A chance constrained programming model and an improved large neighborhood search algorithm for the electric vehicle routing problem with stochastic travel times

Messaoud, Elhassania

doi:10.1007/s12065-021-00648-0

Cited by 14 publications

(4 citation statements)

References 41 publications

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“…Elhassania [12] introduced an innovative approach to the electric vehicle routing problem with stochastic travel times (EVRPSTT) using an iterated local neighborhood search (ILNS). However, the computational time required for each instance, particularly as the number of scenarios in the stochastic environment increases, limits the approach.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the context of small-sized GVRP instances, typically encompassing around 20 customers and 3 fuel stations, we assessed the performance of the proposed DGWO-QL algorithm. This assessment was conducted in comparison with three existing algorithms: the modified clarke and wright savings/distance-based clustering algorithm (MCWS/DBCA) [1], the large neighborhood search (LNS) [11], and the improved large neighborhood search/chance constraint programming (ILNS/CCP) [12]. Additionally, we evaluated the DGWO-QL against our DGWO algorithm with randomly tuned parameters.…”

Section: Small-sized Instancesmentioning

confidence: 99%

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Grey Wolf Optimizer for Green Vehicle Routing Problem

2023

IJIES

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This research proposes an efficient algorithm for solving the green vehicle routing problem (GVRP), capable of generating high-quality solutions while considering environmental impact and computational efficiency. We employ an adapted grey wolf optimizer (GWO) algorithm and Q-learning (QL) for parameter optimization, introducing a discrete grey wolf optimizer (DGWO), a discrete variant of the GWO. The DGWO leverages the 2-opt technique and the Hamming distance concept, making it suitable for addressing discrete problems like GVRP. The key novelty of our approach is the use of QL to refine the parameters of the DGWO, specifically the number of iterations and number of wolves. This application of QL significantly enhances the efficiency and effectiveness of the algorithm compared to DGWO with manual parameter tuning, highlighting the significance of QL in parameter optimization. The proposed discrete grey wolf optimizer-Q-learning (DGWO-QL) algorithm is extensively validated on benchmark instances of GVRP, demonstrating promising results. For smaller benchmark instances comprising of 20 customers and 3 stations, our approach outperforms in 12 out of 16 instances. When tested on larger benchmark instances, within a range of 111 to 500 customers and 21 stations, it achieves success in 10 out of 12 instances. Compared to existing methods, our approach demonstrates improved performance in terms of solution quality and computational efficiency. The results show the robust performance of the DGWO-QL, particularly under stochastic route scenarios, which underscores the advantage of the proposed technique. This study represents a significant contribution to the current body of literature by underscoring the potential of the DGWO-QL algorithm in generating high-quality solutions for GVRP.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Small-sized Instancesmentioning

confidence: 99%

Grey Wolf Optimizer for Green Vehicle Routing Problem

2023

IJIES

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“…Abhinav Gupta [7] considered VRP along with additional constraints of capacity and time-windows (CVRPTW), he aimed to provide a fast and approximately optimal solutions to large-scale CVRPTW problems, and presented a deep Q-network with encoder-decoder based reinforcement learning approach to solve CVRPTW. Messaoud E [8] solved the Electric Vehicle Routing Problem with Stochastic Travel Times (EVRPSTT) by proposing a Chance Constrained Programming (CCP) Model, as well as a new scheme based on an Improved Large Neighborhood Search (ILS) algorithm and a Monte Carlo Sampling (MCS) procedure.Jie Wanchen [9] and Zhen Cao [10] have also established a multi vehicle electric vehicle path planning problem model in our research on the charging and power consumption of electric vehicle batteries. S Fujimura [11] proposed an attention-based end-to-end DRL model to solve VRP which embeds edge information between nodes for rich graph representation learning.The third type of research on pure electric logistics vehicle routing is to start exploring VRP problems by combining external complex conditions.…”

Section: Introductionmentioning

confidence: 99%

Terminal Collaborative Path Optimization of Multi-Distribution Subjects for Electric Unmanned Vehicles

Fan,

Sun

2024

Proceedings of the 4th International Conference on Informatization Economic Development and Management, IEDM 2024, February 23–

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Because the planning and design of urban logistics systems do not incorporate terminal logistics distribution into unified solutions, less consideration is given to the collaborative distribution environment of multiple distribution subjects. This oversight results in low overall efficiency and service quality of logistics distribution. Simultaneously, considering the impact of reducing energy consumption and episodic sites on logistics distribution, electric unmanned vehicles with high efficiency, low cost, and noncontact features have become the main vehicle of terminal logistics distribution. Therefore, this paper considers urban terminal distribution, constructs a multiagent collaborative distribution model with electric unmanned vehicles as the carrier, and designs an improved Tabu Search algorithm to solve the model. In simulation, our optimization model can fully realize the advantages of energy savings, emission reduction and cost reduction of electric unmanned vehicles compared to operation using the traditional single distribution and single source vehicle distribution models. The solution provided here can also reduce the promotion of terminal distribution in logistics activities and promote the green, low carbon and sustainable development in cities.

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“…[3] According to the carbon emission principle, through the optimization of the refrigeration link of refrigerated vehicles and the research on the cold chain distribution path of fresh agricultural products, the carbon emissions in the cold chain logistics link can be reduced. There are many methods to solve VRP model, such as large neighborhood search algorithm, [4] ant colony algorithm, hybrid self-learning particle swarm optimization algorithm, [5] adaptive tabu search algorithm and genetic algorithm. [6] Based on the vehicle routing problem with time windows, this paper analyzes the characteristics of the distribution of fresh product routes, and determines the corresponding costs, including the costs of losses caused by product corruption and the costs caused by anti customer demand time windows.…”

Section: Introductionmentioning

confidence: 99%

Research on the logistics distribution path of fresh products with time windows

Fang¹

2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

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With the improvement of people's living standards, people have higher and higher requirements on the quality and freshness of fresh products. However, the consumption rate of fresh products is very high, especially in the circulation link. At the same time, retailers will put forward restrictive requirements on delivery time and delivery quality. For distributors, they should consider the impact of factors such as vehicle emissions, perishability of delivered goods on distribution, In order to minimize the distribution cost. Based on the vehicle routing problem under the traditional time window, this paper analyzes the characteristics of fresh product routing and constructs the related cost function. Secondly, from the perspective of the distributor, the basic model of vehicle routing optimization for fresh product distribution is constructed with the goal of minimizing the total cost. At the same time, adaptive genetic algorithm is used to solve the model. Finally, a numerical example is analyzed to verify the rationality and feasibility of the model and algorithm built in this paper, and achieve the goal of optimizing the distribution path and reducing the logistics cost.

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A chance constrained programming model and an improved large neighborhood search algorithm for the electric vehicle routing problem with stochastic travel times

Cited by 14 publications

References 41 publications

Grey Wolf Optimizer for Green Vehicle Routing Problem

Grey Wolf Optimizer for Green Vehicle Routing Problem

Terminal Collaborative Path Optimization of Multi-Distribution Subjects for Electric Unmanned Vehicles

Research on the logistics distribution path of fresh products with time windows

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