Ayodele Mobolurin

An action learning evaluation procedure for multiple criteria decision making problems

Abstract In this paper we focus on an extension of the Analytic Hierarchy Process (AHP) that accommodates ambiguity on the part of the decision maker (DM), and facilitates the exploration of the decision domain. We propose a systematic action learning process that builds confidence as it converges from numeric interval estimates to numeric point estimates. Our Multiple Criteria Decision Making (MCDM) problem procedure structures the problem as a hierarchy, evaluates all objects using pairwise comparisons that accommodate vagueness and ambiguity, uses interval prioritization techniques, and does synthesis using the linear additive value function. This action learning process facilitates the understanding of key stakeholders, which is imperative for the successful implementation of the subsequent decision.

Using formal MS/OR modeling to support disaster recovery planning

Abstract All organizations are susceptible to a non-zero risk of experiencing out-of-course events, whether natural or man-made, that can lead to internal “disasters” with respect to business operations. Different types of events (e.g. flood, earthquake, fire, theft, computer failure) have implications for the operations of modern organizations. Hence, there is a critical need for planning and recovery strategies for the effects of disasters. Disaster recovery plans (DRPs) aim at ensuring that organizations can function effectively during and following the occurrence of a disaster. As such, they possess cost, performance, reliability, and complexity characteristics that make their development and selection non-trivial. To date, there has been little modeling of disaster recovery issues in the MS/OR literature. We believe that many of the issues involved can benefit from the application of quantitative decision-making techniques. Consequently, in this paper our contribution is prescriptive rather than descriptive in nature and we propose the use of mathematical modeling as a decision support tool for successful development of a DRP. In arriving at a final DRP, decision-makers must consider a number of options or subplans and select a subset of these subplans for inclusion in the final plan. We present a mathematical programming model which helps the decision maker to select among competing subplans, a subset of subplans which maximizes the “value” of the recovery capability of a recovery strategy. We use hypothetical situations to illustrate how this technique can be used to support the planning process.

Supporting facilitation in group support systems: techniques for analyzing consensus relevant data

In this paper, we present a set of techniques and an approach to support the facilitator in building consensus during group decision making in computer supported group work. The approach utilizes data about each participants expressed preferences (scores and ranks) for a set of decision alternatives under consideration. The data are analyzed to provide the facilitator with information about the level of group consensus, coalescing of subgroups, and areas of strong disagreement. An illustration from a real-world case situation demonstrates the approach.

Modelling pairwise comparisons on ratio scales

Abstract Many ‘real world’ decision problems have multiple criteria on qualitative domains. Given the expectation that more and more of such problems will be modelled as MCDM problems involving scoring on subjective/qualitative domains, then there is a class of significant problems for which the evaluator will require an evaluation framework which handles occurrences of seeming intransitivity and inconsistency. These phenomena are likely to appear in qualitative/subjective domains where the decision-making environment is characterized by ambiguity and vagueness. In this paper we present a model for exploring the implications of pairwise comparisons on ratio scales. This model provides a mathematical explanation for the phenomena of intransitivity and inconsistency that sometimes appear in situations involving scoring on a subjective/qualitative domain. Using this framework we develop methods for generating consistent intervals on ratio scales. We also present a two-stage action learning process for generating consistent points on ratio scales that provides both behavioral and mathematical convergence.

A process for generating quantitative belief functions

Abstract We present a structured methodology for transforming qualitative preference relationships among propositions into appropriate numeric representations. This approach will be useful in the difficult process of knowledge acquisition from experts on the degree of belief in various propositions or the probability of the truthfulness of those propositions. The approach implicitly (through the qualitative assignments) and explicitly (through the vague interval pairwise comparisons) provides for different levels of preference relationships. Among its advantages, it permits the expert to: explore the given problem situation, using linguistic quantifiers; avoid the premature use of numeric measures; and identify input data that are inconsistent with the theory of belief functions.

An approach to using the analytic hierarchy process for solving multiple criteria decision making problems

Abstract In this paper we present a procedure for solving multiple criteria decision making problems. Our procedure, which is an extension of the Analytic Hierarchy Process, addresses the issue of fuzziness in the weighing information provided by the decision maker, the consequent stability of the AHP ranking of the alternatives and the sensitivity of such rankings to small perturbations. These issues are especially important when the concept of optimality incorporates parameters or weights that reflect subjective preferences. The procedure provides a consistent and systematic method for carrying out goal-seeking sensitivity analysis that captures the decision makers preference structure using his/her indifference region.

Generating consensus fuzzy cognitive maps

The Fuzzy Cognitive Maps (FCM) of B. Kosko (1986) are useful tools for exploring the impacts of inputs to fuzzy dynamical systems. The development of an FCM often occurs within a group context because it is felt that the variety of perspectives on the given dynamical system improves the effort to identify the relevant concepts and the causal relationships between the concepts. The assumption is that combining incomplete, conflicting opinions of different experts may cancel out the effects of oversight, ignorance and prejudice. There is also then need to accommodate the inherent fuzziness of the problem. We present an integrated process for rating of the intensity of causal relationships, generating mean FCMs, assessing group consensus, and supporting the building of group consensus.

A qualitative discriminant process for scoring and ranking in group support systems

Abstract Group support systems (GSS) are increasingly being used within organizations to support group work. One area of support that is often desired is the scoring and ranking of alternatives on qualitative/subjective domains. In this article, we present a new, conceptual approach, the qualitative discriminant process, for scoring and ranking in GSS. This approach is based on well-established decision analysis techniques. It significantly advances the state of the art of GSS by addressing four common limitations: (1) the inability to deal with vagueness of human decision makers in articulating preferences; (2) difficulties in mapping qualitative evaluation to numeric estimates; (3) problems in aggregating individual preferences into meaningful group preference; and (4) the lack of simple, user-friendly techniques for dealing with a large number of decision alternatives. Our approach is easy to implement in stand-alone personal computers and GSS platforms. We illustrate this with a real-world problem on a prototype implementation.

A qualitative discriminant approach for generating quantitative belief functions

The authors present an approach that will be useful in knowledge acquisition from experts on the degree of belief in, or the probability of, the truthfulness of various propositions. Its advantages include exploring the given problem situation using linguistic quantifiers; avoiding the premature use of numeric measures; and identifying input data that is inconsistent with the theory of belief functions.

Supporting team decision-making with consensus-relevant information

Many managerial and technical decision-making problems are complicated by at least two factors: (1) some of the evaluation criteria are qualitative/subjective; and (2) group decision-making is involved, thus requiring some means of aggregating individual ratings. In some situations where the consensus level is low group members might be interested in moving towards a higher level of consensus, but might not be sure of the direction in which they should move. In this paper we present an integrated framework for assessing group consensus, and supporting the building of group consensus.