Solving the green-fuzzy vehicle routing problem using a revised hybrid intelligent algorithm
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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.
KeywordsVehicle routing problem Fuzzy travel time Fuzzy simulation Green logistics Genetic algorithm
The authors would like to acknowledge also the gracious support of this work by “Shuguang Program” from Shanghai Education Development Foundation and Shanghai Municipal Education Commission (Grant no. 15SG36), and the Recruitment Program of High-end Foreign Experts (Grant no. GDW20163100009).
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