Solving the green-fuzzy vehicle routing problem using a revised hybrid intelligent algorithm

Ruonan Wang, Jian Zhou, Xiajie Yi, Athanasios A. Pantelous

Research output: Contribution to journalArticleResearchpeer-review

31 Citations (Scopus)


Green logistics is an emerging area in supply chain management, which has been shown to have tremendous impacts in recent years to face the serious climate changes risks. In this paper, the fuel consumption and fuzzy travel time have been delineated in developing and solving the green-fuzzy vehicle routing problem as an extension of the celebrated VRP in which routes are performed to reduce the total expenditure. Different from the existing solution manners, we transform the original fuzzy chance constrained programming model into an equivalent deterministic model, and then revise the original hybrid intelligent algorithm by replacing the embedded fuzzy simulation with analytical function calculation. Finally, a comparative study with the corresponding literature is performed, which shows that the revised algorithm can not only improve the solution accuracy but also shorten the runtime greatly.

Original languageEnglish
Pages (from-to)321-332
Number of pages12
JournalJournal of Ambient Intelligence and Humanized Computing
Issue number1
Publication statusPublished - 29 Jan 2019


  • Fuzzy simulation
  • Fuzzy travel time
  • Genetic algorithm
  • Green logistics
  • Vehicle routing problem

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