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Modeling and Solution Algorithm for Optimization Integration of Express Terminal Nodes With a Joint Distribution Mode

Modeling and Solution Algorithm for Optimization Integration of Express Terminal Nodes With a Joint Distribution Mode

Fanchao Meng, Qingran Ji, Hongzhen Zheng, Huihui Wang, Dianhui Chu
Copyright: © 2021 |Volume: 33 |Issue: 4 |Pages: 25
ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9781799859079|DOI: 10.4018/JOEUC.20210701.oa7
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MLA

Meng, Fanchao, et al. "Modeling and Solution Algorithm for Optimization Integration of Express Terminal Nodes With a Joint Distribution Mode." JOEUC vol.33, no.4 2021: pp.142-166. http://doi.org/10.4018/JOEUC.20210701.oa7

APA

Meng, F., Ji, Q., Zheng, H., Wang, H., & Chu, D. (2021). Modeling and Solution Algorithm for Optimization Integration of Express Terminal Nodes With a Joint Distribution Mode. Journal of Organizational and End User Computing (JOEUC), 33(4), 142-166. http://doi.org/10.4018/JOEUC.20210701.oa7

Chicago

Meng, Fanchao, et al. "Modeling and Solution Algorithm for Optimization Integration of Express Terminal Nodes With a Joint Distribution Mode," Journal of Organizational and End User Computing (JOEUC) 33, no.4: 142-166. http://doi.org/10.4018/JOEUC.20210701.oa7

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Abstract

The rapid development of e-commerce has led to increased pressure on the express delivery industry to transport products to customers in a timely manner. The problem of how to deliver an increasing volume of express orders to customer clusters in a timely manner and at low cost with the joint distribution mode is becoming urgent. In this study, an express terminal node optimization and integration model is presented with an option to detach single customer clusters. In addition, the simulated annealing algorithm (SAA) based on neighborhood search that includes four rules is proposed to solve the problem. Contrast experiments are performed with SAA, the immune genetic algorithm (IGA), and the CPLEX solver. The experimental results indicate that IGA is less effective than SAA, and the running time of the IGA is longer. The CPLEX solver is less effective than the SAA, too. Additionally, the experimental results also show that every neighborhood rule proposed in this study plays a role in the optimization process.