Thesis (M.Phil.)--City University of Hong Kong, 2005. / "Submitted to Department of Building and Construction in partial fulfillment of the requirements for the degree of Master of Philosophy" Includes bibliographical references (leaves 239-241)

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The second marketing model addresses interpretation issues of traditional multiple dimensional scaling (MDS) model, and has three key features. First, it can provide an explicit meaning to each dimension. Second, it can directly provide a link between brand positions and brand attributes, so that marketing mix can be designed to optimize share or profit. Third, for a case of n brands and m product attributes, we only need to estimate m nonzero parameters to represent all brand positions in the reduced space of d dimensions where d < m. The model is applied to a real data set of thirty four cars and nine attributes. Empirical findings suggest that the proposed model is a viable alternative to existing methods. / This thesis proposes two marketing models. The first model is a multiple criteria ranking (MCR) model. It proposes a consensus approach to solve multiple criteria ranking problems. Marketing decisions frequently involve ranking various subjects using multiple criteria. However, it is difficult to have acceptance from all subjects. The proposed multiple criteria ranking model allows each subject to have a "say" in the evaluation process. All subjects together resolve their conflicts through voting so that consensus ranking can be derived under the majority rule principle. The new ranking procedure can be formulated as a series of mathematical programming models, and illustrated with two cases. First, ranking the research performance of thirty one marketing scholars according to their publications in four leading marketing journals. Second, ranking the performance of one hundred information technology companies. / Lam Pong Yuen. / "June 2000." / Added t.p. in Chinese. / Director: Lau Kin Nam. / Source: Dissertation Abstracts International, Volume: 61-08, Section: A, page: 3259. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2000. / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest dissertations and theses, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Quality Capability Self-diagnosis is a convenient and economical way to assess the performance of an operating quality system, as well as a basis for initiating necessary corrective actions essential to quality improvement and preparation for certification. This paper describes the research leading to the development of a cost-effective and systematic methodology for performing quality capability self-diagnosis. ISO 9000 series standards and the methods used to implement multicriteria system evaluation are employed to provide a sound basis for the development of this quality capability self-diagnosis scheme (QCSDS). The QCSDS has been developed to assist manufacturers in the conduct of quality assurance audits using internal personnel. The methodological structure of QCSDS is presented in two major parts: a regular model and a refined model. The regular model includes: (1) development of quality system auditing criteria, (2) selection of a suitable checklist developed from ISO 9000 series requirements, (3) development of importance weights for applicable criteria, (4) performance measurement, (5) quality system rating, (6) analysis of quality auditing results, and (7) suggestions for improvement. The refined model is developed to strengthen capability of the model and its reliability for confirming the effectiveness of an operating quality system using quality cost analysis, utility theory and regression analysis. A decision support system (QCSDDSS) based on the Quattro Pro spreadsheet is incorporated to facilitate the application of QCSDS. The QCSDDSS development is based on the regular model using ISO 9002 to provide both tabular and graphical displays for performance demonstration and improvement analysis. / Graduation date: 1994

Multiple criteria decision analysis (MCDA) techniques are developed to address challenging classification problems arising in engineering management and elsewhere. MCDA consists of a set of principles and tools to assist a decision maker (DM) to solve a decision problem with a finite set of alternatives compared according to two or more criteria, which are usually conflicting. The three types of classification problems to which original research contributions are made are <ol> <li>Screening: Reduce a large set of alternatives to a smaller set that most likely contains the best choice. </li> <li>Sorting: Arrange the alternatives into a few groups in preference order, so that the DM can manage them more effectively. </li> <li>Nominal classification: Assign alternatives to nominal groups structured by the DM, so that the number of groups, and the characteristics of each group, seem appropriate to the DM. </ol> Research on screening is divided into two parts: the design of a sequential screening procedure that is then applied to water resource planning in the Region of Waterloo, Ontario, Canada; and the development of a case-based distance method for screening that is then demonstrated using a numerical example. <br /><br /> Sorting problems are studied extensively under three headings. Case-based distance sorting is carried out with Model I, which is optimized for use with cardinal criteria only, and Model II, which is designed for both cardinal and ordinal criteria; both sorting approaches are applied to a case study in Canadian municipal water usage analysis. Sorting in inventory management is studied using a case-based distance method designed for multiple criteria ABC analysis, and then applied to a case study involving hospital inventory management. Finally sorting is applied to bilateral negotiation using a case-based distance model to assist negotiators that is then demonstrated on a negotiation regarding the supply of bicycle components. <br /><br /> A new kind of decision analysis problem, called multiple criteria nominal classification (MCNC), is addressed. Traditional classification methods in MCDA focus on sorting alternatives into groups ordered by preference. MCNC is the classification of alternatives into nominal groups, structured by the DM, who specifies multiple characteristics for each group. The features, definitions and structures of MCNC are presented, emphasizing criterion and alternative flexibility. An analysis procedure is proposed to solve MCNC problems systematically and applied to a water resources planning problem.

