Pruning of decision alternatives in multiple criteria decision making,based on the Uta method for estimating utilities

A new interactive method for the progressive elimination of elements from a finite set of decision alternatives is proposed. A sequence of alternatives is presented to the decision maker, who places each new alternative presented in rank order relative to the earlier alternatives evaluated. This ranking of elements in a subset of the decision space is used to eliminate other alternatives from further consideration. The approach is broadly based on the UTA method for utility assessment. The emphasis is not, however, on assessing a utility function as such; instead the class of utility functions is used to eliminate elements of the decision space, when the optimality of such elements is inconsistent with the presumed properties of the utility function and the rank orderings given. In this way, the decision maker need only evaluate a relatively small subset of the decision space, before all remaining alternatives are eliminated. The new procedure is illustrated by two numerical examples.     

Application of fuzzy soft set in decision making problems based on grey theory

There are many uncertain problems in practical production and life which need decisions made with soft sets and fuzzy soft sets. However, the basis of evaluation of the decision method is single and simple, the same decision problem can obtain different results from using a different evaluation basis. In this paper, in order to obtain the right result, we discuss fuzzy soft set decision problems. A new algorithm based on grey relational analysis is presented. The evaluation bases of the new algorithm are multiple. There is more information in a decision result based on multiple evaluation bases, which is more easily accepted and logical to one’s thinking. For the two cases examined, the results show that the new algorithm is efficient for solving decision problems.     

Application of fuzzy soft set in decision making problems based on grey theory

There are many uncertain problems in practical production and life which need decisions made with soft sets and fuzzy soft sets. However, the basis of evaluation of the decision method is single and simple, the same decision problem can obtain different results from using a different evaluation basis. In this paper, in order to obtain the right result, we discuss fuzzy soft set decision problems. A new algorithm based on grey relational analysis is presented. The evaluation bases of the new algorithm are multiple. There is more information in a decision result based on multiple evaluation bases, which is more easily accepted and logical to one’s thinking. For the two cases examined, the results show that the new algorithm is efficient for solving decision problems.     

A multiobjective decomposition-coordination framework with an application to vehicle layout and configuration design

This short paper addresses both researchers in multiobjective optimization as well as industrial practitioners and decision makers in need of solving optimization and decision problems with multiple criteria. To enhance the solution and decision process, a multiobjective decomposition-coordination framework is presented that initially decomposes the original problem into a collection of smaller-sized subproblems that can be solved for their individual solution sets. A common solution for all decomposed and, thus, the original problem is then achieved through a subsequent coordination mechanism that uses the concept of epsilon-efficiency to integrate decisions on the desired tradeoffs between these individual solutions. An application to a problem from vehicle configuration design is selected for further illustration of the results in this paper and suggests that the proposed method is an effective and promising new solution technique for multicriteria decision making and optimization. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Supporting Learning in Evolving Dynamic Environments

In dynamic decision environments such as direct sales, customer support, and electronically mediated bargaining, decision makers execute sequences of interdependent decisions under time pressure. Past decision support systems have focused on substituting for decision makers' cognitive deficits by relieving them of the need to explicitly account for sequential dependencies. However, these systems themselves are fragile to change and, further, do not enhance decision makers' own adaptive capacities. This study presents an alternative strategy that defines information systems requirements in terms of enhancing decision makers' adaptation. In so doing, the study introduces a simulation model of how decision makers learn patterns of sequential dependency. When a system was used to manage workflows in a way predicted by the model to enhance learning, decision makers in a bargaining experiment learned underlying patterns of sequential dependencythat helped them adapt to new situations. This result is rare if not unique in the study of dynamic decision environments. It indicates that a shift, away from substituting for short-term deficits and toward enhancing pattern learning, can substantially improve the effectiveness of decision support in dynamic environments. Based on the specific findings in this study, this shift has important implications for designing information system workflows and potential future applications in interface design.     

Multicriteria sorting using a valued indifference relation under a preference disaggregation paradigm

Most multicriteria decision methods need the definition of a significant amount of preferential information from a decision agent. The preference disaggregation analysis paradigm infers the model’s parameter values from holistic judgments provided by a decision agent. Here, a new method for inferring the parameters of a fuzzy outranking model for multicriteria sorting is proposed. This approach allows us to use most of the preferential information contained in a reference set. The central idea is to characterize the quality of the model by measuring discrepancies and concordances amongst (i) the preference relations derived from the outranking model, and (ii) the preferential information contained in the reference set. The model’s parameters are inferred from a multiobjective optimization problem, according to some additional preferential information from a decision agent. Once the model has been fitted, sorting decisions about new objects are performed by using a fuzzy indifference relation. This proposal performs very well in some examples.     

