Mathematical programming approaches to driver scheduling have been reported at many previous workshops and have become the dominant approach to the problem. However the problem frequently is too large for mathematical programming to be able to guarantee an optimal schedule. TRACS II, developed at the University of Leeds, is one such mathematical programming-based scheduling system. Several improvements and alternative solution methods have now been incorporated into the mathematical programming component of the TRACS II system, including a column generation technique which implicitly considers many more valid shifts than standard linear programming approaches. All improvements and alternative strategies have been implemented into the mathematical programming component of TRACS II to allow different solution methods to be used where necessary, and to solve larger problems in a single pass, as well as to produce better solutions. Comparative results on real-world problems are presented.
Abstract-As modern information systems become increasingly business-and safety-critical, it is extremely important to improve both the trust that a user places in a system and their understanding of the risks associated with making a decision. This paper presents the STRAPP framework, a generic framework that supports both of these goals through the use of personalised provenance reasoning engines and state-of-art risk assessment techniques. We present the high-level architecture of the framework, and describe the process of systematically modelling system provenance with the W3C PROV provenance data model. We discuss the business drivers behind the concept of personalizing provenance information, and describe the STRAPP approach to enabling this through a user-adaptive system style. We discuss using data provenance for risk management and treatment in order to evaluate risk levels, and discuss the use of CORAS to develop a risk reasoning engine representing core classes and relationships. Finally, we demonstrate the initial implementation of our personalised provenance system in the context of the Rolls-Royce Equipment Health Management, and discuss its operation, the lessons we have learnt through our research and implementation (both technical and in business), and our future plans for this project.
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