Andre de Korvin

Utilizing fuzzy compatibility of skill sets for team selection in multi-phase projects

Abstract In this paper, a model is developed for the selection of personnel for a multiple phase project which takes into account the match between the skills possessed by each individual, the skills needed for each phase, and rather flexible budget considerations. The algorithm uses the fuzzy construct of compatibility to measure the fit of a person’s skill set to the goal set for each project phase. Based on the individual fuzzy compatibility measures, the team is formed from combined levels of compatibility and acceptable levels of quality defined for the goal set. 1 Introduction , 2 Background present the background information necessary to an understanding of project management phases and compatibility of skills. The development of the model and subsequent algorithm in 3 Model development , 4 Application , respectively rely on fuzzy measures of compatibility. Finally, an application is presented in Section 4 with conclusions stated in Section 5 .

A decision making model for multi-attribute problems incorporating uncertainty and bias measures

Abstract Although a particular attribute, in isolation, may be considered as very important, it will not be of much use in differentiating among alternate decisions if all values for that particular attribute are very close. Another problem is in specifying weights indicating the attributes the decision maker considers important. These weights are difficult to obtain because they often reflect an attitude brought about by considering all of the alternatives rather than reflecting the decision makers behavior. This article addresses the preceding problems through successive iterations of a process by which the decision maker is presented with sets of alternatives that most closely satisfy his ideal choice. The process incorporates the uncertainty of the decision maker with respect to how much each alternatives attribute value satisfies his ideal value. Entropy is also used as a measure of uncertainty in obtaining the alternative that is as close as possible to the ideal. The bias of the decision maker is reflected in how much he feels each attribute will make him happy. Fuzzy set theory is used to cope with the vagueness of the decision makers feelings with respect to his satisfaction with an alternative and the relative importance of each attribute.

A fuzzy difference equation with an application

Difference equations arise in the modeling of many interesting problems. “Measurements” of data or specified information for an underlying problem may be imprecise or only partially specified. This motivates us to initiate a study of “fuzzy difference equations.” In this paper, we will formulate and solve a given difference equation in the fuzzy setting and give a general method for dealing with any first order difference equation.

A fuzzy logic-based decision model to satisfy goals for successful product/service introduction

Abstract The Dempster–Shafer theory of evidence is applied to a multiattribute decision-making problem where the decision maker must determine which of several products/services have the best opportunity for success in a competitive marketplace. Multiattribute decisions are generally constrained by the uncertainty inherent in assessing the relative importance of each attribute element that is needed for success and the evaluation of the product/service to be introduced. The relative importance of each attribute element deemed necessary for success is assessed by the decision maker as a goal to be met. The evaluation of each product/service is addressed through expert opinion about the degree to which each element is contained in each product/service. Then the belief and plausibility that a product/service will satisfy the decision makers goal are calculated. The decision to introduce a product or service depends on the evaluation of the anticipated loss from introduction of a product/service into a competitive market.

Assessing risks due to threats to internal control in a computer-based accounting information system: a pragmatic approach based on fuzzy set theory

A rational risk assessment model, based on the reasoning of fuzzy set theory, is presented. The model would help managers assess risk exposure due to potential threats to internal control in a computer-based accounting information system. Such risk assessment is essential in making appropriate decisions about establishing new internal control policies and procedures that may be necessary to protect the integrity and security of the information system. Copyright

Decision making mechanisms and stopping times in a generalized information system

Abstract A mechanism for a decision maker ethically bound to pursue the best available course of action is studied. The model allows for feedback information to be input for each cycle of decision making. This type of information-feedback loop is relevant to many real decision making situations. Equations describing the updated expected utility in terms of the learning factor are given and a discussion follows as to when learning takes place. It is then shown that these estimates converge to the appropriate values. Moreover these estimators form a martingale or a submartingale, thus allowing the analysis of when and how the decision makers can terminate the feedback decision making loop. Several strategies are discussed as well as the corresponding probabilities of ending the loop in a finite amount of time.

Rough set theory fuzzy belief functions related to statistical confidence:application & evaluation for golf course closing

Rough set theory is developed in terms of fuzzy sets that will allow a decision maker to determine certain and possible rules. In the application presented these rules are determined for the decision to close a golf course when the number of rounds of golf to be played is anticipated to be low. Attributes having an effect on the number of rounds of golf to be played are statistically analyzed and those negative factors are selected for generating rules for closing. Furthermore, the degree of belief of the decision maker in these generated rules is determined to be directly related to statistical confidence intervals.

Sample size: achieving quality and reducing financial loss

Determining the proper sample size such that quality is assured while financial losses are not unnecessarily incurred is critical to an effective quality program. The main purpose of the present work is to design a fuzzy controller to adjust sample sizes according to potential fuzzy loss penalties. A set of fuzzy rules is given where, depending on the antecedents, the sample size may be decreased, moderately modified, or increased. At any given moment the proportion of defects in the sample determines the firing strength of the rules suggesting an appropriate sample size. These rules are then modified to include an analysis of the decision maker’s belief that in a particular situation an inappropriate rule is being considered such that an expected loss would be incurred in meeting or falling short of defined quality goals.

Identifying Precedents under Uncertainty

Information about the case to be decided rarely is complete and precise. So dealing with imprecise information definitely is one of the major issues of legal decision making. In order to be able to identify a non-empty set of precedents most similar to our case, we introduce the Dempster-Shafer rule for combining information from independent sources and use the resulting mass functions to determine the importance of each precedent in our knowledge system. Additionally, the method is illustrated by an example.

Rule Extraction Using Rough Sets When Membership Values Are Intervals

As corporate data is more and more becoming an invaluable asset for decision makers, technologies such as data warehouses and data mining are an essential part of a companys information infrastructure. Methods for mining coiporate data and extracting rules from it are well established for crisp and traditional fuzzy data. This paper aims at extending the traditional fuzzy approach for situations where membership values are intervals.