Transportation planning for sustainable supply chain network using big data technology

Abstract

The popularization of big data technology has a great impact on decision-making and optimization of a sustainable supply chain network. An integrated transportation planning and product retailing decision is a typical optimization problem in the sustainable supply chain network, especially in the setting of subjective uncertainty. Carbon regulatory and environmental changes around the world, as well as growing consumer concerns about carbon emissions, are forcing supply chain managers to redefine carbon emissions. This paper uses big data technology to investigate an integrated transportation planning and retailing optimization problem of supply chain network under different carbon regulatory policies. Different from previous literature, we adopt the uncertainty theory to deal with the uncertainty in big data information and characterize the parameters in the integrated optimization problem as uncertain variables. Four uncertain optimization models under different carbon regulatory policies are established, and their equivalent forms and mathematical properties are discussed based on the inverse uncertainty distribution. The impacts of the different carbon regulatory policies on supply chain performance are analyzed through computational experiments. The research results can not only help supply chain managers make transportation planning and product retailing decisions in the sustainable supply chain network, but also assist governments to formulate effective carbon regulatory policies.