Clustering and Group Selection of Multiple Criteria Alternatives with Application to Space-based Networks

All rights reserved. This study, or parts thereof, may not be reproduced in any form without written permission of the authors. In many real world problems the range of consequences of different alternatives are considerably different. Also, sometimes, selection of a group of alternatives (instead of only one best alternative) is necessary. Traditional decision making approaches treat the set of alternatives with the same method of analysis and selection. In this paper, we propose clustering alternatives into different groups so that different methods of analysis, selection, and implementation for each group can be applied. As an example, consider the selection of a group of functions (or tasks) to be processed by a group of processors. The set of tasks can be grouped according to their similar criteria, and hence each cluster of tasks to be processed by a processor. The selection of the best alternative for each clustered group can be performed using existing methods; however, the process of selecting groups is different than the process of selecting alternatives within a group. We develop theories and procedures for clustering discrete multiple criteria alternatives. We also demonstrate how the set of alternatives is clustered into mutuall

Risk-based decision support system for life cycle management of industrials plants

Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de ComputadoresThe objective of this thesis is to contribute for a better understanding of the decision making process in industrial plants specifically in situations with impact in the long term performance of the plant. The way decisions are made, and especially the motivations that lead to the selection of a specific course of action, are sometimes unclear and lack on justification. This is particularly critical in cases where inappropriate decisions drive to an increase on the production costs. Industrial plants are part of these cases, specifically the ones that are still lacking enhanced monitoring technologies and associated decision support systems. Maintenance has been identified as one of the critical areas regarding impact on performance. This is due to the fact that maintenance costs still represent a considerable slice of the production costs. Thus, understanding the way maintenance procedures are executed, and more important, the methods used to decide when maintenance should be developed and how, have been a concern of decision makers in industrial plants. This thesis proposes a methodology to efficiently transform the existing information on the plant behaviour into knowledge that may be used to support the decision process in maintenance activities. The development of an appropriate knowledge model relating the core aspects of the process enables the extraction of new knowledge based on the past experience. This thesis proposes also a methodology to calculate the risk associated to each maintenance situation and, based on the possible actions and on the impacts they may have in the plant costs performance, suggests the most appropriate course. The suggestion is made aiming the minimization of the life cycle costs. Results have been validated in test cases performed both at simulation and real industrial environments. The results obtained at the tests cases demonstrated the feasibility of the developed methodology as well as its adequateness and applicability in the domain of interest

Multiple Criteria Decision Analysis: Classification Problems and Solutions

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 Screening: Reduce a large set of alternatives to a smaller set that most likely contains the best choice. Sorting: Arrange the alternatives into a few groups in preference order, so that the DM can manage them more effectively. 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. 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. 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. 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