Using Pareto-based multi-objective Evolution algorithms in decision structure to transfer the hazardous materials to safety storage centre
| Autor: | Farzad Mahmoudsoltani, Hamid Shahbandarzadeh, Reza Moghdani |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
021103 operations research Renewable Energy Sustainability and the Environment Computer science Strategy and Management 0211 other engineering and technologies Evolutionary algorithm Novelty Sorting Pareto principle 02 engineering and technology Fuzzy logic Industrial and Manufacturing Engineering 020901 industrial engineering & automation Hazardous waste Genetic algorithm Decomposition (computer science) Algorithm General Environmental Science |
| Zdroj: | Journal of Cleaner Production. 184:893-911 |
| ISSN: | 0959-6526 |
| Popis: | The influence of Hazardous Materials on different kind of human aspects motivate scholars to increase their attention for decreasing negative impacts of them to human life. In this concern, this paper addresses a decision structure to transfer the hazardous materials to the safe location in the industries logistic system. To reach this goal, we present a new mathematical modelling problem in which location routing problem of hazardous material is addressed. To cope imprecise risk in a practical way, we define it to three kinds in the fuzzy environment including accident risk, population risk, and the bio-environmental risk. To obtain optimal results, three well-known multi-objective evolutionary algorithms (MOEAs) including Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm II (SPEA-II) and Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) are presented for solving ten test problems. In this case, this novelty of the model is discussed, firstly, and then proposed algorithms are compared with respect to obtained results. The results of this paper show that Non-dominated Sorting Genetic Algorithm II (NSGA-II) has superior performance in terms of metrics, and Strength Pareto Evolutionary Algorithm II (SPEA-II) beats other multi-objective evolutionary algorithms in terms of obtained number of individuals in final Pareto solutions. |
| Databáze: | OpenAIRE |
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