BeSmart2: A multicriteria decision aid application

This paper presents an improved version of an application whose goal is to provide a simple and intuitive way to use multicriteria decision methods in day-to-day decision problems. The application allows comparisons between several alternatives with several criteria, always keeping a permanent backup of both model and results, and provides a framework to incorporate new methods in the future. Developed in C#, the application implements the AHP, SMART and Value Functions methods

Assessing the quality of the relation between scalarizing function parameters and solutions in multiobjective optimization

Different Multi-Objective Optimization Methods (MOOM) for solving Multi-Objective Optimization Problems (MOOP) have been suggested in the literature. These methods often comprise two stages (not necessarily sequential): i) the search for the Pareto-optimal set and ii) the selection of a single solution from this non-dominated set. Various studies comparing performance of particular aspects of these methods have been carried out. However, a theoretical support that changes on the preferences of a Decision Maker (DM) will be reflected in the same way on the solution of the MOOP given by the MOOM has not been presented. In this work a consistency measure to assess MOOM is proposed. It will used to compare the performance of different methods available in the literature. This study was performed using some benchmark test problems, with two criteria.This work was supported in part by the Portuguese Fundacao para a Ciencia e Tecnologia under grants: PTDC/GES/70168/2006 and SFRH/BD/22808/2005

Scenario-based portfolio model for building robust and proactive strategies

In order to address major changes in the operational environment, companies can (i) define scenarios that characterize different alternatives for this environment, (ii) assign probabilities to these scenarios, (iii) evaluate the performance of strategic actions across the scenarios, and (iv) choose those actions that are expected to perform best. In this paper, we develop a portfolio model to support the selection of such strategic actions when the information about scenario probabilities is possibly incomplete and may depend on the selected actions. This model helps build a strategy that is robust in that it performs relatively well in view of all available probability information, and proactive in that it can help steer the future as reflected by the scenarios toward the desired direction. We also report a case study in which the model helped a group of Nordic, globally operating steel and engineering companies build a platform ecosystem strategy that accounts for uncertainties related to markets, politics, and technological development

On the decomposition of Generalized Additive Independence models

The GAI (Generalized Additive Independence) model proposed by Fishburn is a generalization of the additive utility model, which need not satisfy mutual preferential independence. Its great generality makes however its application and study difficult. We consider a significant subclass of GAI models, namely the discrete 2-additive GAI models, and provide for this class a decomposition into nonnegative monotone terms. This decomposition allows a reduction from exponential to quadratic complexity in any optimization problem involving discrete 2-additive models, making them usable in practice

Value driven conceptual design of Unmanned Air System for defence applications

The work presented concerns the development of a value driven conceptual design assessment framework for a small Unmanned Air System (UAS) to be utilized in a defence application. In the field of Multi-Disciplinary Design Optimization, most recent systematic search has been devoted to fixed topology parametric geometries, pertaining to a single concept, with very little stress put on the optimization of variable topologies describing alternative design concepts. The search is conducted in a highly novel manner, generating a broad range of combinations of UAS configurations and geometries by systematically searching alternative concepts and design configurations through the parameterization of the aircraft geometric topologies. Moreover, the “value” of proposed solutions is assessed in an objective way both from performance and economic perspectives, while the optimal solution is identified after relaxing all of the design constraints as advocated by value driven design philosophy. During the multi-criteria decision analysis, the quantification/conversion of the linguistic preferences of the user between the various attributes to numerical values has disclosed some deficiencies introduced by the unjustifiable numerical scales used in the Analytic Hierarchy Process (AHP). This problem is resolved by a novel value model synthesizing the AHP assessment methodologies with multi-attribute value-focused analysis

Desirability–doability group judgment framework for the collaborative multicriteria evaluation of public policies

Desirability–doability framework (2 × D) is a novel framework for the collaborative evaluation of public policies. Fundamental objectives and performance indicators are agreed upon in workshops, policies are characterised, and barriers to implementation identified. MACBETH interactive protocols are then applied in decision conferences to elicit qualitative judgments about the desirability of policies, within and across objectives; and about their doability under the expected graveness of barriers on contrasting scenarios. Elicited judgments allow, respectively, to construct a shared multicriteria model measuring the overall desirability of policies; and, to measure their doability. Desirability–doability graphs enable visual interactive classification of policies, with sensitivity/robustness analyses of uncertainties. 2 × D was successfully tested in a real-world urban-health policymaking case to evaluate spatial policies. The main novelty of 2 × D is that it bridges the socio-technical gap, present in OR, between the support required by a complex social decision-making process, and that usually offered by analytic techniques – while keeping modeling theoretically sound and simple

QALYs and HYEs: Under what conditions are they equivalent?

The paper examines what restrictions have to be imposed on the individual's preference structure for QALYs and HYEs to yield identical results. It is shown that using QALYs involves imposing three additional restrictions. Empirical evidence suggests that these restrictions cannot be expected to hold in all applications. The main problem in using HYEs appears to be practical. An alternative index is proposed, that may help to bridge the gap between QALYs and HYEs by combining to some extent the advantages of the two measures

Using DEA Factor Efficiency Scores to Eliminate Subjectivity in Goal Programming

Many real-world problems require decision makers to consider multiple criteria when performing an analysis. One popular method used to analyze multicriteria decision problems is goal programming. When applying goal programming, it is often difficult if not impossible to determine the target values and unit penalty weights with any level of confidence. Thus, in many situations, managers and decision makers may be forced to specify these parameters subjectively. In this paper, we present a model framework designed to eliminate the arbitrary assignment of target values and unit penalty weights when applying goal programming to solve multicriteria decision problems. In particular, when neither of these parameters is available, we show how to integrate factor efficiency scores determined from data envelopment analysis into the model. We discuss an application of the methodology to ambulatory surgery centers and demonstrate the model framework via a product mix example