A multi-center joint distribution optimization model considering carbon emissions and customer satisfaction

Xiangyang Ren; Xinxin Jiang; Liyuan Ren; Lu Meng; Xiangyang Ren; Xinxin Jiang; Liyuan Ren; Lu Meng

doi:10.3934/mbe.2023031

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 1: 683-706. doi: 10.3934/mbe.2023031

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A multi-center joint distribution optimization model considering carbon emissions and customer satisfaction

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

Academic Editor: Rekha Guchhait

Received: 14 June 2022 Revised: 01 October 2022 Accepted: 08 October 2022 Published: 13 October 2022

Logistics enterprises are searching for a sustainable solution between the economy and the environment under the concept of green logistics development. Given that, this study integrates carbon emission as one of the costs into the vehicle routing problem with time window (VRPTW) and establishes a multi-center joint distribution optimization model taking into account distribution cost, carbon emission, and customer satisfaction. In the study of carbon emissions, this paper selected the vehicle load rate and vehicle distance as the main indicators. An improved ant colony algorithm is designed to solve the model by introducing the elite strategy, the saving strategy, vehicle service rules, and customer selection rules. Simulation results show that compared with the traditional ant colony optimization and genetic algorithm, the improved ant colony algorithm can effectively reduce the distribution cost and carbon emission and, improve customer satisfaction.
- green logistics,
- joint distribution,
- path optimization,
- time window,
- improved ant colony optimization
Citation: Xiangyang Ren, Xinxin Jiang, Liyuan Ren, Lu Meng. A multi-center joint distribution optimization model considering carbon emissions and customer satisfaction[J]. Mathematical Biosciences and Engineering, 2023, 20(1): 683-706. doi: 10.3934/mbe.2023031

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Abstract

Logistics enterprises are searching for a sustainable solution between the economy and the environment under the concept of green logistics development. Given that, this study integrates carbon emission as one of the costs into the vehicle routing problem with time window (VRPTW) and establishes a multi-center joint distribution optimization model taking into account distribution cost, carbon emission, and customer satisfaction. In the study of carbon emissions, this paper selected the vehicle load rate and vehicle distance as the main indicators. An improved ant colony algorithm is designed to solve the model by introducing the elite strategy, the saving strategy, vehicle service rules, and customer selection rules. Simulation results show that compared with the traditional ant colony optimization and genetic algorithm, the improved ant colony algorithm can effectively reduce the distribution cost and carbon emission and, improve customer satisfaction.

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