Theodor J. Stewart

A CRITICAL SURVEY ON THE STATUS OF MULTIPLE CRITERIA DECISION MAKING THEORY AND PRACTICE

This paper seeks to review and to contrast the main streams of thought in Multiple Criteria Decision Making (MCDM) theory and practice, without attempting to review all MCDM methods in detail. The main purpose is to identify pitfalls in the usage of various approaches, and to suggest approaches which are most robustly and effectively useable, especially by the non-expert in MCDM methodology. Problem areas in MCDM still requiring further research are also discussed.

A genetic algorithm approach to multiobjective land use planning

This paper describes a class of spatial planning problems in which different land uses have to be allocated across a geographical region, subject to a variety of constraints and conflicting management objectives. A goal programming/reference point approach to the problem is formulated, which leads however to a difficult nonlinear combinatorial optimization problem. A special purpose genetic algorithm is developed for the solution of this problem, and is extensively tested numerically. The model and algorithm is then applied to a specific land use planning problem in The Netherlands. The ultimate goal is to integrate the algorithm into a complete land use planning decision support system.

Modeling uncertainty in multi-criteria decision analysis

his paper provides a review of multiple criteria decision analysis (MCDA) for cases where attribute evaluations are uncertain. The main aim is to identify different tools which can be used to represent uncertain evaluations, and to broadly survey the available decision models that can be used to support uncertain decision making. The review includes models using probabilities or probability-like quantities; explicit risk measures such as quantiles and variances; fuzzy numbers, and scenarios. The practical assessment of uncertain outcomes and preferences associated with these outcomes is also discussed.

An aspiration-level interactive model for multiple criteria decision making

Abstract A simple, eclectic approach for solving discrete alternative multiple criteria decision problems is presented. It is based on the concept of the level of aspiration, and draws on ideas of various researchers. It assumes that the user has a set of alternatives with each alternative having a score on each of a number of objectives or measures of performance. The user determines his levels of aspiration for different objectives. He is then provided with considerable feedback as to the degree of feasibility of each level of aspiration as well as the degree of feasibility with respect to all levels of aspiration as a whole. The closest nondominated solution to the solution specified by the levels of aspiration is provided. The proposed method is easy to use and easy to understand and has been implemented on a personal computer (an IBM PC or compatible with 512K RAM). We describe an experimental application in which 49 students in an MBA program used the method to solve two discrete alternative multiple criteria decision problems.

Robustness of Additive Value Function Methods in MCDM

A simulation model is constructed of choice between a discrete number of non-dominated alternatives. The long-run goals of the decision maker are assumed to be consistent with a hypothetical preference structure which satisfies assumptions of completeness, transitivity and additive independence for an ideal set of criteria. The use of additive value functions to aid the decision maker in this choice is simulated for a variety of contexts and under a number of non-idealities such as the omission of criteria, confounding of criteria and inconsistent responses. It is found that ideal preference orderings are well identified by additive value functions provided that the non-idealities are moderate and that sufficient effort is put into modelling changing marginal values for different levels of performance. One of the potentially most sensitive areas is that of shifts in the decision makers reference points as a result of the types of preference information asked.

Dealing with Uncertainties in MCDA

Many MCDA models are based on essentially deterministic evaluations of the consequences of each action in terms of each criterion, possibly subjecting final results and recommendations to a degree of sensitivity analysis. In many situations, such an approach may be justified when the primary source of complexity in decision making relates to the multicriteria nature of the problem rather than to the stochastic nature of individual consequences. Nevertheless, situations do arise, especially in strategic planning problems, when risks and uncertainties are as critical as the issue of conflicting management goals. In such situations, more formal modelling of these uncertainties become necessary. In this paper, we start by reviewing the meaning and origin of risk and uncertainty. We recognize both internal uncertainties (related to decision maker values and judgements) and external uncertainties (related to imperfect knowledge concerning consequences of action), but for this paper focus on the latter. Four broad approaches to dealing with external uncertainties are discussed. These are multiattribute utility theory and some extensions; stochastic dominance concepts, primarily in the context of pairwise comparisons of alternatives; the use of surrogate risk measures as additional decision criteria; and the integration of MCDA and scenario planning. To a large extent, the concepts carry through to all schools of MCDA. A number of potential areas for research are identified, while some suggestions for practice are included in the final section.

Multicriteria decision analysis: Some thoughts based on the tutorial and discussion sessions of the ESIGMA meetings

Abstract This paper seeks to offer an overview of the streams of thought in the field of multicriteria decision analysis or aid (MCDA) presented and discussed in the sessions of the annual meeting of the EURO Special Interest Group on Multicriteria Analysis (ESIGMA). Our main purpose is to emphasize some of the most important and original contributions made by the prominent scientists invited to those meetings and to reflect on the conclusions of the discussions held along the ten years of existence of ESIGMA, without attempting to review the broad multicriteria field in detail.

A Scenario-Based Framework for Multicriteria Decision Analysis in Water Resources Planning

This paper describes the evolution of a general group decision-making or negotiation support procedure for water resources planning at a strategic level (such as regional development planning), based on principles of multicriteria decision analysis. A number of difficulties have been encountered in applying many of the standard multiple-criteria decision-making (MCDM) tools and techniques directly in this context. The paper introduces a broader framework in which these difficulties may be overcome, so that MCDM tools can be used effectively for evaluating strategic planning options. This framework is based on direct evaluation of sequences of “policy scenarios,” which are neither a full set of possible planning “alternatives,” nor fully specified down to the finest level of detail. The overall procedure and some experiences in applying it within the context of regional water resource planning in South Africa are discussed.

Multiobjective decision support for land use planning

The overall objective of this paper is to show how a formal decision support method can be used effectively to support a land-use planning problem. Central to our approach is a heuristic algorithm based on a goal-programming/reference-point approach. The algorithm is tested on a small region in the Netherlands. To demonstrate the potential use of the algorithm, a planning problem is defined for this region. An interactive session with a land-use planner is then simulated, to show how feedback from the planner is used to generate a plan in a number of rounds. It is concluded that the approach has potential for the support of land-use problems especially in the first rounds of policy design as long as maps are used to interface between planner and algorithm. It is also shown that computational problems still hinder the achievement of realistic detail in the representation of the plan area.

Search for a moving target when searcher motion is restricted

Abstract A problem of search for a moving target in discrete space and time is considered for the case in which the rate of re-location of resources is constrained. Algorithms are developed for numerically determining search policies when resources are arbitrarily divisible and when they are indivisible (single searcher). A potential heuristic approach to the problem of limited resource divisibility is also discussed. The approaches are compared by using as an example the search for a one-dimensional random walker.