Stochastic Constraint Programming is an extension of Constraint Programming for modelling and solving combinatorial problems involving uncertainty. A solution to such a problem is a policy tree that specifies decision variable assignments in each scenario. Several solution methods have been proposed but none seems practical for large multi-stage problems. We propose an incomplete approach: spec...

In the current work, a framework is presented for amine solvent selection in gas treating process. Since the appropriate decision making in this field affects the capital and operational costs, multi attribute decision making (MADM) techniques were used to rank alternatives. The determination of criteria and alternatives is the most important aspect in the MADM. Criteria were divided into two c...

Constant expansion of network-centric services inevitably led into development of new software technologies that would enable seamless and transparent access to the expanding amount of information. The software agent-oriented model fits convincingly well within this context as a better-suited technology over typical modular, client-server approach. This model offers concepts like autonomous beh...

The social dimension of sustainable development has most often been neglected when developing future scenarios, or, at best, been dealt with as a framework condition for successful environmental sustainability strategies. However, given the long-disputed trade-off between social and environmental improvements in a market economy, environmental and social criteria must be developed and incorpora...

This paper presents an approach to multi-step scenario specification and matching, which aims to address some of the issues and problems inherent in to scenario specification and event correlation found in most previous work. Our approach builds upon the unification algorithm which we have adapted to provide a seamless, integrated mechanism and framework to handle event matching, filtering, and...