Green Transportation and Information Uncertainty in Gasoline Distribution: Evidence from China. Issue 11 (2nd September 2021)

• Record Type: Journal Article
• Authors: Xu, Xiaofeng; Wang, Chenglong; Li, Jian; Shi, Chunming
• Is Part Of: Emerging markets finance & trade. Volume 57:Issue 11(2021)
• Page Start: 3101
• Page End: 3119
• Publication Date: 2021-09-02
• Subjects: Gasoline distribution -- green vehicle routing problem -- uncertainty transformation; C61; Balkan Peninsula -- Commerce -- Periodicals; Balkan Peninsula -- Foreign economic relations -- Periodicals; Europe, Central -- Commerce -- Periodicals; Europe, Central -- Foreign economic relations -- Periodicals; Europe, Eastern -- Commerce -- Periodicals; Europe, Eastern -- Foreign economic relations -- Periodicals; Turkey -- Commerce -- Periodicals; Turkey -- Foreign economic relations -- Periodicals; Asia -- Commerce -- Periodicals; Asia -- Foreign economic relations -- Periodicals; Eurasia -- Commerce -- Periodicals; Eurasia -- Foreign economic relations -- Periodicals; 382.05
• DOI: 10.1080/1540496X.2019.1708323
• Languages: English
• ISSNs: 1540-496X

Abstract

ABSTRACT: The green vehicle routing problem of distributing gasoline to retail gasoline stations in a way that is not just the most cost-effective overall but also reduces its own carbon emissions needs to account for uncertainty from multiple sources. Therefore, in this paper we study how to do this, simultaneously reducing the cost and minimizing the environmental impact in the presence of information uncertainty. Among the sources of uncertainty are random demand by gas stations and uncertain travel time to them by tankers, which we build into our model. We also design a three-stage heuristic algorithm. In the first stage, the chance constraints of the model are converted into their deterministic equivalents. In the second stage, gas stations are clustered, based on their distance and demand for gasoline. In the third stage, we use a genetic algorithm to solve the model based on the first two stages. Finally, we use our simulation results to propose how gas stations can optimize their cost of gasoline distribution.