A Genetic Algorithm for a Green Vehicle Routing Problem

Costa, Paulo Roberto de Oliveira da; Mauceri, Stefano; Carroll, Paula; Pallonetto, Fabiano

doi:10.1016/j.endm.2018.01.008

Cited by 77 publications

(24 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The heuristic is combined with a dynamic programming procedure, which leads to efficient solutions for large instances. More recently, to better estimate CO 2 emissions, de Oliveira da Costa et al [55] applied the same metaheuristics using real road speed and gradient data.…”

Section: Strategies Based On Greening the Objective Functionmentioning

confidence: 99%

Optimizing Energy Consumption in Transportation: Literature Review, Insights, and Research Opportunities

et al. 2020

View full text Add to dashboard Cite

From airplanes to electric vehicles and trains, modern transportation systems require large quantities of energy. These vast amounts of energy have to be produced somewhere—ideally by using sustainable sources—and then brought to the transportation system. Energy is a scarce and costly resource, which cannot always be produced from renewable sources. Therefore, it is critical to consume energy as efficiently as possible, that is, transportation activities need to be carried out with an optimal intake of energetic means. This paper reviews existing work on the optimization of energy consumption in the area of transportation, including road freight, passenger rail, maritime, and air transportation modes. The paper also analyzes how optimization methods—of both exact and approximate nature—have been used to deal with these energy-optimization problems. Finally, it provides insights and discusses open research opportunities regarding the use of new intelligent algorithms—combining metaheuristics with simulation and machine learning—to improve the efficiency of energy consumption in transportation.

show abstract

Section: Strategies Based On Greening the Objective Functionmentioning

confidence: 99%

Optimizing Energy Consumption in Transportation: Literature Review, Insights, and Research Opportunities

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Point 0 represents one depot of paths using the "byg29" dataset. The chromosome sequence of a successful solution after acceptable iteration = [0, 1,28,2,25,4,8,11,5,20,9,19,3,14,17,16,21,13,10,18,23,12,27,7,26,22,6,24,15,9,19]. The path plot of this chromosome is shown in Figure 7.…”

Section: Figure 5 Solution Costs Based On Generationmentioning

confidence: 99%

“…EA is preferred to acquire successful and productive solutions for various problem fields. For example, the Genetic Algorithm (GA), which is one of the EA, was used for green vehicle routing problem [1] .…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary Approach to Multiple Traveling Salesman Problem for Efficient Distribution of Pharmaceutical Products

Kocyigit

Şahingöz

Di̇ri̇

2020

2020 International Conference on Electrical Engineering (ICEE)

View full text Add to dashboard Cite

Considerable growth of computer science has created novel solutions for variable problem fields and has increased the efficiency of available solutions. Evolutionary algorithms are quite successful in dealing with real-world problems that require optimization. In this article, we implemented a Genetic Algorithm that is well known evolutionary algorithm in order to provide an efficient solution for the Distribution of Pharmaceutical Products, which is a vital optimization problem, especially in situations such as a pandemic. The Multiple Traveling Salesman Problem approach was used to distribute pharmaceutical products as soon as possible. Moreover, we strengthened our proposal algorithm with 2-Opt Algorithm to get optimal results in earlier iterations. Different datasets from a library were applied to measure the quality of solutions and computation time. At the end of the work, we observed that our proposed algorithm generates successful solutions in an acceptable running time. This study will be extended with a new mutation concept as future work.

show abstract

“…The steps of genetic algorithm include coding, decoding, initializing population, fitness calculation, selection, crossover, and mutation. The detail can refer to reference [38].…”

Section: A Genetic Algorithmmentioning

confidence: 99%

Navigability Analysis of Local Gravity Map With Projection Pursuit-Based Selection Method by Using Gravitation Field Algorithm

Liu

Xin

2019

IEEE Access

View full text Add to dashboard Cite

The positioning accuracy of the gravity-aided navigation is closely related to the selection of local gravity maps. In this paper, the local gravity maps are converted into 8-bit gray images. The navigability features of the local gravity maps are extracted by using the image texture feature analysis methods, such as the gray histogram complexity, the Sobel operator, and the gray level co-occurrence matrix, and the navigability comprehensive evaluation of each local gravity map is obtained by using the projection pursuit model. According to the nebula model, a gravitation field algorithm is proposed. The gravitation field algorithm, genetic algorithm, and firefly algorithm are used to obtain the optimal projection direction of the projection pursuit model, respectively. We compared the optimizing results and gave the navigability evaluation of local gravity maps that provide the basis for the selection of local gravity map. The comparison results show that the gravitation field algorithm has the best performance in obtaining the optimal projection direction, and the contributions of the navigability features in the navigability evaluation are most equal. Under the same experimental conditions, the local gravity map selected by the method proposed in this paper has the highest positioning accuracy and the best matching track. INDEX TERMS Navigability analysis, gravitation field algorithm, projecting pursuit-based selection method, gravity navigability features.

show abstract

A Genetic Algorithm for a Green Vehicle Routing Problem

Cited by 77 publications

References 13 publications

Optimizing Energy Consumption in Transportation: Literature Review, Insights, and Research Opportunities

Optimizing Energy Consumption in Transportation: Literature Review, Insights, and Research Opportunities

An Evolutionary Approach to Multiple Traveling Salesman Problem for Efficient Distribution of Pharmaceutical Products

Navigability Analysis of Local Gravity Map With Projection Pursuit-Based Selection Method by Using Gravitation Field Algorithm

Contact Info

Product

Resources

About