Multi-task transportation scheduling model with backhauls based on hub and spoke in collaborative logistics network (original) (raw)

Abstract

Hub and spoke collaborative logistics network has recently received considerable attention in terms of resource scheduling problem. In view of that problem such as low scale merit, inefficient resource utilization and conflict of interest between the operator and customer in the process of network operation, the paper seeks to formulate the scheduling strategy for multiple logistics tasks oriented to multi-origin and multi-destination on the basis of multiple-allocation-hybrid H&S collaborative network (including two layers). Taking the backhaul resource, resource departure time, capability limitation, task delay penalty, etc. into account, the aim of the problem is to minimize both total cost and total time for the task set. According to the characteristics of the problem, the immune genetic algorithm based on three-layer encoding mechanism is designed to solve the model. Moreover, the models and algorithms are validated by extensive results of numerical examples.

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Acknowledgements

This work was supported by the Fundamental Research Funds for the Central Universities, China (Grant no. 17CX04023B).

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Authors and Affiliations

1. School of Economic Management, China University of Petroleum, Qingdao, 266580, China
   Xiaofeng Xu & Yuping Sun
2. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
   Jue Wang

Authors

1. Xiaofeng Xu
2. Yuping Sun
3. Jue Wang

Corresponding author

Correspondence toXiaofeng Xu.

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Xu, X., Sun, Y. & Wang, J. Multi-task transportation scheduling model with backhauls based on hub and spoke in collaborative logistics network.J Ambient Intell Human Comput 10, 333–343 (2019). https://doi.org/10.1007/s12652-018-0706-6

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• Received: 31 December 2017
• Accepted: 08 February 2018
• Published: 23 February 2018
• Version of record: 23 February 2018
• Issue date: 29 January 2019
• DOI: https://doi.org/10.1007/s12652-018-0706-6

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