SEaM: Analyzing Schedule Executability Through Simulation
Increasing attention is being dedicated to the problem of schedule execution management. This has encouraged research focused on the analysis of strategies for the execution of plans in real working environments. To this aim, much work has been recently done to devise scheduling procedures which increase the level of robustness of the produced solutions. Yet, these results represent only the first step in this direction: in order to improve confidence in the theoretical results, it is also necessary to conceive experimental frameworks where the devised measures may find confirmation through empirical testing. This approach also has the advantage of unveiling possible counter-intuitive insights of the proposed scheduling strategies, which otherwise might remain concealed. This paper presents: (a) an experimental platform designed to tackle the problem of schedule execution with uncertainty; (b) an analysis of a variety of schedule execution tests performed under variable environmental conditions.
KeywordsSchedule Problem Project Schedule Project Schedule Problem Execution Algorithm Exogenous Event
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- 1.Davenport, A.J., Beck, J.C.: A Survey of Techniques for Scheduling with Uncertainty (2000) (February 2006), accessible on-line at: http://tidel.mie.utoronto.ca/publications.php
- 7.Schoppers, M.: Universal Plans for Reactive Robots in Unpredictable Environments. In: Proceedings of the Tenth International Joint Conference on Artificial Intelligence, IJCAI 1987 (2005)Google Scholar
- 8.Ambros-Ingerson, J., Steel, S.: Integrating Planning, Execution and Monitoring. In: Proceedings of the Seventh National Conference on Artificial Intelligence, AAAI 1988 (1988)Google Scholar
- 9.Beetz, M., McDermott, D.: Improving Robot Plans During Their Execution. In: Proceedings of the Second International Conference on AI Planning Systems, AIPS 1994 (1994)Google Scholar
- 10.Cheng, C., Smith, S.F.: Generating Feasible Schedules under Complex Metric Constraints. In: Proceedings of the 12th National Conference on Artificial Intelligence, AAAI 1994, pp. 1086–1091. AAAI Press, Menlo Park (1994)Google Scholar
- 12.Cesta, A., Oddi, A., Smith, S.F.: Profile Based Algorithms to Solve Multiple Capacitated Metric Scheduling Problems. In: Proceedings of the 4th International Conference on Artificial Intelligence Planning Systems, AIPS 1998, pp. 214–223. AAAI Press, Menlo Park (1998)Google Scholar
- 13.Cesta, A., Oddi, A., Smith, S.F.: An Iterative Sampling Procedure for Resource Constrained Project Scheduling with Time Windows. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence, pp. 1022–1029. Morgan Kaufmann, San Francisco (1999)Google Scholar
- 15.Policella, N., Rasconi, R.: Designing a Testset Generator for Reactive Scheduling. Intelligenza Artificiale (Italian Journal of Artificial Intelligence) II(3), 29–36 (2005)Google Scholar
- 16.Kolisch, R., Schwindt, C., Sprecher, A.: Benchmark Instances for Project Scheduling Problems. In: Weglarz, J. (ed.) Project Scheduling - Recent Models, Algorithms and Applications, pp. 197–212. Kluwer Academic Publishers, Boston (1998)Google Scholar