文章基本信息

标题：MULTI-OBJECTIVE GENETIC ALGORITHMS FOR THE GREEN VEHICLE ROUTING PROBLEM: A COMPARATIVE STUDY
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作者：FAHAD LAYTH MALALLAH ; BARAA T. SHAREF ; ASO MOHAMMAD DARWESH 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2017
卷号：95
期号：23
页码：6546
出版社：Journal of Theoretical and Applied
摘要：The Green Vehicle Routing Problem (GVRP) is an extension of the standard VRP taking into account the awareness of companies and governments of the dangerous effect of gases emissions. The primary objective of the GVRP is to minimize the volume of emitted carbon dioxide (co2) in adding to the optimization of the traveled distance and other functional objectives. In this paper, we model the GVRP as a bi-objective optimization problem for which many solving algorithms can be adapted and applied including deferent variants and extensions of Multi-Objective Genetic Algorithms (MOGAs). We select three elitist MOGAs: Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm - II (SPEA-II) and the Indicator-Based Evolutionary Algorithm (IBEA) to evaluate the quality of the returned Pareto fronts using deferent metrics: computation time, traveled distance, emissions volume, generational distance, spacing, entropy, and contribution. The comparison is performed on a set of standard benchmark problems. The experimental results show that IBEA outperforms other algorithms over many metrics.
关键词：Green Supply Chain; Green Multi-Objective VRP; Multi-Objective Genetic Algorithms