Value of agreement in decision analysis: concept, measures and application

In multi-criteria decision analysis workshops, participants often appraise the options individually before discussing the scoring as a group. The individual appraisals lead to score ranges within which the group then seeks the necessary agreement to identify their preferred option. Preference programming enables some options to be identified as dominated even before the group agrees on a precise scoring for them. Workshop participants usually face time pressure to make a decision. Decision support can be provided by flagging options for which further agreement on their scores seems particularly valuable. By valuable, we mean the opportunity to identify other options as dominated (using preference programming) without having their precise scores agreed beforehand. The present paper quantifies this Value of Agreement and extends the concept to portfolio decision analysis and criterion weights. The new concept is validated through a case study in recruitment

Domination and Decomposition in Multiobjective Programming

During the last few decades, multiobjective programming has received much attention for both its numerous theoretical advances as well as its continued success in modeling and solving real-life decision problems in business and engineering. In extension of the traditionally adopted concept of Pareto optimality, this research investigates the more general notion of domination and establishes various theoretical results that lead to new optimization methods and support decision making. After a preparatory discussion of some preliminaries and a review of the relevant literature, several new findings are presented that characterize the nondominated set of a general vector optimization problem for which the underlying domination structure is defined in terms of different cones. Using concepts from linear algebra and convex analysis, a well known result relating nondominated points for polyhedral cones with Pareto solutions is generalized to nonpolyhedral cones that are induced by positively homogeneous functions, and to translated polyhedral cones that are used to describe a notion of approximate nondominance. Pareto-oriented scalarization methods are modified and several new solution approaches are proposed for these two classes of cones. In addition, necessary and sufficient conditions for nondominance with respect to a variable domination cone are developed, and some more specific results for the case of Bishop-Phelps cones are derived. Based on the above findings, a decomposition framework is proposed for the solution of multi-scenario and large-scale multiobjective programs and analyzed in terms of the efficiency relationships between the original and the decomposed subproblems. Using the concept of approximate nondominance, an interactive decision making procedure is formulated to coordinate tradeoffs between these subproblems and applied to selected problems from portfolio optimization and engineering design. Some introductory remarks and concluding comments together with ideas and research directions for possible future work complete this dissertation

Multiple Criteria Decision Support; Proceedings of an International Workshop, Helsinki, Finland, August 7-11, 1989

Multiple Criteria Decision Making has been an important and active research area for some 20 years. In the 1970's, research focused on the theory of multiple objective mathematical programming and on procedures for solving multiple objective mathematical programming problems. During the 1980's, a shift in emphasis towards multiple criteria decision support was observed. Accordingly, much research has focused on the user interface, the behavioral foundations of decision making, and on supporting the entire decision-making process from problem structuring to solution implementation. Because of the shift in research emphasis the authors decided to make "Multiple Criteria Decision Support" the theme for the International Workshop, which was held at Suomen Saeaestoepankkiopisto in Espoo, Finland. The Workshop was organized by the Helsinki School of Economics, and sponsored by the Helsinki School of Economics and IIASA, Austria. This volume provides an up-to-date coverage of the theory and practice of multiple criteria decision support. The authors trust that it will serve the research community as well as the previously published Conference Proceedings based on IIASA Workshops

Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding

Optimization is considered as a decision-making process for getting the most out of available resources for the best attainable results. Many real-world problems are multi-objective or multi-attribute problems that naturally involve several competing objectives that need to be optimized simultaneously, while respecting some constraints or involving selection among feasible discrete alternatives. In this Reprint of the Special Issue, 19 research papers co-authored by 88 researchers from 14 different countries explore aspects of multi-objective or multi-attribute modeling and optimization in crisp or uncertain environments by suggesting multiple-attribute decision-making (MADM) and multi-objective decision-making (MODM) approaches. The papers elaborate upon the approaches of state-of-the-art case studies in selected areas of applications related to sustainable development decision aiding in engineering and management, including construction, transportation, infrastructure development, production, and organization management

Product Design Selection with Variability for an Implicit Value Function

Often in engineering design selection there is no one design alternative that is better in terms of all attributes, and the preferred design(s) is dependent on the preferences of the Decision Maker (DM). In addition, there is always uncontrollable variability, which is mainly of two types, that has to be accounted for. The first type, preference variability, is caused due to the DM's lack of information on end users' needs. The second type, attribute variability, is caused due to uncontrollable engineering design parameters like manufacturing errors. If variability is not accounted for, the preferred design(s) found might be erroneous. Existing methods presume an explicit form for the DM's "value function" to simplify this selection problem. But, such an assumption is restrictive and valid only in some special cases. The objective of this research is to develop a decision making framework for product design selection that does not presume any explicit form for the DM's value function and that accounts for both preference and attribute variability. Our decision making framework has four research components. In the first component, Deterministic Selection, we develop a method for finding the preferred design(s) when the DM gives crisp preference estimates, i.e., best guess of actual preferences. In the second component, Sensitivity Analysis, we develop a method for finding the allowed variation in the preference estimates for which the preferred design(s) do not change. In the third component, Selection with Preference Variability, we develop a method for finding the preferred design(s) when the DM gives a range of preferences instead of crisp estimates. Finally, in our fourth component, Selection with Preference and Attribute Variability, we develop a method in which the DM gives a range of values for attributes of the design alternatives in addition to a range for preferences. We demonstrate the methods developed in each component with two engineering examples and provide numerical experimental results for verification. Our experiments indicate that the preferred design(s) found in our first, third, and fourth components always include the actual preferred design(s) and that our second component finds the allowed variation in preference estimates efficiently