Optimal Factory Scheduling using Stochastic Dominance A*
This work addresses scheduling inefficiencies for factories dealing with uncertain times, though it is incremental as it builds on existing A* methods.
The paper tackles the factory scheduling problem with stochastic processing and setup times by developing the SDA* algorithm, which relaxes pruning conditions to find optimal schedules, resulting in a 70% improvement in expected cost over deterministic approximations in empirical tests.
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.