一类或决策模型及其应用

提出了一类特殊类型的数学规划模型并给出了一种新的分枝定界算法.这类数学模型尽管可以转化为0-1规划模型,但它相对于转化后的0-1规划模型:①决策意义明确,表达形式相对简单;②不需要引入参数M并在求解前确定其上界;③相对于求解转化后的0-1规划模型的分枝定界法,新分枝定界算法在最好情形下计算量最多为原算法的八分之一.作为本模型的一个应用,可以用来解决一些要么不实施要么有一定数量下限限制才可以实施的决策问题.     

A visual reference direction approach to solving discrete multiple criteria problems

In this paper, we propose a new visual interactive method for solving discrete multiple criteria problems. The method is based on the use of a reference direction, which is determined by the aspiration levels for the criteria specified by the decision maker. The reference direction is projected onto the set of efficient alternatives. A subset found in this way is presented to a decision maker in a visual form using computer graphics. He can choose any efficient alternatives he pleases.We need notmake any assumptions about the properties of the utility function.The method has been implemented on an IBM/PC1 microcomputer. The name of the program is Vimda (a Visual Interactive Method for Discrete Alternatives).     

Factors which influence decision making in new product evaluation

New product development is indeed very important for companies. However, developing new products is a risky and uncertain process. In order to reduce the risks and uncertainties, companies need to evaluate their new product initiatives carefully and make accurate decisions. Although the outcome of a new product evaluation decision can be influenced by the environmental uncertainties that are beyond a company’s control, companies can successfully improve the accuracy of their new product evaluation decisions. This article presents an integrated framework for understanding how various factors affect decision making in new product evaluation and provides guidelines for reducing their negative impacts on new product decisions. The results indicate that the quality of new product evaluation decisions is affected by four major sets of factors, namely the nature of the task, the type of individuals who are involved in the decisions, the way the individuals’ opinions are elicited and the way the opinions are aggregated.     

A framework for decision support systems for scheduling problems

Considering a decision support system as a tool where executive's judgment can be included along with the mathematical tool kit of the management scientist, this paper shows the need to include problem management as an integral component of the decision support system for scheduling problems. A methodology based on the resolution of conflicts among various constraints in scheduling problems is proposed to implement the problem management system in a decision support system for these problems. The paper concludes with some guidelines to create a workable framework for providing effective decision support to solve scheduling problems and the identification of some fruitful directions for future research.     

A new data envelopment analysis method for priority determination and group decision making in the analytic hierarchy process

The DEAHP method for weight deviation and aggregation in the analytic hierarchy process (AHP) has been found flawed and sometimes produces counterintuitive priority vectors for inconsistent pairwise comparison matrices, which makes its application very restrictive. This paper proposes a new data envelopment analysis (DEA) method for priority determination in the AHP and extends it to the group AHP situation. In this new DEA methodology, two specially constructed DEA models that differ from the DEAHP model are used to derive the best local priorities from a pairwise comparison matrix or a group of pairwise comparison matrices no matter whether they are perfectly consistent or inconsistent. The new DEA method produces true weights for perfectly consistent pairwise comparison matrices and the best local priorities that are logical and consistent with decision makers (DMs)’ subjective judgments for inconsistent pairwise comparison matrices. In hierarchical structures, the new DEA method utilizes the simple additive weighting (SAW) method for aggregation of the best local priorities without the need of normalization. Numerical examples are examined throughout the paper to show the advantages of the new DEA methodology and its potential applications in both the AHP and group decision making.     

Information acquisition in new product introduction

In the presence of huge losses from unsuccessful new product introductions, companies often seek forecast information from various sources. As the information can be costly, companies need to determine how much effort to put into acquiring the information. Such a decision is strategically important because an insufficient investment may cause lack of knowledge of product profitability, which in turn may lead to introducing a loss-making product or scrapping a potentially profitable one. In this paper, we use decision analytical models to study information acquisition for new product introduction. Specifically, we consider a decision maker (DM) who, prior to introducing a new product, can purchase forecasts and use the information to update his knowledge of the market demand. We analyze and compare two approaches: The first approach is to determine the total amount of forecasts to purchase all at once. The second one is to purchase forecasts sequentially and, based on the purchased forecasts, determine whether those forecasts are informative enough for making an introduction decision or an additional forecast is needed. We present dynamic programming formulations for both approaches and derive the optimal policies. Via a numerical study, we find the second approach, i.e., purchasing forecasts sequentially, can generate a significant profit advantage over the first one when (1) the cost of acquiring forecasts is neither too high nor too low, (2) the precision of the forecasts is of a moderate level, and (3) the profit margin of the new product is small.     

