A Computational Approach to Analyzing Supply Chain Sustainability

Abstract

This thesis utilizes a convex optimization control policy (COCP) to analyze the supply chain processes within a global inter-country network and their effects on the climate change crisis. While annual greenhouse gas (GHG) emissions have skyrocketed over the past century, emissions per capita have remained constant, prompting the rise of potentially devastating consequences to our health, environment, food supply, and economy. Given this climate change emergency, the implementation of sustainable environmental practices and models for analyzing exceedingly intricate supply chain networks will be crucial in lowering GHG emissions globally. That said, in this paper, we train a supply chain model that implements a COCP to minimize a cost function such that it maximizes the profit achievable for a certain good in addition to minimizing GHG emissions within an intercountry network. After training the supply chain model, we identify Pareto optimal solutions that take into account both profit and GHG emissions, ultimately proving better decision-making for the storage and transportation of goods. By evaluating the performance of our COCP, this thesis demonstrates the improvements that can be made throughout a supply chain network, with a focus on environmental impacts, while also illustrating a complete picture of the relationship between GHG emissions, profit, industry type, and countries within a network.