Abstract
We propose Pareto optimal reachability analysis to solve multi-objective scheduling and planing problems using real-time model checking techniques. Not only the makespan of a schedule, but also other objectives involving quantities like performance, energy, risk, cost etc., can be optimized simultaneously in balance. We develop the Pareto optimal reachability algorithm for Uppaal to explore the state-space and compute the goal states on which all objectives will reach a Pareto optimum. After that diagnostic traces are generated from the initial state to the goal states, and Pareto optimal schedules are obtainable from those traces. We demonstrate the usefulness of this new feature by two case studies.
This work has been supported by Danish National Research Foundation – Center for Foundations of Cyber-Physical Systems, a Sino-Danish research center.
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Notes
- 1.
\(\epsilon \) denotes a delay at locations; \(\tau \) denotes an inner transition between locations.
- 2.
If negative prices are present, the state-space graph must be acyclic. This is guaranteed by the finite time horizon over 20 orbits (about 31 h).
- 3.
This level is larger than the threshold of 40% in the linear battery model so as to make the satellite on-board battery that is non-linear work safer in the real situation.
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Zhang, Z., Nielsen, B., Larsen, K.G., Nies, G., Stenger, M., Hermanns, H. (2017). Pareto Optimal Reachability Analysis for Simple Priced Timed Automata. In: Duan, Z., Ong, L. (eds) Formal Methods and Software Engineering. ICFEM 2017. Lecture Notes in Computer Science(), vol 10610. Springer, Cham. https://doi.org/10.1007/978-3-319-68690-5_29
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