Multi-depot vehicle routing problem with time windows under shared depot resources

Li, Jian; Li, Yang; Pardalos, Pãnos M.

doi:10.1007/s10878-014-9767-4

Cited by 68 publications

(29 citation statements)

References 26 publications

(30 reference statements)

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“…e time window of RC1 is [6,18]. RC2 and RC3 have the same Nondominated Sorting Algorithm-II (NSGA-II) Input: pop size , nodes, R, Rmax, Nmax, sp, cp, and mp Output: Pareto front optimal solutions (1) Initialize parameters (2) # set the population size (pop size ), number of customers (nodes), number of generation (R), maximum number of generations (Rmax), maximum number of runs (Nmax), selection probability (sp), crossover probability (cp), and mutation probability (mp) (3) For N � 1:Nmax (4) For R � 1:Rmax (5) Generate the initial population with size pop size (6) Objective function evaluation (7) # compute the objective function to minimize the total operating cost and TNV (8) Divide pop size into nondominance front and calculate the crowding distance of each individual (9) For i � 1: R (10)…”

Section: Relevant Parameter Settingmentioning

confidence: 99%

“…e low transportation resource utilization and irrational transport operations of logistics facilities still appear and need to be addressed [7,8]. Resource sharing (RS) strategies have been proposed to optimize resource utilization and reduce operating cost in multidepot logistics networks [9]. Customers are reassigned to appropriate logistics facilities through these strategies to avoid unreasonable routing via customer information sharing [10].…”

Section: Introductionmentioning

confidence: 99%

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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: Relevant Parameter Settingmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Liu et al [24] established a mixed integer programming model aiming at minimizing the cost for the MDVRP with time windows based on vehicle leasing and sharing, transforming the problem into a single depot vehicle routing problem by introducing a virtual distribution depot, and proposed a hybrid genetic algorithm. Li et al [25] considered the constraints of time window, vehicle capacity, and travel time establishing an integer programming model with the minimum total travel cost, and proposed a hybrid genetic algorithm with adaptive local search to solve it. Fan et al [6] proposed a half-open MDVRP based on joint distribution mode of fresh food, considering the timeliness requirements of fresh products transportation, and constructed an optimization model aiming at minimizing the total distribution cost, and designed an ant colony algorithm to solve the optimization model.…”

Section: Literature Reviewmentioning

confidence: 99%

Time-Dependent Multi-Depot Heterogeneous Vehicle Routing Problem Considering Temporal–Spatial Distance

et al. 2021

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Aiming at the multi-depot heterogeneous vehicle routing problem under the time-dependent road network and soft time window, considering vehicle fixed cost, time window penalty cost and vehicle transportation cost, an optimization model of time-dependent multi-depot heterogeneous vehicle routing problem is established with the objective of minimizing distribution cost. According to the characteristics of the problem, a hybrid genetic algorithm with variable neighborhood search considering the temporal–spatial distance is designed. Customers are clustered according to the temporal–spatial distance to generate initial solutions, which improves the quality of the algorithm. The depth search capability of the variable neighborhood search algorithm is applied to the local search strategy of the genetic algorithm to enhance the local search capability of the algorithm. An adaptive neighborhood search number strategy and a new acceptance mechanism of simulated annealing are proposed to balance the breadth and depth required for population evolution. The validity of the model and algorithm is verified by several sets of examples of different scales. The research results not only deepen and expand the relevant research on vehicle routing problem, but also provide theoretical basis for logistics enterprises to optimize distribution scheme.

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“…Given that delivery should meet the time window, Afshar-Nadjafi [42] established a mixed integer-programming model and proposed a constructive heuristic algorithm to solve the MDVRPTW model, which aimed to minimize the total cost of heterogeneous fleets. Li et al [43] formulated an integer programming model and proposed a hybrid genetic algorithm with adaptive local search to study the multi-depot vehicle routing problem with time windows. Naccache et al [44] established a model based on multi-pickup and delivery problem under time window constraints, and developed a hybrid adaptive large neighborhood search to solve this problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Collaborative Mechanism for Pickup and Delivery Problems with Heterogeneous Vehicles under Time Windows

et al. 2019

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The sustainability and complexity of logistics networks come from the temporally and spatially uneven distributions of freight demand and supply. Operation strategies without considering the sustainability and complexity could dramatically increase the economic and environmental costs of logistics operations. This paper explores how the unevenly distributed demand and supply can be optimally matched through collaborations, and formulates and solves a Collaborative Pickup and Delivery Problem under Time Windows (CPDPTW) to optimize the structures of logistics networks and improve city sustainability and liverability. The CPDPTW is a three-stage framework. First, a multi-objective linear optimization model that minimizes the number of vehicles and the total cost of logistics operation is developed. Second, a composite algorithm consisting of improved k-means clustering, Demand-and-Time-based Dijkstra Algorithm (DTDA) and Improved Non-dominated Sorting Genetic Algorithm-II (INSGA-II) is devised to solve the optimization model. The clustering algorithm helps to identify the feasible initial solution to INSGA-II. Third, a method based on improved Shapley value model is proposed to obtain the collaborative alliance strategy that achieves the optimal profit allocation strategy. The proposed composite algorithm outperforms existing algorithms in minimizing terms of the total cost and number of electro-tricycles. An empirical case of Chongqing is employed to demonstrate the efficiency of the proposed mechanism for achieving optimality for logistics networks and realizing a win-win situation between suppliers and consumers.

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Multi-depot vehicle routing problem with time windows under shared depot resources

Cited by 68 publications

References 26 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

Time-Dependent Multi-Depot Heterogeneous Vehicle Routing Problem Considering Temporal–Spatial Distance

Collaborative Mechanism for Pickup and Delivery Problems with Heterogeneous Vehicles under Time Windows

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