A framework for dynamic multiple-criteria decision making

a r t i c l e i n f o The classic multiple-criteria decision making (MCDM) model assumes that, when taking a decision, the decision maker has defined a fixed set of criteria and is presented with a clear picture of all available alternatives. The task then reduces to computing the score of each alternative, thus producing a ranking, and choosing the one that maximizes this value. However, most real-world decisions take place in a dynamic environment, where the final decision is only taken at the end of some exploratory process. Exploration of the problem is often beneficial, in that it may unveil previously unconsidered alternatives or criteria, as well as render some of them unnecessary. In this paper we introduce a flexible framework for dynamic MCDM, based on the classic model, that can be applied to any dynamic decision process and which is illustrated by means of a small helicopter landing example. In addition, we outline a number of possible applications in very diverse fields, to highlight its versatility. Most real-world decision problems are dynamic, in the sense that the final decision is taken only at the end of some exploratory process, during which both alternatives and criteria may vary, as the examples of Section 6.1 testify. However, the classic multiple-criteria decision making (MCDM) model is unable to capture this dynamicity, since it assumes that, before proceeding with the ranking, the decision maker must have identified fixed sets of criteria and alternatives. While, in principle, this model could be used in a dynamic setting by considering subsequent decisions to be completely independent one from the other, doing so would constitute a gross oversimplification of the way humans think about the fine interlinking that exists among decisions in a dynamic environment, in which earlier evaluations affect later ones. The framework we propose in this paper aims to address this problem by extending the classic MCDM model in a flexible way that enables its use in very diverse fields requiring some form of dynamic decision making. The rest of this paper is organized as follows. In Section 2, we briefly review the classic MCDM model and present the general theory our framework is set in. Subsequently, in Section 3, we delve into the crucial issue of choosing an appropriate aggregation function for this model, and present some well-known examples from the recent literature. We then give, in Section 4, a …