Journal ArticleDOI
Collaborative multi-depot pickup and delivery vehicle routing problem with split loads and time windows
TLDR
In this article, a 3D customer clustering algorithm with split load strategies is developed to reassign each customer to its favorable service provider considering multiple customer service characteristics, and a hybrid genetic algorithm with tabu search is designed to optimize the pickup and delivery routes and maximize the logistics resource utilization.Abstract:
Optimization of collaborative multi-depot pickup and delivery logistics networks (CMDPDLN) with split loads and time windows involves a customer demand splitting strategy and a multi-depot pickup and delivery vehicle routing problem under time window constraints. In the collaborative network, the customer demand splitting scheme based on customer clustering aims to achieve the balance of demands’ spatial distribution and improve the efficiency of logistics transportation. The multi-depot pickup and delivery vehicle routing problem focuses on establishing a collaborative network optimization model to coordinate the pickup and delivery services among multiple depots and determine the optimal routes with reduced operating cost through logistics resource sharing. A 3D customer clustering algorithm with split load strategies is developed to reassign each customer to its favorable service provider considering multiple customer service characteristics. A hybrid genetic algorithm with tabu search is designed to optimize the pickup and delivery routes and maximize the logistics resource utilization. A realistic logistics network in Chongqing, China is used to test the performance of the proposed solution methods for the CMDPDLN optimization. Computational results show the effectiveness of customer clustering and demand splitting in simplifying and improving the large-scale collaborative network, and the adaptability of the hybrid algorithm in finding the minimal-cost vehicle routes. Therefore, the collaboration and demand split strategy adopted in network optimization can provide a reference for logistics operational management and facilitate sustainable pickup and delivery networks.read more
Citations
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Branch-price-and-cut algorithms for the pickup and delivery problem with time windows and multiple stacks
Marilène Cherkesly,Marilène Cherkesly,Guy Desaulniers,Guy Desaulniers,Stefan Irnich,Gilbert Laporte +5 more
TL;DR: Computational results obtained on instances derived from benchmark instances for the pickup and delivery traveling salesman problem with multiple stacks are reported, and the advantage of incorporating the multi-stack policy in the pricing problem is revealed.
Journal ArticleDOI
The Vehicle Routing Problem: State-of-the-Art Classification and Review
Shi-Yi Tan,Wei-Chang Yeh +1 more
TL;DR: In this article, a taxonomic framework for vehicle routing problems (VRPs) and their variants is presented. And a classification table is provided to provide readers with recent trends and solution methodologies in the field of VRPs and some well-known variants.
Journal ArticleDOI
A clustering-based extended genetic algorithm for the multidepot vehicle routing problem with time windows and three-dimensional loading constraints
Journal ArticleDOI
An augmented Lagrangian relaxation method for the mean-standard deviation based vehicle routing problem
Mao-Can Song,L. Cheng +1 more
TL;DR: In this article , the authors developed a novel augmented Lagrangian relaxation approach for the problem of vehicle routing optimization in traffic congestions, where the travel times of multiple days were used to obtain the travel time means and standard deviations of links from these data.
Journal ArticleDOI
An Investigation of Multimodal Transport for Last Mile Delivery in Rural Areas
TL;DR: In this paper , a multimodal transport design for last mile delivery in rural areas is proposed, based on three typical transport modes (local logistics providers, public transport, and crowdsourcing logistics), which uses genetic algorithms (GA) to solve the logistical problems faced.
References
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Finding optimal solutions for vehicle routing problem with pickup and delivery services with time windows: A dynamic programming approach based on state–space–time network representations
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