Minimizing the Fuel Consumption of a Multiobjective Vehicle Routing Problem Using the Parallel Multi-Start NSGA II Algorithm

Psychas, Iraklis-Dimitrios; Marinaki, Magdalene; Marinakis, Yannis; Migdalas, Athanasios

doi:10.1007/978-3-319-29608-1_5

Cited by 12 publications

(2 citation statements)

References 37 publications

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“…presented an FCVRP model, in which air pollution and fuel consumption are considered to be minimized via the gravitational search algorithm in order to obtain optimal solutions in a reasonable computational time. A recent study byPsychas et al (2016) involved a bi-objective version of FCVRP, in which the first objective examines travel time, and the second, corresponds to fuel consumption. Two types of modeling, including asymmetric and symmetric problems, were presented for multi-objective optimization, and a new version of NSGA-II, initiated with multiple initial populations, was used to solve those models Zhang et al (2015b).…”

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confidence: 99%

The green vehicle routing problem: A systematic literature review

Moghdani

Salimifard

Demir

et al. 2021

Journal of Cleaner Production

154

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Recent decades have seen increasing utilization of optimization packages, based on Operations Research and Mathematical Programming techniques, for effective management of the provision of goods and services in distribution systems. Large numbers of real-world applications, both in North America and Europe, have widely shown that the use of computerized procedures for distribution process planning produces substantial savings (generally from 5% to 20%) in global transportation costs. It is easy to see that the impact of these savings on the global economic system is significant. The transportation process involves all stages of production and distribution systems and represents a relevant component (generally from 10% to 20%) of the final cost of goods. The green vehicle routing problem (GVRP) is an emerging research field that attracts many researchers. This survey paper aims to classify and review the literature on GVRPs from various perspectives. This paper covers publications between 2006 and 2019 including 309 papers. To this end, a systematic literature review has been implemented in order to respond to corresponding questions related to this area and proposed an extensive structure compromising various aspects including variants of GVRPs, objective functions, uncertainty, and solutions approach to analyze GVRPs studies in different perspectives. Some new research areas have been drawn based on problem classification, uncertainties, solution methodologies, and finally, the objective function approaches for future research directions and the results of this study show that researches on GVRPs are relatively fresh and there is still a room for large improvements in several areas.

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confidence: 99%

The green vehicle routing problem: A systematic literature review

Moghdani

Salimifard

Demir

et al. 2021

Journal of Cleaner Production

154

View full text Add to dashboard Cite

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“…Lin et al (2014) discussed the issue of GVRP, which focuses on optimizing fuel consumption. This study is commonly referred to as the fuel consumption vehicle routing problem (FCVRP) (Moghdani et al, 2020;Psychas et al, 2014;Rao et al, 2016).…”

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confidence: 99%

Hybrid Henry Gas Solubility Optimization: An Effective Algorithm for Fuel Consumption Vehicle Routing Problem

Utama

Farida

Fitriani

et al. 2021

JITI

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The depletion of non-renewable fuel reserves is the biggest problem in the logistics sector. This problem encourages the transportation sector to increase fuel efficiency in distribution activities. The fuel optimization problem in distribution routing problems is called the Fuel Consumption Vehicle Routing Problem (FCVRP). This study proposes a novel Hybrid Henry Gas Solubility Optimization (HHGSO) to solve FCVRP problems. Experiments with several parameter variants were carried out to determine the performance of HHGSO in optimizing fuel consumption. The results show that the parameters of the HHGSO algorithm affect fuel consumption and computation time. In addition, the higher the KPL, the smaller the resulting fuel consumption. The proposed algorithm is also compared with several algorithms. The comparison results show that the proposed algorithm produces better computational time and fuel consumption than the Hybrid Particle Swarm Optimization and Tabu Search algorithms.

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