Intelligent Decision Aiding in Transportation and Logistics

Abstract

Decision aiding in transportation and logistics usually concerns a set of alternatives (solutions, options, actions, etc.) evaluated from multiple points of view considered relevant by a Decision Maker (DM). The aim is to select a subset of best alternatives or to rank alternatives from the best to the worst. As the points of view, called criteria, are usually in conflict, the only objective information that stems from the decision problem formulation is a dominance relation in the set of alternatives (alternative a dominates alternative b if a is at least as good as b on all considered criteria). While dominance relation permits to eliminate many irrelevant (i.e. dominated) alternatives, it does not compare completely all of them, resulting in a situation where many alternatives remain incomparable. This situation may be addressed by taking into account preferences of a DM. This paper will focus on decision aiding methods based on intelligent construction of DM’s preferences. Comparing to traditional decision aiding methods, intelligent decision aiding does not require from the DM a difficult to elicit preference information, like exhaustive pairwise comparisons, criteria weights or trade-offs, but it constructs the DM’s preference model from decision examples. As model building from decision examples is typical for Artificial Intelligence, the author calls this approach intelligent decision aiding. In case of choice and ranking, decision examples provided by a DM have the form of pairwise comparison of selected alternatives. A preference model should be able to reconstruct the provided pairwise comparisons. In general, the model construction follows logical induction. In case of real function models, this induction translates into ordinal regression. This paper will show construction techniques for three kinds of preference models: a set of value (utility) functions, a set of outranking relations, and a set of “if…, then…” monotonic decision rules. An important feature of all these techniques is identification of all instances of the preference model that are compatible with (i.e. reconstruct) the input preference information – this permits to draw robust conclusions regarding DM’s preferences when any of these models is applied on the whole set of considered alternatives. Finally, the paper will show how these construction techniques can be applied to real world problems from the area of transportation, where alternatives are evaluated on subsets of criteria structured into technical, functional and strategic levels.