Date of Award:


Document Type:


Degree Name:

Doctor of Philosophy (PhD)


Electrical and Computer Engineering

Department name when degree awarded

Electrical Engineering


Ronald L. Thurgood


Many professions occasionally involve the selection of an alternative from among many problem solutions which have impacts in multiple-interest areas; however, due to the very nature of his work, the practicing engineer, regardless of specialty, is unavoidably engaged in this selection process. The emergence of national concern for environmental and social consequences of technical enterprises, as reflected through legislative action, has accentuated the need for multicriteria design methodologies in some areas of engineering (i.e., automotive). Consequently, interest in the development of pragmatic and theoretically sound approaches to multi-impact design situations has been keen. Any approach to multicriteria design/decision problems involves two fundamental aspects: (1) generating information regarding the range of possible designs and their associated impacts; and (2) generating relative value information which is used to compare the relative imp-cats leading to the selection of a "preferred" or "best compromise" alternative. The methodology developed herein is the integration of a formal mathematical programming technique for generating the full range of feasible alternatives with a pragmatic and well-accepted group-interaction technique for extracting value information regarding alternatives. The integration results in an iterative group-interaction process which leads to successive reductions in the preferred range of alternatives until the most preferred alternative is identified.

The methodology developed in this research represents an improvement over other methodologies reported in the literature in two areas: 1) The noninferior set is explicitly identified insuring selection of a group decision point which is noninferior, 2) a least squared error mathematical filtering technique is developed for smoothing relative value data obtained from the decision making body. In addition, a convergence proof is developed which not only indicates the theoretically sound and robust nature of the algorithm developed in this work but in addition provides a basis for an improved class of algorithms for solving classical nonlinear constrained problems. The technique was developed for and implemented in an interactive software package. The multiobjective decision problem is solved in a single encounter with a cooperative decision making group.