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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.
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-5530-3517
2019 (English)In: International Journal of Transportation Engineering, ISSN 2322-259XArticle in journal (Refereed) 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.

Place, publisher, year, edition, pages
Tarahan Parseh Transportation Research Institute , 2019.
Keywords [en]
Transportation, Fuel consumption, Supply chain management, Routing, Genetic algorithm
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-17494DOI: 10.22119/IJTE.2019.134126.1410OAI: oai:DiVA.org:his-17494DiVA, id: diva2:1340736
Available from: 2019-08-06 Created: 2019-08-06 Last updated: 2019-11-13Bibliographically approved

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Publisher's full texthttp://www.ijte.ir/article_91266_0.html

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Fathi, Masood

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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