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Collaborative distribution optimization model and algorithm for an intelligent supply chain based on green computing energy management

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Abstract

Collaborative distribution is the core of modern logistics, and the collaborative distribution centre is the physical location of distribution. This article aims to study the use of green computing energy management to promote a collaborative distribution optimization model and algorithm for an intelligent supply chain. A multiobjective genetic algorithm for energy management using green computing and a multiobjective hybrid genetic algorithm based on parallel selection methods are designed and implemented. A joint optimization model of VRP & VFP for logistics distribution is established. Collaborative system design and collaborative system operation inventory control issues are integrated. Considering uncertain demand, a multiobjective mixed-integer programming model of energy management using green computing is established to solve this problem. Experimental research shows that the optimal solution is found before the optimal operation of the 24th-generation collaborative system. The designed functional value of the collaborative system is 66109, and the optimal operating value of the collaborative system is 57348.

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Acknowledgements

This work was supported by the National Social Science Fund of P.R. China (17BGL211), the fund of Humanities and Social Sciences of Ministry of Education of P.R. China (17YJAZH074).

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Authors and Affiliations

  1. School of Management, Hubei University of Education, Wuhan, 430205, Hubei, China

    Lu Cai, Zhongming Tang & Aijun Liu

  2. School of Economics, Management & Low, Hubei Normal University, Huangshi, 435002, Hubei, China

    Yongcai Yan

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  1. Lu Cai
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  2. Yongcai Yan
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Correspondence to Yongcai Yan.

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Cai, L., Yan, Y., Tang, Z. et al. Collaborative distribution optimization model and algorithm for an intelligent supply chain based on green computing energy management. Computing (2021). https://doi.org/10.1007/s00607-021-00972-4

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  • DOI: https://doi.org/10.1007/s00607-021-00972-4

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