Parallelism for Perturbation Management and Robust Plans
An important insufficiency of modern industrial plans is their lack of robustness. Disruptions prevent companies from operating as planned before and induce high costs for trouble shooting. The main reason for the severe impact of disruptions stems from the fact that planners do traditionally consider the precise input to be available at planning time.
The Repair Game is a formalization of a planning task, and playing it performs disruption management and generates robust plans with the help of game tree search. Technically, at each node of a search tree, a traditional optimization problem is solved such that large parts of the computation time are blocked by sequential computations. Nevertheless, there is enough node parallelism which we can make use of, in order to bring the running times onto a real-time level, and in order to increase the solution quality per minute significantly. Thus, we are able to present a planning application at the cutting-edge of Operations Research, heavily taking advantage of parallel game tree search. We present simulation experiments which show the benefits of the repair game, as well as speedup results.
KeywordsRotation Plan Planning Task Original Plan Game Tree Node Parallelism
Unable to display preview. Download preview PDF.
- 1.Althöfer, I.: Root evaluation errors: How they arise and propagate. ICCA Journal 11(3), 55–63 (1988)Google Scholar
- 3.Ehrhoff, J., Grothklags, S., Lorenz, U.: Das Reparaturspiel als Formalisierung von Planung unter Zufallseinflüssen, angewendet in der Flugplanung. In: Proceedings of GOR conference: Entscheidungsunterstützende Systeme in Supply Chain Managment und Logistik, pp. 335–356. Physika-Verlag ,Heidelberg (2005)Google Scholar
- 4.Engell, S., Märkert, A., Sand, G., Schultz, R.: Production planning in a multiproduct batch plant under uncertainty. Preprint 495-2001, FB Mathematik, Gerhard-Mercator-Universität Duisburg (2001)Google Scholar
- 6.Flemming, P.J., Wallace, J.J.: How not to lie with statistics: the correct way to summerize benchmark results. CACM 29(3), 218–221 (1986)Google Scholar
- 8.Kaindl, H., Scheucher, A.: The reason for the benefits of minmax search. In: Proc. of the 11 th IJCAI, Detroit, MI, pp. 322–327 (1989)Google Scholar
- 10.Koenig, S., Furcy, D., Bauer, C.: Heuristic search-based replanning. In: Proceedings of the International Conference on Artificial Intelligence Planning and Scheduling, pp. 294–301 (2002)Google Scholar
- 11.Kouvelis, P., Daniels, R.L., Vairaktarakis, G.: Robust scheduling of a two-machine flow shop with uncertain processing times. IIE Transactions 32(5), 421–432 (2000)Google Scholar
- 15.Reinefeld, A.: An Improvement of the Scout Tree Search Algorithm. ICCA Journal 6(4), 4–14 (1983)Google Scholar
- 16.Römisch, W., Schultz, R.: Multistage stochastic integer programming: an introduction. Online Optimization of Large Scale Systems, 581–600 (2001)Google Scholar
- 17.Rosenberger, J.M., Schaefer, A.J., Goldsman, D., Johnson, E.L., Kleywegt, A.J., Nemhauser, G.L.: Simair: A stochastic model of airline operations. In: Winter Simulation Conference Proceedings (2000)Google Scholar
- 18.Russel, S., Norvig, P.: Artificial Intelligence, A Modern Approach. Prentice Hall Series in Artificial Intelligence (2003)Google Scholar