Reinforcement Learning Model for Optimizing Bid Price and Service Quality in Crowdshipping
Crowdshipping establishes a short-term connection between shippers and individual carriers, bridging the service requirements in last-mile logistics. From the perspective of a carrier operating multiple vehicles, this study considers the challenge of maximizing profits by optimizing bid strategies f...
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MDPI AG
2025-06-01
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| Series: | Systems |
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| author | Daiki Min Seokgi Lee Yuncheol Kang |
| author_facet | Daiki Min Seokgi Lee Yuncheol Kang |
| author_sort | Daiki Min |
| collection | DOAJ |
| description | Crowdshipping establishes a short-term connection between shippers and individual carriers, bridging the service requirements in last-mile logistics. From the perspective of a carrier operating multiple vehicles, this study considers the challenge of maximizing profits by optimizing bid strategies for delivery prices and transportation conditions in the context of bid-based crowdshipping services. We considered two types of bid strategies: a price bid that adjusts the RFQ freight charge and a multi-attribute bid that scores both price and service quality. We formulated the problem as a Markov decision process (MDP) to represent uncertain and sequential decision-making procedures. Furthermore, given the complexity of the newly proposed problem, which involves multiple vehicles, route optimizations, and multiple attributes of bids, we employed a reinforcement learning (RL) approach that learns an optimal bid strategy. Finally, numerical experiments are conducted to illustrate the superiority of the bid strategy learned by RL and to analyze the behavior of the bid strategy. A numerical analysis shows that the bid strategies learned by RL provide more rewards and lower costs than other benchmark strategies. In addition, a comparison of price-based and multi-attribute strategies reveals that the choice of appropriate strategies is situation-dependent. |
| format | Article |
| id | doaj-art-d4eb4a53b59c4f0e92edd5397110df76 |
| institution | Kabale University |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-d4eb4a53b59c4f0e92edd5397110df762025-08-20T03:29:49ZengMDPI AGSystems2079-89542025-06-0113644010.3390/systems13060440Reinforcement Learning Model for Optimizing Bid Price and Service Quality in CrowdshippingDaiki Min0Seokgi Lee1Yuncheol Kang2School of Business, Ewha Womans University, Seoul 03760, Republic of KoreaRayen School of Engineering, Youngstown State University, Youngstown, OH 44555, USASchool of Business, Ewha Womans University, Seoul 03760, Republic of KoreaCrowdshipping establishes a short-term connection between shippers and individual carriers, bridging the service requirements in last-mile logistics. From the perspective of a carrier operating multiple vehicles, this study considers the challenge of maximizing profits by optimizing bid strategies for delivery prices and transportation conditions in the context of bid-based crowdshipping services. We considered two types of bid strategies: a price bid that adjusts the RFQ freight charge and a multi-attribute bid that scores both price and service quality. We formulated the problem as a Markov decision process (MDP) to represent uncertain and sequential decision-making procedures. Furthermore, given the complexity of the newly proposed problem, which involves multiple vehicles, route optimizations, and multiple attributes of bids, we employed a reinforcement learning (RL) approach that learns an optimal bid strategy. Finally, numerical experiments are conducted to illustrate the superiority of the bid strategy learned by RL and to analyze the behavior of the bid strategy. A numerical analysis shows that the bid strategies learned by RL provide more rewards and lower costs than other benchmark strategies. In addition, a comparison of price-based and multi-attribute strategies reveals that the choice of appropriate strategies is situation-dependent.https://www.mdpi.com/2079-8954/13/6/440reinforcement learningcrowdshippingbidding strategymulti-attribute strategy |
| spellingShingle | Daiki Min Seokgi Lee Yuncheol Kang Reinforcement Learning Model for Optimizing Bid Price and Service Quality in Crowdshipping Systems reinforcement learning crowdshipping bidding strategy multi-attribute strategy |
| title | Reinforcement Learning Model for Optimizing Bid Price and Service Quality in Crowdshipping |
| title_full | Reinforcement Learning Model for Optimizing Bid Price and Service Quality in Crowdshipping |
| title_fullStr | Reinforcement Learning Model for Optimizing Bid Price and Service Quality in Crowdshipping |
| title_full_unstemmed | Reinforcement Learning Model for Optimizing Bid Price and Service Quality in Crowdshipping |
| title_short | Reinforcement Learning Model for Optimizing Bid Price and Service Quality in Crowdshipping |
| title_sort | reinforcement learning model for optimizing bid price and service quality in crowdshipping |
| topic | reinforcement learning crowdshipping bidding strategy multi-attribute strategy |
| url | https://www.mdpi.com/2079-8954/13/6/440 |
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