Journal of the ACM (JACM)
Intelligent scheduling
Optimal paths in graphs with stochastic or multidimensional weights
Communications of the ACM
Intelligent Scheduling Systems
Intelligent Scheduling Systems
Path planning under time-dependent uncertainty
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Proactive algorithms for job shop scheduling with probabilistic durations
Journal of Artificial Intelligence Research
State space search for risk-averse agents
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An axiomatic approach to robustness in search problems with multiple scenarios
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Hi-index | 0.00 |
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 approximation, with comparable search effort.