Graduate Thesis Or Dissertation
 

Multiobjective decision making in industrial energy and environmental planning

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/2j62s726m

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  • Multiobjective Decision Making (MODM) has been suggested for the solution of complicated decision problems. Decision analysis in numerous areas, including industrial energy and environmental planning, necessarily requires consideration of multiple conflicting objectives. MODM has been successfully applied to a number of these problems of this type. Moreover, it has the ability to deal with both quantitative and qualitative factors, each which involve different units of measurement. The objective of this study is to introduce a MODM process for energy and environmental planning problems in forest products manufacturing industries. Throughout the analytic process, the posteriori articulation of decision maker's (DM) preferences is assumed. This mandates development of two procedures: (1) the generation of nondominated solutions and (2) evaluation of the solutions by DM judgement to determine the final, best-compromised solution. For the first procedure, a Multiobjective Linear Programming (MOLP) model is introduced, formulated as a prototype example through the examination of fuel-mix options. Three objectives are observed in the MOLP model, including: (1) total energy costs, (2) environmental impacts, and (3) business and performance risks. In order to overcome the complexities caused by the use of different qualitative units of measurement, factors (2) and (3) are quantified in numerical values. The constraint method is then applied for the generation of nondominated solutions. As the second procedure, an evaluation procedure which includes multiple screening methods is proposed for ease of problem application for consideration of a large number of alternatives. This methodology is based on rating and pairwise comparison methods. Special emphasis is placed on the achievement of a higher DM level of confidence when the final solution is selected. The methodology can be divided into two regions: (1) step-by-step reduction of alternatives, and (2) judgmental options for upgrading DM confidence. This methodology provides a useful and flexible tool for problems as characterized above and for large-scale problems.
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