Schedule Robustness Through Broader Solve and Robustify Search for Partial Order Schedules
In previous work, we have defined a two-step procedure called Solve-and-Robustify for generating flexible, partial order schedules. This partitioned problem solving approach — first find a viable solution and then generalize it to enhance robustness properties — has been shown to provide an effective basis for generating flexible, robust schedules while simultaneously achieving good quality with respect to optimization objectives. This paper extends prior analysis of this paradigm, by investigating the effects of using different start solutions as a baseline to generate partial order schedules. Two approaches are compared: the first constructs partial order schedules from a single fixed-time schedule, obtained by first performing an extended makespan optimization search phase; the second considers the search for fixed-time schedules and flexible schedules in a more integrated fashion, and constructs partial order schedules from a number of different fixed-time starting solutions. The paper experimentally shows how the characteristics of the fixed-time solutions may lower the robustness of the final partial order schedules and discusses the reasons for such behavior.
Unable to display preview. Download preview PDF.
- 1.Policella, N., Smith, S.F., Cesta, A., Oddi, A.: Generating Robust Schedules through Temporal Flexibility. In: Proceedings of the 14th International Conference on Automated Planning & Scheduling, ICAPS 2004, pp. 209–218. AAAI Press, Menlo Park (2004)Google Scholar
- 5.Policella, N.: Scheduling with Uncertainty: a Proactive Approach using Partial Order Schedules. PhD thesis, University of Rome La Sapienza (2005)Google Scholar
- 7.Aloulou, M.A., Portmann, M.C.: An Efficient Proactive Reactive Scheduling Approach to Hedge against Shop Floor Disturbances. In: Proceedings of 1st Multidisciplinary International Conference on Scheduling: Theory and Applications, MISTA 2003, pp. 337–362 (2003)Google Scholar
- 8.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
- 11.Smith, T.B., Pyle, J.M.: An effective algorithm for project scheduling with arbitrary temporal constraints. In: Proceedings of the 19th National Conference on Artificial Intelligence, AAAI 2004, pp. 544–549 (2004)Google Scholar
- 13.Oddi, A., Smith, S.F.: Stochastic Procedures for Generating Feasible Schedules. In: Proceedings 14th National Conference on Artificial Intelligence, AAAI 1997, pp. 308–314. AAAI Press, Menlo Park (1997)Google Scholar
- 14.Resende, M., Ribeiro, C.: Greedy Randomized Adaptive Search Procedures. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 219–249. Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
- 15.Policella, N., Wang, X., Smith, S., Oddi, A.: Exploiting Temporal Flexibility to Obtain High Quality Schedules. In: Proceedings of the 20th National Conference on Artificial Intelligence, AAAI 2005. AAAI Press, Menlo Park (2005)Google Scholar