Solution of multi-objective transportation-p-facility location problem with effect of variable carbon emission by evolutionary algorithms
Abstract This paper presents an evolutionary approach-based solution of multi-objective transportation-p-facility location problem (MOT-p-FLP) that minimizes overall transportation time, cost of transportation, and carbon emission (CE) from available sites to facility sites by seeking transported pr...
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| Autor*in: |
Vansia, Dhruvrajsinh O. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 25(2021), 15 vom: 08. Feb., Seite 9993-10005 |
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| Übergeordnetes Werk: |
volume:25 ; year:2021 ; number:15 ; day:08 ; month:02 ; pages:9993-10005 |
| Links: |
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| DOI / URN: |
10.1007/s00500-021-05619-2 |
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| Katalog-ID: |
OLC2126631710 |
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| 520 | |a Abstract This paper presents an evolutionary approach-based solution of multi-objective transportation-p-facility location problem (MOT-p-FLP) that minimizes overall transportation time, cost of transportation, and carbon emission (CE) from available sites to facility sites by seeking transported product quantities and the facility locations in the Euclidean plane. Genetic algorithm (GA), non-dominated sorting genetic algorithm (NSGA-II and NSGA-III), and modified Self-Adaptive Multi-Population Elitism Jaya Algorithm (SAMPE JA) are utilized to solve the problem. We compared obtained compromise solutions of the problem by evolutionary algorithms with population size, the maximum number of generations, crossover probability, mutation probability, and computational time. Sensitivity analysis for supply, demand, and carbon cap parameters is incorporated in the model’s solution, which helps the decision maker make the appropriate decision. These evolutionary algorithms (NSGA-II and NSGA-III) give Pareto-optimal solutions, and it helps management decide on the selection of p-facility locations. They could transport their product to the facility locations easily, with minimum transport, CE costs, and transportation time. As a result, management can give equal attention to their profit and environment, which helps to build world market credibility. At last, the paper concludes the results. | ||
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solution of multi-objective transportation-p-facility location problem with effect of variable carbon emission by evolutionary algorithms |
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Solution of multi-objective transportation-p-facility location problem with effect of variable carbon emission by evolutionary algorithms |
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Abstract This paper presents an evolutionary approach-based solution of multi-objective transportation-p-facility location problem (MOT-p-FLP) that minimizes overall transportation time, cost of transportation, and carbon emission (CE) from available sites to facility sites by seeking transported product quantities and the facility locations in the Euclidean plane. Genetic algorithm (GA), non-dominated sorting genetic algorithm (NSGA-II and NSGA-III), and modified Self-Adaptive Multi-Population Elitism Jaya Algorithm (SAMPE JA) are utilized to solve the problem. We compared obtained compromise solutions of the problem by evolutionary algorithms with population size, the maximum number of generations, crossover probability, mutation probability, and computational time. Sensitivity analysis for supply, demand, and carbon cap parameters is incorporated in the model’s solution, which helps the decision maker make the appropriate decision. These evolutionary algorithms (NSGA-II and NSGA-III) give Pareto-optimal solutions, and it helps management decide on the selection of p-facility locations. They could transport their product to the facility locations easily, with minimum transport, CE costs, and transportation time. As a result, management can give equal attention to their profit and environment, which helps to build world market credibility. At last, the paper concludes the results. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
| abstractGer |
Abstract This paper presents an evolutionary approach-based solution of multi-objective transportation-p-facility location problem (MOT-p-FLP) that minimizes overall transportation time, cost of transportation, and carbon emission (CE) from available sites to facility sites by seeking transported product quantities and the facility locations in the Euclidean plane. Genetic algorithm (GA), non-dominated sorting genetic algorithm (NSGA-II and NSGA-III), and modified Self-Adaptive Multi-Population Elitism Jaya Algorithm (SAMPE JA) are utilized to solve the problem. We compared obtained compromise solutions of the problem by evolutionary algorithms with population size, the maximum number of generations, crossover probability, mutation probability, and computational time. Sensitivity analysis for supply, demand, and carbon cap parameters is incorporated in the model’s solution, which helps the decision maker make the appropriate decision. These evolutionary algorithms (NSGA-II and NSGA-III) give Pareto-optimal solutions, and it helps management decide on the selection of p-facility locations. They could transport their product to the facility locations easily, with minimum transport, CE costs, and transportation time. As a result, management can give equal attention to their profit and environment, which helps to build world market credibility. At last, the paper concludes the results. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract This paper presents an evolutionary approach-based solution of multi-objective transportation-p-facility location problem (MOT-p-FLP) that minimizes overall transportation time, cost of transportation, and carbon emission (CE) from available sites to facility sites by seeking transported product quantities and the facility locations in the Euclidean plane. Genetic algorithm (GA), non-dominated sorting genetic algorithm (NSGA-II and NSGA-III), and modified Self-Adaptive Multi-Population Elitism Jaya Algorithm (SAMPE JA) are utilized to solve the problem. We compared obtained compromise solutions of the problem by evolutionary algorithms with population size, the maximum number of generations, crossover probability, mutation probability, and computational time. Sensitivity analysis for supply, demand, and carbon cap parameters is incorporated in the model’s solution, which helps the decision maker make the appropriate decision. These evolutionary algorithms (NSGA-II and NSGA-III) give Pareto-optimal solutions, and it helps management decide on the selection of p-facility locations. They could transport their product to the facility locations easily, with minimum transport, CE costs, and transportation time. As a result, management can give equal attention to their profit and environment, which helps to build world market credibility. At last, the paper concludes the results. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
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Solution of multi-objective transportation-p-facility location problem with effect of variable carbon emission by evolutionary algorithms |
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