Memetic algorithm for the dynamic vehicle routing problem with simultaneous delivery and pickup

Berahhou, Amina; Benadada, Youssef; Bouanane, Khaoula

doi:10.5267/j.ijiec.2022.6.001

Cited by 7 publications

(3 citation statements)

References 1 publication

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“…In addition, if the fitness function is not properly selected, the genetic algorithm often converges to local optima, failing to identify the global optimum [174][175][176]. Genetic algorithms can be improved in four aspects: individuals and populations, selection operations, crossover operations, and mutation operations, thereby optimizing the population and addressing the drawbacks of early maturity and local optima; an example of an improved GA is MA [177][178][179]. The ant colony algorithm and particle swarm algorithm are the most commonly used swarm intelligence algorithms.…”

Section: Metaheuristic Algorithmsmentioning

confidence: 99%

A Bibliometric Visualized Analysis and Classification of Vehicle Routing Problem Research

Tang

2023

Sustainability

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The vehicle routing problem (VRP), as a classic combinatorial optimization problem, has always been a hot research topic in operations research. In order to gain a deeper understanding of the VRP problem, this work uses the knowledge graph to comprehensively analyze and summarize the literature related to VRP from 1959 to 2022 in the Web of Science (WoS) database. Firstly, according to the basic statistical information of the literature, the annual publications, the authors, their institutions and countries, the keyword co-occurrence, and the literature co-citation network are analyzed to comprehensively summarize and generalize the research on VRP and predict its future development trend. The results show that, in the past 60 years, there have been abundant changes in the research on VRP. The United States and China have made the most important contributions in the field of VRP. According to the WoS literature retrieval results and classification methods, the VRP models and their solutions are comprehensively classified, and the model solving algorithms are divided into exact algorithms, heuristic algorithms, metaheuristic algorithms, hyper-heuristic algorithms, machine learning, etc. The results show that the development of information computing technology plays an important role in research on the VRP problem, and dynamic VRP, hyper-heuristic algorithms, deep reinforcement learning, etc. are the future development directions of the VRP model and its optimization. The results of this research can provide help and guidance for beginners and scholars outside the industry to comprehensively understand the development and research hotspots of VRP.

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Section: Metaheuristic Algorithmsmentioning

confidence: 99%

A Bibliometric Visualized Analysis and Classification of Vehicle Routing Problem Research

Tang

2023

Sustainability

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“…The seru formation is solved by the GA combining local search to obtain a better solution (Berahhou et al, 2022).…”

Section: Ga For Seru Formationmentioning

confidence: 99%

To reduce maximum tardiness by Seru Production: model, cooperative algorithm combining reinforcement learning and insights

Fu¹,

Yu²,

Sun³

et al. 2023

10.5267/j.ijiec

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The maximum tardiness reflects the worst level of service associated with customer needs; thus, the principle that seru production reduces the maximum tardiness is investigated, and a model to minimize the maximum tardiness of the seru production system is established. In order to obtain the exact solution, the non-linear seru production model with minimizing the maximum tardiness is split into a seru formation model and a linear seru scheduling model. We propose an efficient cooperative algorithm using a genetic algorithm and an innovative reinforcement learning algorithm (CAGARL) for large-scale problems. Specifically, the GA is designed for the seru formation problem. Moreover, the QL-seru algorithm (QLSA) is designed for the seru scheduling problem by combining the features of meta-heuristics and reinforcement learning. In the QLSA, we design an innovative QL-seru table and two state trimming rules to save computational time. After extensive experiments, compared with the previous algorithm, CAGARL improved by an average of 56.6%. Finally, several managerial insights on reducing maximum tardiness are proposed.

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“…In the biomedical works, metaheuristic has been used to optimize the detection of COVID-19 severity [5], lung cancer [6], brain tumor [7], anterior cruciate ligament deficiency [8], melanoma [9], and so on. In the manufacturing and logistics, metaheuristic has been used to optimize the parallel machine scheduling [10], vehicle distribution logistics [11], pickup and delivery problem [12], inventory management and assortment planning [13], logistic distribution center [14], and so on. In transportation sector, metaheuristic has been used to optimize the maintenance scheduling of highway networks [15], forecast the traffic flow [16], and so on.…”

Section: Introductionmentioning

confidence: 99%

Quad Tournament Optimizer: A Novel Metaheuristic Based on Tournament Among Four Strategies

2023

IJIES

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This paper introduced a novel metaheuristic that is developed based on the tournament mechanism, namely quad tournament optimizer (QTO). As its name suggests, QTO proposes a new approach of metaheuristic in which there are four searches conducted by each agent in every iteration. These searches are: (1) searching toward the global best solution, (2) searching toward the middle between the global best solution and a randomly selected solution, (3) searching relative to a randomly selected solution, and (4) neighbourhood search around the corresponding solution and the global best solution. A solution candidate is generated by each search. Then, a tournament is carried out to find the best candidate. This strategy is novel because most of metaheuristic deploys only single search or multiple searches where each search is conducted sequentially. QTO is challenged to find the optimal solution of 23 classic functions. In this challenge, QTO is benchmarked against five shortcoming metaheuristics: marine predator algorithm (MPA), slime mould algorithm (SMA), golden search optimizer (GSO), hybrid pelican Komodo algorithm (HPKA), and guided pelican algorithm (GPA). The result indicates that QTO outperforms all these benchmark metaheuristics.

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Memetic algorithm for the dynamic vehicle routing problem with simultaneous delivery and pickup

Cited by 7 publications

References 1 publication

A Bibliometric Visualized Analysis and Classification of Vehicle Routing Problem Research

A Bibliometric Visualized Analysis and Classification of Vehicle Routing Problem Research

To reduce maximum tardiness by Seru Production: model, cooperative algorithm combining reinforcement learning and insights

Quad Tournament Optimizer: A Novel Metaheuristic Based on Tournament Among Four Strategies

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