Flow Merging and Hub Route Optimization in Collaborative Transportation | Zendy

Kerui Weng | Zendy; Zihao Xu | Zendy

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Flow Merging and Hub Route Optimization in Collaborative Transportation

Author(s) -

Kerui Weng,

Zihao Xu

Publication year - 2014

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2014/621487

Subject(s) - lagrangian relaxation , mathematical optimization , computer science , integer programming , routing (electronic design automation) , heuristic , vehicle routing problem , decomposition , benders' decomposition , relaxation (psychology) , flow network , operations research , flow (mathematics) , work (physics) , transportation theory , destinations , mathematics , computer network , engineering , tourism , mechanical engineering , psychology , ecology , social psychology , geometry , biology , law , political science

This paper studies the optimal hub routing problem of merged tasks in collaborative transportation. This problem allows all carriers’ transportation tasks to reach the destinations optionally passing through 0, 1, or 2 hubs within limited distance, while a cost discount on arcs in the hub route could be acquired after paying fixed charges. The problem arises in the application of logistics, postal services, airline transportation, and so forth. We formulate the problem as a mixed-integer programming model, and provide two heuristic approaches, respectively, based on Lagrangian relaxation and Benders decomposition. Computational experiments show that the algorithms work well

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