An Effective Two-Stage Algorithm for the Bid Generation Problem in the Transportation Service Market

Shiying Liu, Fang Yang, Tailin Liu and Mengli Li ()
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Shiying Liu: School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Fang Yang: Key Laboratory of Big Data Intelligent Computing, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Tailin Liu: School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Mengli Li: School of Modern Posts, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Mathematics, 2024, vol. 12, issue 7, 1-12

Abstract: This study designs a two-stage algorithm to address the bid generation problem of carriers when adding new vehicle routes in the presence of the existing vehicle routes to provide transportation service. To obtain the best auction combination and bid price of the carrier, a hybrid integer nonlinear programming model is introduced. According to the characteristics of the problem, a set of two-stage hybrid algorithms is proposed, innovatively integrating block coding within a genetic algorithm framework with a depth-first search approach. This integration effectively manages routing constraints, enhancing the algorithm’s efficiency. The block coding and each route serve as decision variables in the set partition formula, enabling a comprehensive exploration of potential solutions. After a simulation-based analysis, the algorithm has been comprehensively validated analytically and empirically. The improvement of this research lies in the effectiveness of the proposed algorithm, i.e., the ability to handle a broader range of problem scales with less time in addressing complex operator bid generation in combinatorial auctions.

Keywords: vehicle routing problem; hybrid integer nonlinear programming model; two-stage algorithm; depth-first search (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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