Generalizing variance to allow the inclusion of decision attitude in decision making under uncertainty

The problem of decision making under uncertainty is considered. It is noted that an alternative is described in terms of an uncertainty profile. We observe that a major difficulty in the decision process is the comparison of these uncertainty profiles. We discuss the need for introducing some features of an uncertainty profile to help simplify this comparison. We note that the quantification of these simplifying features involves some subjective considerations about the decision makers preferences. We introduce the idea of the decision maker’s attitudinal character to help in the formulation of these considerations. We then investigate two important features associated with an uncertainty profile. The first, the representative value, is a generalization of expected value commonly used under probabilistic uncertainty. The second, called the measure of deviation, provides a generalization of the concept of variance. We show how these new measures allows us to consider uncertainty profiles other then just the probabilistic one. They also allow us introduce other decision maker attitudes then the one implicitly assumed with the expected value and variance.     

SMAA - Stochastic multiobjective acceptability analysis

Stochastic multiobjective acceptability analysis (SMAA) is a multicriteria decision support technique for multiple decision makers based on exploring the weight space. Inaccurate or uncertain input data can be represented as probability distributions. In SMAA the decision makers need not express their preferences explicitly or implicitly; instead the technique analyses what kind of valuations would make each alternative the preferred one. The method produces for each alternative an acceptability index measuring the variety of different valuations that support that alternative, a central weight vector representing the typical valuations resulting in that decision, and a confidence factor measuring whether the input data is accurate enough for making an informed decision.     

Decision Networks

Decision Networks is a technique for solving problems which involve a sequence of decisions. It is similar in style to critical path analysis in that it consists of arrow diagrams which give a visual representation of the problem and are used as a basis for a simple calculation procedure. The technique can deal with deterministic and stochastic problems and in the latter case is more general than decision trees. The decision network approach meets the need for a method of solution for multi-stage decision problems which is easily understood, helps the user to visualize the nature of the problem and is routine in application.     

On Multiple Criteria Decision Support for Suppliers on the Competitive Electric Power Market

As an active participant of a competitive energy market, the generator (the energy supplier) challenges new management decisions being exposed to the financial risk environment. There is a strong need for the decision support models and tools for energy market participants. This paper shows that the stochastic short-term planning model can be effectively used as a key analytical tool within the decision support process for relatively small energy suppliers (price-takers). A self-scheduling method for the thermal units on the energy market is addressed. A schedule acquired for given preferences can be used as a desired pattern for bidding process. The uncertainty of the market prices is modeled by a set of possible scenarios with assigned probabilities. Several risk criteria are introduced leading to a multiple criteria optimization problem. The risk criteria are well appealing and easily computable (by means of linear programming) but they meet the formal risk aversion standards. The aspiration/reservation based interactive analysis applied to the multiple criteria problem allows us to find an efficient solution (generation scheme) well adjusted to the generator preferences (risk attitude).     

A DEA approach for fair allocation of common revenue

An issue of considerable importance, how to allocate a common revenue in an equitable manner across a set of competing entities. This paper introduces a new approach to obtaining allocation common revenue on all decision making units (DMUs) in such a way that the relative efficiency is not changed. In this method for determining allocation common revenue dose not need to solving any linear programming. A numerical example is provided to illustrate the results of the analysis.     

Doing Something about the Future

This paper is concerned with the need for more interdisciplinary, systems oriented, research directed towards major problems encountered by decision makers in industry and government; a need which is more difficult to meet in the face of traditional methods of organising knowledge and research. It emphasises the need to acknowledge the true complexity of the problems and the interactive nature of any effective research procedure. As an illustration, both of the need and the problems involved in meeting it, the development of a new program of research into problems of Management and Technology at the International Institute of Applied Systems Analysis, which is supported by seventeen nations of all political complexions, is analysed. The implication is that we can do something about the future, but we must be prepared to do.     

Probabilistic judgments specified partially in the analytic hierarchy process

The Analytic Hierarchy Process (AHP) is a decision-making tool which yields priorities for decision alternatives. This paper proposes a new approach to elicit and synthesize expert assessments for the group decision process in the AHP. These new elicitations are given as partial probabilistic specifications of the entries of pairwise comparisons matrices. For a particular entry of the matrix, the partial probabilistic elicitations could arise in the form of either probability assignments regarding the chance of that entry falling in specified intervals or selected quantiles for that entry. A new class of models is introduced to provide methods for processing this partial probabilistic information. One advantage of this approach is that it allows to generate as many pairwise comparison matrices of the decision alternatives as one desires. This, in turn, allows us to determine the statistical significance of the priorities of decision alternatives.