A combined intelligent and game theoretical methodology for collaborative multicenter pickup and delivery problems with time window assignment

Wang, Yong; Wang, Xiuwen; Guan, Xiangyang; Li, Qin; Fan, Jintai; Wang, Haizhong

doi:10.1016/j.asoc.2021.107875

Cited by 16 publications

(8 citation statements)

References 72 publications

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“…Different strategies, such as TWA and VS, have been developed to improve the operational efficiency of logistics networks ( Cattaruzza et al, 2014 , Spliet and Gabor, 2015 , Subramanyam et al, 2018 , Zhen et al, 2020 ; Wang et al, 2021c; Marques et al, 2022 ). In addition, disasters have occurred frequently in recent years, so studies have focused on improving the response speed and reducing the losses in emergency logistics networks (Mohammadi et al, 2020; Wang et al, 2021 , Wolfinger et al, 2021 , Gentili et al, 2022 ). Several studies related to 2E-EVRPTWA in MDVRPTW, 2E-VRPTW, TWA, VS, and EVRP are reviewed.…”

Section: Literature Reviewmentioning

confidence: 99%

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Emergency logistics network optimization with time window assignment

Wang

Fan

et al. 2023

Expert Systems with Applications

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Section: Literature Reviewmentioning

confidence: 99%

“…The relevant parameters adopted in the computational experiment are from Li’s and Wang’s studies ( Li et al, 2020 , Wang et al, 2021 ) and are shown in Tables 12 . The time windows of DCs and satellites for customers are distributed within [0,720].…”

Section: Empirical Analysesmentioning

confidence: 99%

Emergency logistics network optimization with time window assignment

Wang

Fan

et al. 2023

Expert Systems with Applications

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“…In recent years, the concepts of collaboration and sharing have been introduced in many studies to realize the efficient configuration of resources, thereby promoting cost and resource savings through multidepot collaboration [48][49][50]. Many third-party platforms are created to provide sharing services, which promote various collaboration modes [51,52].…”

Section: Cmvrpdcdtwmentioning

confidence: 99%

Collaborative Multidepot Vehicle Routing Problem with Dynamic Customer Demands and Time Windows

et al. 2022

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Dynamic customer demands impose new challenges for vehicle routing optimization with time windows, in which customer demands appear dynamically within the working periods of depots. The delivery routes should be adjusted for the new customer demands as soon as possible when new customer demands emerge. This study investigates a collaborative multidepot vehicle routing problem with dynamic customer demands and time windows (CMVRPDCDTW) by considering resource sharing and dynamic customer demands. Resource sharing of multidepot across multiple service periods can maximize logistics resource utilization and improve the operating efficiency of delivery logistics networks. A bi-objective optimization model is constructed to optimize the vehicle routes while minimizing the total operating cost and number of vehicles. A hybrid algorithm composed of the improved k-medoids clustering algorithm and improved multiobjective particle swarm optimization based on the dynamic insertion strategy (IMOPSO-DIS) algorithm is designed to find near-optimal solutions for the proposed problem. The improved k-medoids clustering algorithm assigns customers to depots in terms of specific distances to obtain the clustering units, whereas the IMOPSO-DIS algorithm optimizes vehicle routes for each clustering unit by updating the external archive. The elite learning strategy and dynamic insertion strategy are applied to maintain the diversity of the swarm and enhance the search ability in the dynamic environment. The experiment results with 26 instances show that the performance of IMOPSO-DIS is superior to the performance of multiobjective particle swarm optimization, nondominated sorting genetic algorithm-II, and multiobjective evolutionary algorithm. A case study in Chongqing City, China is implemented, and the related results are analyzed. This study provides efficient optimization strategies to solve CMVRPDCDTW. The results reveal a 32.5% reduction in total operating costs and savings of 29 delivery vehicles after optimization. It can also improve the intelligence level of the distribution logistics network, promote the sustainable development of urban logistics and transportation systems, and has meaningful implications for enterprises and government to provide theoretical and decision supports in economic and social development.

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“…This requires behavior-centric policies to incentivize consumers to reduce excessive consumption, and stimulate reusing, repurposing, and recycling of goods as much as possible. Collaboration within and across the industry 80,81 , as well as enhanced connectivity and data analytics could facilitate improvements in logistics through load consolidation and route, schedule, and network optimizations 82,83 . The availability of a wider rail infrastructure and waterways network could support a modal shift towards a lower carbon freight alternative 84,85 .…”

Section: Hauling Towards Zeromentioning

confidence: 99%