Product-oriented time window assignment for a multi-compartment vehicle routing problem

Martins, Sara; Ostermeier, Manuel; Amorim, Pedro; Hübner, Alexander; Almada-Lobo, Bernardo

doi:10.1016/j.ejor.2019.01.053

Cited by 47 publications

(17 citation statements)

References 43 publications

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“…Constraint (18) ensures that the total demand of customers served by a logistics facility should be beyond its capacity. Constraints (19) and (20) guarantee the continuous departure time of vehicles at logistics facilities. Constraints ( 21) and ( 22) guarantee the continuous arrival time of vehicles at customers.…”

Section: Decision Variables X Ijvmentioning

confidence: 99%

“…Implement selection, PMC, and mutation operations to generate offspring population (11) Combine parent and offspring population, perform nondominated sorting and calculate the crowding distance of each individual (12) Build the new population on the basis of the parent and offspring populations following partial order ( 13) end ( 14) end (15) For n � 1: nodes (16) Select the customers that accept time window and have time window violation (17) Select the appropriate time window from candidate time windows to assign to customer (18) Calculate the TWA cost and compare it with the penalty cost (19) Assign time window [8,20]. e time window of RC4 and RC5 is the same as [10,22].…”

Section: Relevant Parameter Settingmentioning

confidence: 99%

“…Most logistics service providers try to recycle cargoes within the predetermined time windows in logistics operations to reduce the penalty cost [17]. However, the vehicles cannot provide recycling services for customers in the expected time windows due to long transportation distance and unreasonable service time windows for logistics facilities [18,19]. erefore, a set of candidate time windows are assigned to customers in this study, and recycling vehicle routes are designed on the basis of time window assignment (TWA) to optimize the operational efficiency and minimize the operating cost in multidepot RVRP with RS and TWA (MRVRPRSTWA).…”

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: Decision Variables X Ijvmentioning

confidence: 99%

Section: Relevant Parameter Settingmentioning

confidence: 99%

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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“…e means of a fix-and-optimize-based math-heuristic method are used to solve this two-stage stochastic optimization problem. Martins et al [26] extended research on MCVRPS by tackling a multiperiod setting with a product-oriented TWA and proposed an adaptive large neighborhood search as the solution. Wang et al [27] established a biobjective programming model that optimizes the total operating routing cost and the total number of delivery vehicles for a collaborative multidepot VRP with TWA (CMDVRPTWA).…”

Section: Literature Reviewmentioning

confidence: 99%

Collaborative Multidepot Petrol Station Replenishment Problem with Multicompartments and Time Window Assignment

Wang

et al. 2020

Journal of Advanced Transportation

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Energy supply is an important system that affects the overall efficiency of urban transportation. To improve the system operational efficiency and reduce costs, we formulate and solve a collaborative multidepot petrol station replenishment problem with multicompartments and time window assignment by establishing a mixed-integer linear programming model. The hybrid heuristic algorithm composed of genetic algorithm and particle swarm optimization is used as a solution, and then the Shapley value method is applied to analyze the profit allocation of each petrol depot under different coalitions. The optimal membership sequence of the cooperation is determined according to the strict monotone path. To analyze and verify the effectiveness of the proposed method, a regional petrol supply network in Chongqing city in China is investigated. Through cooperation between petrol depots in the supply network, the utilization of customer clustering, time window coordination, and distribution truck sharing can significantly reduce the total operation costs and improve the efficiency of urban transportation energy supply. This approach can provide theoretical support for relevant government departments and enterprises to make optimal decisions. The implementation of the joint distribution of energy can promote the sustainable development of urban transportation.

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“…The extension of this assignment problem is often called the transportation problem, which not only assigns the task to the machine, but also forms the transportation sequence when there is more than one task assigned to one machine: this is called the vehicle routing problem (VRP). There are plenty of examples of the transportation problem in the literature, such as transportation mode selection (road, waterway, railway) [10,11], connecting the transport mode by assigning the inbound and outbound gates of the product [12][13][14][15], transportation planning with time windows [14,[15][16][17], and optimal location assignment [18][19][20][21]. However, the AP aims to increase efficiency and sustainability by reducing time, cost, fuel consumption, and carbon dioxide emissions, increasing customer satisfaction levels and profits, and making other improvements.…”

Section: Literature Reviewmentioning

confidence: 99%

Modified Differential Evolution Algorithm for a Transportation Software Application

Supattananon

Akararungruangkul

2019

Journal of Open Innovation: Technology, Market, and Complexity

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This research developed a solution approach that is a combination of a web application and the modified differential evolution (MDE) algorithm, aimed at solving a real-time transportation problem. A case study involving an inbound transportation problem in a company that has to plan the direct shipping of a finished product to be collected at the depot where the vehicles are located is presented. In the newly designed transportation plan, a vehicle will go to pick up the raw material required by a certain production plant from the supplier to deliver to the production plant in a manner that aims to reduce the transportation costs for the whole system. The reoptimized routing is executed when new information is found. The information that is updated is obtained from the web application and the reoptimization process is executed using the MDE algorithm developed to provide the solution to the problem. Generally, the original DE comprises of four steps: (1) randomly building the initial set of the solution, (2) executing the mutation process, (3) executing the recombination process, and (4) executing the selection process. Originally, for the selection process in DE, the algorithm accepted only the better solution, but in this paper, four new selection formulas are presented that can accept a solution that is worse than the current best solution. The formula is used to increase the possibility of escaping from the local optimal solution. The computational results show that the MDE outperformed the original DE in all tested instances. The benefit of using real-time decision-making is that it can increase the company’s profit by 5.90% to 6.42%.

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Product-oriented time window assignment for a multi-compartment vehicle routing problem

Cited by 47 publications

References 43 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

Collaborative Multidepot Petrol Station Replenishment Problem with Multicompartments and Time Window Assignment

Modified Differential Evolution Algorithm for a Transportation Software Application

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