Discovering subproblem prioritization rules for shifting bottleneck algorithms
Shifting bottleneck (SB) algorithms have been successful in solving most real-sized scheduling problems. However, it is known that under certain conditions, solution quality degrades because of subproblem interactions. Changing the order in which subproblems are solved greatly affects the solution quality. We demonstrate that for some class of problems it is possible to induce production (IF-THEN) rules that determine the subproblem solution order and lead to good quality solutions, matching the performance of the SB algorithm in most other instances.
shifting bottleneck algorithms jobshop scheduling classification inductive learning