Proactive Two-Level Dynamic Distribution Routing Optimization Based on Historical Data

Ge, Xianlong; Xue, Guiqin; Wen, Pengzhe

doi:10.1155/2018/5191637

Cited by 7 publications

(6 citation statements)

References 33 publications

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“…Min et al [42] proposed the maximum-minimum distance clustering method to cluster customers in split-delivery VRP for the better performance of the algorithm. Ge et al [43] added the service radius and load expansion factors to the clustering algorithm to avoid the vehicle overloading in MDVRPTW. Fan et al [44] introduced a clustering algorithm based on the temporal-spatial distance to reduce the computational complexity and enhance the quality of initial solution in MDVRPTW.…”

Section: Literature Reviewmentioning

confidence: 99%

Multidepot Recycling Vehicle Routing Problem with Resource Sharing and Time Window Assignment

Wang

Guan

et al. 2021

Journal of Advanced Transportation

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This study aims to provide tactical and operational decisions in multidepot recycling logistics networks with consideration of resource sharing (RS) and time window assignment (TWA) strategies. The RS strategy contributes to efficient resource allocation and utilization among recycling centers (RCs). The TWA strategy involves assigning time windows to customers to enhance the operational efficiency of logistics networks. A biobjective mathematical model is established to minimize the total operating cost and number of vehicles for solving the multidepot recycling vehicle routing problem with RS and TWA (MRVRPRSTWA). A hybrid heuristic algorithm including 3D k-means clustering algorithm and nondominated sorting genetic algorithm- (NSGA-) II (NSGA-II) is designed. The 3D k-means clustering algorithm groups customers into clusters on the basis of their spatial and temporal distances to reduce the computational complexity in optimizing the multidepot logistics networks. In comparison with NSGA algorithm, the NSGA-II algorithm incorporates an elitist strategy, which can improve the computational speed and robustness. In this study, the performance of the NSGA-II algorithm is compared with the other two algorithms. Results show that the proposed algorithm is superior in solving MRVRPRSTWA. The proposed model and algorithm are applied to an empirical case study in Chongqing City, China, to test their applicability in real logistics operations. Four different scenarios regarding whether the RS and TWA strategies are included or not are developed to test the efficacy of the proposed methods. The results indicate that the RS and TWA strategies can optimize the recycling services and resource allocation and utilization and enhance the operational efficiency, thus promoting the sustainable development of the logistics industry.

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Section: Literature Reviewmentioning

confidence: 99%

Multidepot Recycling Vehicle Routing Problem with Resource Sharing and Time Window Assignment

Wang

Guan

et al. 2021

Journal of Advanced Transportation

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“…In addition, the genetic algorithm was used to solve the model. Ge et al [15] studied the data-driven proactive vehicle routing optimization based on the historical distribution data of logistic companies, and four models were established for predicting customer demands, customer clustering, proactive demand quotas, and replenishment strategies.…”

Section: Literature Reviewmentioning

confidence: 99%

Electric Vehicle Routing Problems with Stochastic Demands and Dynamic Remedial Measures

Zhu

Jin

2020

Mathematical Problems in Engineering

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With the rapid development of e-commerce, logistic enterprises must better predict customer demand to improve distribution efficiency, so as to deliver goods in advance, which makes logistics stochastic and dynamic. In order to deal with this challenge and respond to the concept of “green logistics,” an electric vehicle routing problem with stochastic demands (EVRPSD) and proactive remedial measures is investigated, and an EVRPSD model with probability constraints is established. At the same time, a hybrid heuristic algorithm, combining a saving method and an improved Tabu search algorithm, is proposed to solve the model. Moreover, two insertion strategies with the greedy algorithm for charging stations and dynamic nodes are introduced. Finally, a large number of experimental data show that the heuristic algorithm proposed in this paper is feasible and effective.

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“…Swarm intelligent optimization algorithms are meta-heuristic algorithms based on the intelligence of groups of organisms in nature, which can solve complex multi-dimensional optimization problems [22]. Swarm intelligent optimization algorithms are represented by ant colony algorithm, particle swarm algorithm, and genetic algorithm, which are widely used in many fields such as neural networks, electric power, the chemical industry, image recognition, and so on [23][24][25]. With the continuous enrichment and development of related research, many new intelligent optimization algorithms such as the cuckoo algorithm, swarm algorithm, firefly algorithm, etc., among which the swarm algorithm is simple in structure, flexible, efficient, and good in performance, which has been applied by many types of research to solve the optimization problems in many fields, and proved its effectiveness and superiority [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Simulation Research on Logistics Distribution Path Based on Colony Intelligence Optimisation

Liu,

2024

Applied Mathematics and Nonlinear Sciences

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In light of the growing emphasis on “logistics integration,” this paper contends that the logistics network system requires comprehensive integration and optimization. We propose the use of the Bacterial Foraging Optimization (BFO) algorithm to enhance the Vehicle Routing Problem (VRP). The penalty function method is employed to direct the BFO algorithm in identifying the optimal vehicle path encoding and to improve the convergence operations for logistics distribution. This study analyzes the fundamental characteristic elements of the VRP, introduces a basic model for the VRP, and develops a logistics distribution path optimization model that minimizes the total cost by thoroughly considering the expenses associated with cold chain low-carbon logistics distribution. A simulation environment is established, and algorithm parameters are set to determine the optimal solutions for both the BFO and the Improved BFO (IBFO) algorithms in the test function. An example is selected for practical application, wherein the design of the distribution center and points is carried out, and the colony intelligence optimization algorithm is applied to simulate the logistics and distribution paths. Combined with the specific case of cold chain distribution of food, the optimization of the cold chain logistics distribution path is solved. In the case study, when the carbon penalty coefficient is ε = 15, the optimal logistics and distribution site selection program is Guangxi and Guizhou, and the total cost of the supply chain is 16,363,365.92 yuan. At this time, the supply chain cost is optimal. Compared with the carbon penalty coefficient ε = 20, the economic cost increased by 70152.24 yuan, but the carbon emission decreased by 1890.9 kg. The optimization of logistics and distribution processes encourages enterprises to develop strategies that promote low carbon and environmental protection and achieve better economic and ecological outcomes.

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Proactive Two-Level Dynamic Distribution Routing Optimization Based on Historical Data

Cited by 7 publications

References 33 publications

Multidepot Recycling Vehicle Routing Problem with Resource Sharing and Time Window Assignment

Multidepot Recycling Vehicle Routing Problem with Resource Sharing and Time Window Assignment

Electric Vehicle Routing Problems with Stochastic Demands and Dynamic Remedial Measures

Simulation Research on Logistics Distribution Path Based on Colony Intelligence Optimisation

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