(448a) A Decision Support Framework for Strategic Decision Assessment of a Sustainable Biorefinery

In recent years there has been a marked surge in the search for alternative sources of energy that wean the world off of dependence on fossil fuels and reduce our carbon foot-print on this world. After a boom in U.S. corn-based ethanol in the early part of the 21st century, the interest has gradually shifted towards more viable sources of biofuels and biochemicals. Cellulosic ethanol, biodiesel, and syngas are examples of such fuels that are extremely attractive owing to the fact that the raw materials can be composed completely of ?left-over? wastes of food crops and forest harvests that don't interfere with the human food chain and the natural ecosystem.

Even with increased research emphasis on biofuel and biochemical production technologies, large scale production is still hampered by many factors. Some of these factors that have received considerable interest in recent times include, cultivating more robust feedstock, genetically engineering enzymes and microbes that are more efficient and resistant to poisoning by by-products and improving process technologies that can render large scale production of biofuels a more profitable option for private entities. One area that has not received enough research interest in recent times is enterprise wide modeling and optimization in order to make the entire enterprise more competitive in the energy sector.

A sustainable enterprise is often defined as an enterprise that does not have a negative socio-environmental impact on the society. We further refine this definition to encompass not only the ability to positively impact the environment, but also maintaining such an impact through value creation and profitability. An enterprise is defined as being sustainable if it produces goods and services that benefit our environment and is able to preserve such an influence through continued growth. Modeling an enterprise that produces renewable-based fuels and chemicals can provide valuable insight into the inter-play of the bioproduct supply-chain. This idea had been motivated by a recent surge in the research areas concerning supply-chain optimization and how cost-cutting measures throughout a supply-chain can render an otherwise sluggish enterprise, profitable.

Our work tries to develop a systematic framework, within which strategic decisions a biofuels enterprise makes, can be evaluated. The framework developed is an optimization-based framework which integrates capital budgeting decisions with operational constraints. The decision making process models the actual corporate structure of a process enterprise and is broken down into two concrete steps; the first step acts as a screening model which provides a preliminary network topology and enterprise portfolio choices (raw materials, supplier, product, technology) to the second step which further evaluates each node and portfolio choice in some detail to arrive at the final topology and portfolio decisions.

The first step in the framework is a deterministic strategic optimization model with a rolling time horizon of 10 years and semi-annual time steps. The problem is a typical network and capacity design problem with added budgeting constraints and product and technology portfolio considerations. The objective function used in the model is the company's shareholder value. The constraints and parameters introduced in the model include amongst others, mass balances on supply chain nodes, raw material availability constraints, production and distribution capacity constraints, key performance indicators, tax subsidy considerations, and loan obligations. The model decision variables include asset acquisition, plant capacity design and increments, feedstock selection with seasonal considerations, semi-annual production and inventory targets, market selection, sales forecasts, and distribution network design. The model will act as a screening model whose results will be fed into the second step of the framework for detailed analysis.

The second step of the framework is a real-options based analysis tool formulated as a MILP problem. Real options analysis is a new paradigm in engineering that borrows techniques from the more established financial options analysis to evaluated assets typically possessed by any manufacturing enterprise. Such analysis has been shown to have practical importance for cases where model parameters have a high degree of uncertainty. A biorefinery of the future will be plagued by uncertainty in raw material prices and availability, product demands and prices, and technological evolution. In such cases, the option to drop (or carry out) a certain action at any point in time has intrinsic value (called real-options value or ROV) and the proposed module seeks to maximize the value of this option. A binomial tree is used to generate scenarios for uncertain parameters and a dynamic programming approach is used to evaluate the network and portfolio decisions at each time step. The candidate network solutions yielded by the screening model are fed into this module and timing decisions for entry into new product and technology markets, raw material supplier selection, and capacity planning decisions are evaluated using this tool.

The outcome of such an approach is topological and portfolio choices that are evaluated using state of the art tools. With mounting competitiveness in the energy sector it will be essential for a biorefining enterprise to assess accurately the options they have for raw material, technology, and product choices. Entry into any market will entail a large capital investment up front and the operation of the enterprise will evolve over time almost certainly with changing market dynamics. Consequently traditional approaches for project appraisal such as the NPV approach will fail to represent an accurate picture since they are static in nature and do not consider uncertainty in model parameters. Hence the systematic approach aforementioned can provide the firm with an essential tool that can be exploited to assess long-term decisions that the enterprise will have to ?live-with? once they are made.