Multiple criteria decision analysis (MCDA) techniques are developed to address challenging classification problems arising in engineering management and elsewhere. MCDA consists of a set of principles and tools to assist a decision maker (DM) to solve a decision problem with a finite set of alternatives compared according to two or more criteria, which are usually conflicting. The three types of classification problems to which original research contributions are made are <ol> <li>Screening: Reduce a large set of alternatives to a smaller set that most likely contains the best choice. </li> <li>Sorting: Arrange the alternatives into a few groups in preference order, so that the DM can manage them more effectively. </li> <li>Nominal classification: Assign alternatives to nominal groups structured by the DM, so that the number of groups, and the characteristics of each group, seem appropriate to the DM. </ol> Research on screening is divided into two parts: the design of a sequential screening procedure that is then applied to water resource planning in the Region of Waterloo, Ontario, Canada; and the development of a case-based distance method for screening that is then demonstrated using a numerical example. <br /><br /> Sorting problems are studied extensively under three headings. Case-based distance sorting is carried out with Model I, which is optimized for use with cardinal criteria only, and Model II, which is designed for both cardinal and ordinal criteria; both sorting approaches are applied to a case study in Canadian municipal water usage analysis. Sorting in inventory management is studied using a case-based distance method designed for multiple criteria ABC analysis, and then applied to a case study involving hospital inventory management. Finally sorting is applied to bilateral negotiation using a case-based distance model to assist negotiators that is then demonstrated on a negotiation regarding the supply of bicycle components. <br /><br /> A new kind of decision analysis problem, called multiple criteria nominal classification (MCNC), is addressed. Traditional classification methods in MCDA focus on sorting alternatives into groups ordered by preference. MCNC is the classification of alternatives into nominal groups, structured by the DM, who specifies multiple characteristics for each group. The features, definitions and structures of MCNC are presented, emphasizing criterion and alternative flexibility. An analysis procedure is proposed to solve MCNC problems systematically and applied to a water resources planning problem.

In this study, an integrated solution methodology for a general discrete multi-criteria decision making problem is developed based on the well-known outranking method Promethee II. While the methodology handles the existence of interdependency between the criteria, it can also incorporate the prospect theory in order to correctly reflect the decision behavior of the decision maker. A software is also developed for the application of the methodology and some applications are performed and presented.

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In this dissertation, we focus on the problem of self-management in distributed systems. In this context, we propose a new methodology for reactive self-management based on multiple criteria decision analysis (MCDA). The general structure of the proposed methodology is extracted from the commonalities of the former well-established approaches that are applied in other problem domains. The main novelty of this work, however, lies in the usage of MCDA during the reaction processes in the context of the two problems that the proposed methodology is applied to. In order to provide a detailed analysis and assessment of this new approach, we have used the proposed methodology to design distributed autonomous agents that can provide self-management in two outstanding problems. These two problems also represent the two distinct ways in which the methodology can be applied to self-management problems. These two cases are: 1) independent self management, and 2) coordinated self-management. In the simulation case study regarding independent self-management, the methodology is used to design and implement a distributed resource consolidation manager for clouds, called IMPROMPTU. In IMPROMPTU, each autonomous agent is attached to a unique physical machine in the cloud, where it manages resource consolidation independently from the rest of the autonomous agents. On the other hand, the simulation case study regarding coordinated self-management focuses on the problem of adaptive routing in mobile ad hoc networks (MANET). The resulting system carries out adaptation through autonomous agents that are attached to each MANET node in a coordinated manner. In this context, each autonomous node agent expresses its opinion in the form of a decision regarding which routing algorithm should be used given the perceived conditions. The opinions are aggregated through coordination in order to produce a final decision that is to be shared by every node in the MANET. Although MCDA has been previously considered within the context of artificial intelligence---particularly with respect to algorithms and frameworks that represent different requirements for MCDA problems, to the best of our knowledge, this dissertation outlines a work where MCDA is applied for the first time in the domain of these two problems that are represented as simulation case studies. / Graduate

This study is concerned with a sorting problem / the placement of alternatives into preference classes in the existence of multiple criteria. An interactive model is developed to address the problem, assuming that the decision maker has an underlying utility function which is linear. A recent methodology, Even-Swaps, which is based on value tradeoff is utilized in the model for both making an estimation of the underlying utility function and generating possible dominance among the alternatives on which it is performed. Convex combinations, dominance relations, weight space reduction, Even-Swaps and direct decision maker placements are utilized to place alternatives in preference classes. The proposed algorithm is experimented with randomly generated alternative sets having different characteristics.