A Multi-Objective Vehicle-Cargo Matching Decision Method Considering Market Supply–Demand Fluctuations and Diverse Stakeholder Interests
Arabian Journal for Science and Engineering, ISSN: 2191-4281
2025
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The vehicle-cargo matching (VCM) sector within the freight industry faces significant challenges, including substantial fluctuations in vehicle-cargo supply and demand quantities and suboptimal matching efficiency. This study addresses these issues by innovatively establishing a multi-objective VCM decision model and segmenting typical business scenarios based on varying vehicle-cargo supply–demand ratios, with objective functions scientifically set according to the diverse requirements of each participant. This model fully considers the characteristics of the VCM system and is solved using appropriate Kuhn–Munkres (an improved Hungarian algorithm) algorithms to ensure efficient and accurate results. Evaluation indicators are also innovatively set from both economic and social benefit perspectives, incorporating managerial assessments and corporate development requirements. The study compares the impacts of different objective functions on VCM outcomes across various scenarios. Results indicate that, in most cases, the platform can achieve maximum profits without explicitly targeting profit maximization, thus accommodating other managerial assessment requirements. Compared to previous single-objective function studies, this approach increases economic efficiency by 22.14% and decreases the empty driving rate by 12.1%. The model is directly applicable to real-world logistics, offering a practical, comprehensive solution for maximizing resources and profitability within the evolving freight industry landscape.
Bibliographic Details
Springer Science and Business Media LLC
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