Informatica

In this paper, a new multi-criteria decision-making procedure is presented, which captures preferential information in the form of the threshold model. It is based on the ELECTRE-like sorting analysis restricted by the localization principle, which enables high adaptability of the decision model and reduces the cognitive load imposed on the decision-makers. It lays the foundation for the introduction of three concepts that have been previously insufficiently supported by outranking methods – semiautomatic derivation of criteria weights according to the selective effects of discordance and veto thresholds, convergent group consensus seeking, and autonomous multi-agent negotiation. The interdependent principles are justified, and the methodological solutions underlying their implementation are provided.

In general terms some situations are described which require the exploitation of heuristics either to solve a mathematical optimization problem or to analyse results. A possibility to implement heuristic knowledge for selecting a suitable algorithm depending on available problem data and information retrieved from the user, is investigated in detail. We describe some inference strategies and knowledge representations that can be used in this case, and the rule-based implementation within the EMP system for nonlinear programming. Case studies are presented which outline on the one hand the heuristic recommendation of an optimization code and the achieved numerical results on the other hand.