A hybrid heuristic approach for the multi-objective multi depot vehicle routing problem

Londoño, Andrés Arias; González, Walter Gil; Giraldo, Oscar Danilo Montoya; Escobar, John Wilmer

doi:10.5267/j.ijiec.2023.9.006

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…Variable neighborhood search is also popular in the research on genetic algorithms. Londoño et al [155] used the variable neighborhood search algorithm within a Chu-Beasley Genetic Algorithm to overcome the multi-depot vehicle routing problem. Pan et al [156] used hybrid metaheuristic algorithms involving a genetic algorithm and multiple population genetic algorithm (MPGA) with variable neighborhood search to optimize a perishable product supply chain network.…”

Section: Citation Metricsmentioning

confidence: 99%

A Bibliometric Analysis of a Genetic Algorithm for Supply Chain Agility

Lam,

Lee

2024

Mathematics

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As a famous population-based metaheuristic algorithm, a genetic algorithm can be used to overcome optimization complexities. A genetic algorithm adopts probabilistic transition rules and is suitable for parallelism, which makes this algorithm attractive in many areas, including the logistics and supply chain sector. To obtain a comprehensive understanding of the development in this area, this paper presents a bibliometric analysis on the application of a genetic algorithm in logistics and supply chains using data from 1991 to 2024 from the Web of Science database. The authors found a growing trend in the number of publications and citations over the years. This paper serves as an important reference to researchers by highlighting important research areas, such as multi-objective optimization, metaheuristics, sustainability issues in logistics, and machine learning integration. This bibliometric analysis also underlines the importance of Non-Dominated Sorting Genetic Algorithm II (NSGA-II), sustainability, machine learning, and variable neighborhood search in the application of a genetic algorithm in logistics and supply chains in the near future. The integration of a genetic algorithm with machine learning is also a potential research gap to be filled to overcome the limitations of genetic algorithms, such as the long computational time, difficulties in obtaining optimal solutions, and convergence issues for application in logistics and supply chains.

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Section: Citation Metricsmentioning

confidence: 99%

A Bibliometric Analysis of a Genetic Algorithm for Supply Chain Agility

Lam,

Lee

2024

Mathematics

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On the optimization of green multimodal transportation: a case study of the West German canal system

Binsfeld,

Hamdan,

Jouini

et al. 2024

Ann Oper Res

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In this study, we address a biobjective multimodal routing problem that consists of selecting transportation modes and their respective quantities, optimizing transshipment locations, and allocating port orders. In the objective functions, we minimize total transportation costs and use the EcoTransit methodology to minimize total greenhouse gas emissions. The optimization model selects the transportation mode and transshipment port where quantities are transshipped from one mode to another. We compare inland waterway transportation and trucks encountering infrastructure failures that require rerouting or modal shifting in a real-life case study on the supply of goods for the chemical industry in the West German canal system. We propose a population-based heuristic to solve large instances in a reasonable computation time. A sensitivity analysis of demand, of varying lock times, and of infrastructure failure scenarios was conducted. We show that compared with inland waterway transportation, multimodal transportation reduces costs by 23% because of longer lock times. Our analysis shows that the use of inland waterway transportation only during infrastructure failures imposes nearly 28% higher costs per day depending on the failure location compared to that of the case of no failures. We also show that the use of a multimodal transportation system helps to reduce this cost increase in lock failure scenarios.

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A hybrid heuristic approach for the multi-objective multi depot vehicle routing problem

Londoño,

González,

Giraldo

et al. 2024

10.5267/j.ijiec

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Efficiency in logistics is often affected by the fair distribution of the customers along the routes and the available depots for goods delivery. From this perspective, in this study, the Multi-depot Vehicle Routing Problem (MDVRP), by considering two objectives, is addressed. The two objectives in conflict for MDVRP are the distance traveled by vehicles and the standard deviation of the routes’ length. A significant standard deviation value provides a small distance traveled by vehicles, translated into unbalanced routes. We have used a weighted average objective function involving the two objectives. A Variable Neighborhood Search algorithm within a Chu-Beasley Genetic Algorithm has been proposed to solve the problem. For decision-making purposes, several values are chosen for the weight factors multiplying the terms at the objective function to build up a non-dominated front of solutions. The methodology is tested in large-size instances for the MDVRP, reporting noticeable results for managerial insights.

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A hybrid heuristic approach for the multi-objective multi depot vehicle routing problem

Cited by 3 publications

References 33 publications

A Bibliometric Analysis of a Genetic Algorithm for Supply Chain Agility

A Bibliometric Analysis of a Genetic Algorithm for Supply Chain Agility

On the optimization of green multimodal transportation: a case study of the West German canal system

A hybrid heuristic approach for the multi-objective multi depot vehicle routing problem

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