During the past six months, we have made significant progress in our research. We have developed a CLP based logical specification system for modelling multi-objective decision problem, an algorithm for translating logical specifications into integer programming problems and a prototype implementation. Also we developed a generic symbolic IP solver which can be applied to solve challenging stochastic integer programming problems. This new algorithm provides a powerful means for decision support with uncertainty.
We will continue this fruitful research aiming to provide a practical system for multi-objective decision support with uncertainty. In particular, we will refine and enhance the logical specification system by introducing constructs for specifying uncertainty. The translation algorithm will also be further developped to support dealing with stochastic variables. We will also concentrate on the development of MGBA for solving stochastic integer problems. Due to the complexity of computation, we will explore the inherent parallelism of the algorithm and build up parallel implementation of the algorithm for solving large scale stochastic integer problems. We are also planning to perform some serious case study on real world problems. A WWW-based interface will be build in the late stage of the project to deploy the system on the internet for demostration and application.