Approximate Dynamic Programming Based Approaches for Green Supply Chain Design

Nowhere are low emission operations more important than in logistics to remote pristine locations such as within the Arctic Circle. This study focuses on a long term economic assessment of research sites in Greenland from the logistics perspective. We study the strategic supply chain design problem as a bi-objective optimization problem, where cost and carbon footprint must be controlled. We model the problem as a time-spaced multi-commodity network flow problem with inventory tracking. To solve the multi-year model efficiently, we deploy an approximate dynamic programming (ADP) algorithm based on an approximation of the value function. To establish the efficient frontier between cost and emissions, an ADP based two phase algorithm is designed. We also compare the ADP based algorithms with traditional integer programming based algorithms. Computational results show that the ADP based two phase algorithm is more efficient and robust in determining the efficient frontier. We also show how these algorithms provide guidance for the operations of the logistics network.