Abstract

The global warming, which is caused by increasing concentrations of carbon emissions, mainly results from human activities such as fossil fuel burning and deforestation. In order to alleviate global warming and its adverse effects, many countries including the United States and the European Union members have attempted to enact legislation or design market-based carbon trading mechanism for controlling carbon emission. Analyzing the impact of such governmental legislations on supply chain operations has particularly been noticed both in theory and practice. This implies that firms need to incorporate the governmental regulations into their decision making process. This thesis presents an integrated model of production-distribution planning in supply chains considering congestion and carbon emission capacity constraints. The objective of the model is to minimize the sum of production, inventory, and transportation cost subject to emission capacity constraints. Our model adopts a Carbon Cap regulation policy that requires the total carbon emission resulting from production and distribution of commodities from facilities to demand points to be constrained. Considering congestion at the production facilities for work in process (WIP) inventory, which may increase nonlinearly after a certain level of utilization (i.e. critical utilization), leads to a nonlinear multi-period mixed integer program. We then develop a robust approach that captures the uncertainty in estimating the emission of each of the logistic activities. We propose a Lagrangian relaxation approach and a heuristic to build feasible solutions which solves large instances. Finally, computational results on a set of instances are reported to assess the performance of the proposed MIP formulation and of our algorithmic approach.