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Friday, June 19, 2015
09:30 AM - 10:30 AM
CNLS Conference Room (TA-3, Bldg 1690)


Models to Optimize the Design and Management of Biomass Supply Chain

Sandra D. Eksioglu
Industrial Engineering Department, Clemson University

During this presentation we will discuss two studies related to optimizing the design and management of the biomass supply chain. The first study presents a model that optimizes biomass co-firing decisions in coal-fired power plants. The second study presents a model that optimizes the design of a multi-modal transportation network for a biomass supply chain system, where intermodal hubs are subject to probabilistic disruptions.

I. Co-firing biomass in coal-fired power plants is a strategy used to reduce greenhouse gas emissions. We present a mathematical model that integrates decisions about biomass purchasing, transportation and plant investment. The model captures the loss in process efficiencies due to using biomass, a product which has lower heating value as compared to coal. The objective is to identify a co-firing strategy at the plant level to optimize savings due to production tax credit (PTC), savings from reducing the amount of coal used, as well as, production and transportation costs. We formulate the problem as a mixed integer nonlinear program; and provide two linear approximations of this problem which are easier to solve. We use these approximations to derive lower and upper bounds, and conduct extensive numerical analysis to evaluate the quality of these bounds. We develop a case study using data from nine states located in the southeast region of USA. Via our numerical analysis we observe the following: (a) Incentives such as PTC are necessary in order to increase production of renewable energy. (b) The PTC should not be “one size fits all”. Instead, tax credits could be a function of plant capacity, or the amount of renewable electricity produced.

II.This study presents a mathematical model to design a multi-modal transportation network for a biomass supply chain system, where intermodal hubs are subject to probabilistic disruptions. The model determines the location of intermodal hubs, and the traffic flows along multi-modal terminals (including waterway ports, railroad ramps and truck terminals). The disruption probabilities of intermodal hubs are estimated using historical data. We developed an accelerated Benders decomposition algorithm to solve this challenging NP-hard problem. We applied the model to a case study using data from the southeast region of U.S. The numerical experiments show that this customized algorithm can solve large scale problem instances to a near-optimal solution in a reasonable time. A number of managerial insights are drawn into the impact of intermodal-related risk on the supply chain performance.

Host: Russell Bent