On Closed-loop Supply Chain and Routing Problems for Hazardous Materials Transportation with Risk-averse Programming, Robust Optimization and Risk Parity

Abstract

The objective of this dissertation is to develop mathematical optimization models that assist and improve the decision making process in hazardous materials (hazmat) routing and supply chain network design. First, a mathematical model for hazmat closed-loop supply chain network design problem is proposed. The model, which can be viewed as a way to combine a number of directions previously considered in the literature, considers two echelons in forward direction (production and distribution centers), three echelons in backward direction (collection, recovery and disposal centers) and emergency team positioning with objectives of minimizing the strategic, tactical and operational costs as well as the risk exposure on the road network. Since the forward flow of hazmat is directly related to the reverse flow, and since hazmat accidents can occur in all stages of lifecycle (storage, shipment, loading and unloading, etc), it is argued that such a unified framework is essential. The resulting model is a complicated multiobjective mixed integer programming problem. It is demonstrated how it can be solved with a two-phase solution procedure on a case study based on a standard dataset from Albany, NY. Second, the uncertainties of model parameters such as demand and return are considered. With a known distribution for the uncertain data, a two-stage stochastic optimization model is developed, and its performance is studied on the same case study. A robust optimization framework is developed for the same problem in a case where the distributions of demand and return are unknown. The model characteristics and performance are presented based on the Albany case study. Other than the demand and return, risk exposure on the road network during hazmat transportation can have uncertainty. Third, the risk involved in hazmat transportation is taken into account, where Risk Parity idea in conjunction with modern risk-averse stochastic optimization (namely coherent measures of risk) are studied. A generalized Risk Parity model is studied, and a combined two-stage diversification-risk framework is proposed. The results of a numerical case study on hazmat routing problem under heavy-tailed distributions of losses are outlined. The model aims to fairly distribute the hazmat shipment amounts on the road network and promote risk equity on the involved communities.