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A Genetic Algorithm with Multiple Populations to Reduce Fuel Consumption in Supply Chain
Department of Industrial Engineering, University of Semnan, Iran.
Department of Industrial Engineering, University of Semnan, Iran.
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID-id: 0000-0001-5530-3517
2019 (Engelska)Ingår i: International Journal of Transportation Engineering, ISSN 2322-259XArtikel i tidskrift (Refereegranskat) Epub ahead of print
Abstract [en]

Reducing fuel consumption by transportation fleet in a supply chain, reduces transportation costs and consequently, the product final cost. Moreover, it reduces environmental pollution, and in some cases, it helps governments constitute less subsidies for fuels. In this paper, a supply chain scheduling is studied, with the two objective functions of minimizing the total fuel consumption, and the total order delivery time. After presenting the mathematical model of the problem, a genetic algorithm, named Social Genetic Algorithm (SGA) is proposed to solve it. The proposed algorithm helps decision makers determine the allocation of orders to the suppliers and vehicles and production and transportation scheduling to minimize total order delivery time and fuel consumption. In order for SGA performance evaluation, its results are compared with another genetic algorithm in the literature and optimal solution. Finally, a sensitivity analysis is performed on SGA. The results of comparisons also show the high performance of SGA. Moreover, by increasing the number of suppliers and vehicles and decreasing the number of orders, the value of the objective function is reduced.

Ort, förlag, år, upplaga, sidor
Tarahan Parseh Transportation Research Institute , 2019.
Nyckelord [en]
Transportation, Fuel consumption, Supply chain management, Routing, Genetic algorithm
Nationell ämneskategori
Produktionsteknik, arbetsvetenskap och ergonomi
Forskningsämne
Produktion och automatiseringsteknik
Identifikatorer
URN: urn:nbn:se:his:diva-17494DOI: 10.22119/IJTE.2019.134126.1410OAI: oai:DiVA.org:his-17494DiVA, id: diva2:1340736
Tillgänglig från: 2019-08-06 Skapad: 2019-08-06 Senast uppdaterad: 2019-11-13Bibliografiskt granskad

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Förlagets fulltexthttp://www.ijte.ir/article_91266_0.html

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Fathi, Masood

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Produktionsteknik, arbetsvetenskap och ergonomi

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