QMOEA: A Q-learning-based multiobjective evolutionary algorithm for solving time-dependent green vehicle routing problems with time windows

Qi, Rui; Li, Junqing; Wang, Juan; Jin, Hui; Han, Yuyan

doi:10.1016/j.ins.2022.06.056

Cited by 62 publications

(8 citation statements)

References 45 publications

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“…Individuals who are stronger (more suited to the environment) than the competition are more likely to generate offsprings who can better survive. A hot research area in recent years is applying multiobjective evolutionary algorithms (MOEA) to solving VRPs with multiple objectives [300,299,169,321,322,227]. Swarm intelligence [142] is a field of study that examines natural and artificial systems made up of numerous individuals that cooperate with each other.…”

Section: Metaheuristicsmentioning

confidence: 99%

Heuristics for Vehicle Routing Problem: A Survey and Recent Advances

Liu¹,

Lu²,

Gui³

et al. 2023

Preprint

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Vehicle routing is a well-known optimization research topic with significant practical importance. Among different approaches to solving vehicle routing, heuristics can produce a satisfactory solution at a reasonable computational cost. Consequently, much effort has been made in the past decades to develop vehicle routing heuristics. In this article, we systematically survey the existing vehicle routing heuristics, particularly on works carried out in recent years. A classification of vehicle routing heuristics is presented, followed by a review of their methodologies, recent developments, and applications. Moreover, we present a general framework of state-of-the-art methods and provide insights into their success. Finally, three emerging research topics with notable works and future directions are discussed.

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Section: Metaheuristicsmentioning

confidence: 99%

Heuristics for Vehicle Routing Problem: A Survey and Recent Advances

Liu¹,

Lu²,

Gui³

et al. 2023

Preprint

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“…Zhang et al (2020) used a hybrid multi-objective evolution algorithm (HMOEA-GL) approach to achieve this goal. Another goal of VRPTW is to minimize the total duration of the vehicle, energy consumption, customer satisfaction, total service time and minimize mileage, which has been discussed by Qi et al (2022), Wang et al (2019), Pérez-Rodríguez and Hernández-Aguirre (2019), Semiz and Polat (2020) In previous research, other goals were also discussed, such as improving the performance of the algorithm proposed by Dong et al (2018), proving the effectiveness of the solution method and stochastic models, mathematical proposals for new models, and developing new models have been discussed by Errico et al, (2018), Truden et al (2022), Srivastava et al, (2021), Ticha et al (2019), Nguyen et al (2016), andBaños et al (2016).…”

Section: Library Reviewmentioning

confidence: 99%

Metaheuristic Optimization for Vehicle Routing Problem with Time Windows (VRPTW)

Fitri¹,

Ishak²

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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The problem of determining vehicle routes in logistics has a role Which on pIt isnting bagi company to reduce transportation costs and late fees. Time window constraints are very common in current distribution processes. This research focuses on optimizing fuel costs and penalties. In this study the authors used the Vehicle Routing Problem with Time Windows (VRPTW). VRPTW is one of the most tackled transportation problems in real world situations. Artificial Bee Colony Algorithm (ABC) and Camel Algorithm (CA) will be used in the research. Study This is Focused on optimizing fuel costs and penalties. Furthermore, the two algorithms will be compared which of the two algorithms is the most optimal for solving the route determination problem in VRPTW. Numerical testing will be carried out for the proposed algorithm, as well as conducting experiments on several parameters to determine the effect of parameters for fuel costs and penalties.

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“…This space is ranged from 50 to 1000 with a step size of 10 for iterations and 5 to 100 with a step size of 1 for the number of wolves. An initial Q-table [19], with dimensions corresponding to the length of the parameter space, is randomly initialized with values between -1 and 1. Subsequently, QL parameters are set: a learning rate (α) of 0.5, a discount factor (γ) of 0.9, an exploration rate (ε) of 0.1, and a total of 2000 training episodes.…”

Section: Ql For Parameter Settingsmentioning

confidence: 99%

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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QMOEA: A Q-learning-based multiobjective evolutionary algorithm for solving time-dependent green vehicle routing problems with time windows

Cited by 62 publications

References 45 publications

Heuristics for Vehicle Routing Problem: A Survey and Recent Advances

Heuristics for Vehicle Routing Problem: A Survey and Recent Advances

Metaheuristic Optimization for Vehicle Routing Problem with Time Windows (VRPTW)

Grey Wolf Optimizer for Green Vehicle Routing Problem

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