorithm. Finally, a case study of a fresh-product distribution company in Shenzhen, China was conducted to validate the practicality of the proposed model and algorithms. A comparison with the NSGA-Ⅱ and MOPSO algorithms showed the superiority of the proposed MOGAPSO algorithm on distribution-cost reduction and customer time-satisfaction improvement. Moreover, the dynamic inserting algorithm demonstrated a better performance on distribution-cost reduction.
- fresh-product distribution,
- multi-objective vehicle routing optimization,
- MOGAPSO algorithm,
- dynamic inserting algorithm
Citation: Wenjie Wang, Suzhen Wen, Shen Gao, Pengyi Lin. A multi-objective dynamic vehicle routing optimization for fresh product distribution: A case study of Shenzhen[J]. Electronic Research Archive, 2024, 32(4): 2897-2920. doi: 10.3934/era.2024132

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Abstract

To improve the fast and efficient distribution of fresh products with dynamic customer orders, we constructed a multi-objective vehicle routing optimization model with the objectives of minimizing the distribution costs including freshness-loss cost, cold-chain-refrigeration cost, and delay-penalty cost, and maximizing customer time satisfaction. An improved multi-objective genetic algorithm (GA)-based particle swarm optimization (MOGAPSO) algorithm was designed to quickly solve the optimal solution for the distribution routes for fresh-product orders from regular customers. Furthermore, online real-time orders of fresh products were periodically inserted into the distribution routes with local optimization solutions by applying a dynamic inserting algorithm. Finally, a case study of a fresh-product distribution company in Shenzhen, China was conducted to validate the practicality of the proposed model and algorithms. A comparison with the NSGA-Ⅱ and MOPSO algorithms showed the superiority of the proposed MOGAPSO algorithm on distribution-cost reduction and customer time-satisfaction improvement. Moreover, the dynamic inserting algorithm demonstrated a better performance on distribution-cost reduction.

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