Dynamic and stochastic modeling for a closed-loop automotive supply chain under fuel issue and government intervention: A case study

Abstract

Nowadays, due to the continuous changes in the economic and political conditions, the nature of the decisions in the automotive and fuel industries has become dynamic and stochastic. In the case of decision-making under dynamic and stochastic conditions, the goals of supply chain members may be in conflict with each other. Furthermore, according to environmental reasons, the closed-loop supply chain and the issue of decreasing carbon dioxide emissions have attracted extremely attention in the automotive industry. This paper examines a closed-loop automotive supply chain with dual-channel sale system alongside a fuel supply chain under government support. The car manufacturer develops fuel-efficient technologies to diminish carbon dioxide emissions originated from transportation. By reducing fuel demand, the fuel manufacturer can use the remaining fuel in other profitable industries. The car-scrapping center also buys worn-out cars from customers in cooperation with the government and sells the part of the raw materials that can be reused to the car manufacturer. This problem is modeled under two approaches, static and dynamic, and the results are compared with each other. To face uncertainty, car and fuel demands are considered stochastic with a uniform distribution function. Furthermore, the government's desire for the improvement of fuel-efficient technologies is applied as a government intervention strategy in the car manufacturer decisions. Under this plan, the government contributes to the investment costs of fuel-efficient technology and receives income tax from the car manufacturer. By doing this, the technology is improved and also the demand and profit of the car manufacturer is increased. Several numerical experiments for a real case are examined and the proposed models are evaluated. Finally, in order to extract managerial insights, sensitivity analysis is accomplished on the major parameters of the model.