language: en

[In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96), Portland, OR, USA, August 1996]

Optimal Factory Scheduling using Stochastic Dominance A*

Peter R. Wurman and Michael P. Wellman
University of Michigan Artificial Intelligence Laboratory
1101 Beal Avenue Ann Arbor, MI, 48109-2110
fpwurman, wellmang@umich.edu

Abstract We examine a standard factory scheduling problem with stochastic processing and setup times, minimizing the expectation of the weighted number of tardy jobs. Because the costs of operators in the schedule are stochastic and sequence dependent, standard dynamic programming algorithms such as A* may fail to find the optimal schedule. The SDA* (Stochastic Dominance A*) algorithm remedies this difficulty by relaxing the pruning condition. We present an improved state-space search formulation for these problems and discuss the conditions under which stochastic scheduling problems can be solved optimally using SDA*. In empirical testing on randomly generated problems, we found that in 70%, the expected cost of the optimal stochastic solution is lower than that of the solution derived using a deterministic ap proximation, with comparable search effort.

1 INTRODUCTION