by Alexander H. Levis. / Bibliography: p. 24-25. / "October, 1983." / Office of Naval Research contract ONR/N00014-83-K-0185 Army Research Institute for Behavioral and Social Sciences contract MDA903-83-C-0196

by John Tsitsiklis and Michael Athans. / Bibliography: leaf 4. / "November, 1983." Caption title. / ONR contract ONR/N00014-77-C-0532 (NR-041-519)

by David A. Casta? / Prepared under ONR Contract no. N00014-76-C-0346. Originally presented as the author's thesis, (Ph.D.), M.I.T. Dept. of Mathematics, 1976. / Bibliography: p.142-147.

Kevin L. Boettcher and Robert R. Tenney. / "October 1985." Caption title. / Bibliography: leaf [6]. / Office of Naval Research grant ONR/N00014-77-C-0532 (NR 041-519) ONR/N00014-84-K-0519 (NR 649-003)

R. Paul Wiley and Robert R. Tenney. / Caption title. / Bibliography: leaf [6]. / Office of Naval Research grant ONR/N00014-77-C-0532 (NR 041-519) ONR/N00014-84-K-0519 (NR 649-003)

Wu, Xiaobing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves [48]-51). / Abstract also in Chinese. / Thesis Committee --- p.i / Abstract --- p.ii / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Thesis Organization --- p.7 / Chapter 2 --- Related Work --- p.9 / Chapter 2.1 --- Static Skyline --- p.9 / Chapter 2.2 --- Monochromatic Reverse Skyline --- p.11 / Chapter 2.3 --- Other Skyline-Related Topics --- p.13 / Chapter 2.4 --- Bichromatic Reverse Nearest Neighbor --- p.14 / Chapter 3 --- Basic Properties of Bichromatic Reverse Skyline --- p.16 / Chapter 3.1 --- Bichromatic Reverse Skyline --- p.16 / Chapter 3.2 --- Basic Properly --- p.19 / Chapter 4 --- The BRS Algorithm --- p.24 / Chapter 4.1 --- Midway Conversion --- p.24 / Chapter 4.2 --- Heuristics --- p.27 / Chapter 4.3 --- The Algorithm --- p.32 / Chapter 5 --- Any Mixture of Bi- and Uni-directional Dimensions --- p.37 / Chapter 6 --- Experiments --- p.40 / Chapter 6.1 --- Bi-directional Retrieval --- p.40 / Chapter 6.2 --- Mixture of Bi- and Uni-directional Axes --- p.44 / Chapter 7 --- Conclusions --- p.46 / Chapter A --- List of Publications --- p.47 / Bibliography --- p.48

Submitted in fulfillment of the requirements for the Master's Degree in Information and Communication Technology, Durban, University of Technology, Durban, South Africa, 2017. / This dissertation reports on the development of an algorithm based on an existing matchmaking method to solve diverse decision problems in a multi-person environment. The capacity to effectively achieve a lucrative and accurate decision making is a critical aspect of resource management. But the accuracy of a decision making process can be highly compromised because of the high subjectivity and multiple conflicting attributes that are present in human judgement. multi-person decision making is an effective approach for achieving a lucrative and accurate decision making process. The multi-person decision process has proven to be tedious mainly because the existing multi-person decision making methods are extensions of single decision making methods. This imposes additional computational resources, especially for a large number of decision makers because they aggregate the preferences of several decision makers into a unified format.This work therefore seeks to improve the multi-person decision making process using a matchmaking approach. In doing so, the Hunt ForTune matchmaking algorithm was investigated and improved for this purpose. Thus, the preferences of decision makers for each attribute are collected as an attribute description vector. The attribute, its description vector, flexibility and priority vector are compactly represented as a 4-tuple profile. The improved Hunt ForTune matchmaking algorithm is applied to different sets of multi-person decision problems and offered as an effective way of enhancing decision accuracy. The improved matchmaking decision algorithm is compared with a novel mathematical technique of Hausdorff distance. Results generally show that multi-person matchmaking algorithm is suitable and efficient for diverse decision making in the presence of multiple decision makers. The practical implication of the proposed multi-person matchmaking algorithm for decision making is that it provides a less complicated way to capture and represent the preferences of multiple decision makers irrespective of decision domain. The originality of the work reported in this dissertation is built on a matchmaking algorithm by introducing effective profile representation using vector analysis approach to capture the preferences of multiple decision makers and similarity metrics to provide an efficient and robust way to accurately perform a multi-person decision process. / M

Ph.D. (Business Management) / Please refer to full text to view abstract

This research develops an algorithm for solving a class of multiple objective decision problems. These problems are characterized by continuous policy variables, nonlinear constraints, and nonlinear criterion functions. Our underlying philosophy is that of the Gestalt psychologists-- we cannot separate the problem and its solution from the environment in which the problem is placed. The decision maker is necessarily a part of this environment, thus implying that he, as an individual, must be part of the solution of the problem. Another central assumption in this research is that there is not an "optimal" answer to the problem, only "satisfactory" solutions. The reasons for this are based partly on the insensitivities of the body to minute changes and to the insensitivity of our preferences within certain ranges of acceptance. In addition, we assure that the individual is capable of solving decision situations involving a maximum of about 10 goals and that he operates upon them in some sort of serial manner as he searches for a satisfactory alternative. The serial manner is a reflection of his current ranking of the goals. Based on these assumptions we have developed a cyclical interactive algorithm in which the decision maker guides a search mechanism in attempting to find a satisfactory alternative. Each cycle in the search consists of an optimization phase and an evaluation phase, after which the decision maker can define a new direction of search or terminate the algorithm. The optimization phase is based on a linearization technique which has been quite effective in terms of the problems we have attempted to solve. It is capable of solving general nonlinear programming problems with a large number of nonlinear constraints. Although the constraint set must be convex in order to guarantee the location of a global optimum, we can use the method on concave sets recognizing that we may find only a local optimum. An extensive synthetic case study of a water pollution decision problem with 6 conflicting goals is provided to demonstrate the feasibility of the algorithm. Finally, the limitations of the research are discussed. We tentatively conclude that we have developed a method applicable to our research problem and that the method can be applied to "real world" decision situations.

This dissertation contains a discussion concerning the validity of the principle of optimality and the dynamic programming algorithm in the context of discrete time and state multistage decision processes. The multistage decision model developed for the purpose of the investigation is of a general structure, especially as far as the reward function is concerned. The validity of the dynamic programming algorithm as a solution method is investigated and results are obtained for a rather wide class of decision processes. The intimate relationship between the principle and the algorithm is investigated and certain important conclusions are derived. In addition to the theoretical considerations involved in the implementation of the dynamic programming algorithm, some modeling and computational aspects are also investigated. It is demonstrated that the multistage decision model and the dynamic programming algorithm as defined in this study provide a solid framework for handling a wide class of multistage decision processes. The flexibility of the dynamic programming algorithm as a solution procedure for nonroutine reservoir control problems is demonstrated by two examples, one of which is a reliability problem. To the best of the author's knowledge, many of the theoretical derivations presented in this study, especially those concerning the relation between the principle of optimality and the dynamic programming algorithm, are novel.