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In manufacturing, the production is often done on out-of-the-shelfmanufacturing lines, whose underlying scheduling heuristics are not known dueto the intellectual property. We consider such a setting with a black-boxjob-shop system and an unknown scheduling heuristic that, for a givenpermutation of jobs, schedules the jobs for the black-box job-shop with thegoal of minimizing the makespan. Here, the jobs need to enter the job-shop inthe given order of the permutation, but may take different paths within the jobshop, which depends on the black-box heuristic. The performance of theblack-box heuristic depends on the order of the jobs, and the natural problemfor the manufacturer is to find an optimum ordering of the jobs.  Facing a real-world scenario as described above, we engineer the Monte-Carlotree-search for finding a close-to-optimum ordering of jobs. To cope with alarge solutions-space in planning scenarios, a hierarchical Monte-Carlo treesearch (H-MCTS) is proposed based on abstraction of jobs. On synthetic andreal-life problems, H-MCTS with integrated abstraction significantlyoutperforms pure heuristic-based techniques as well as other Monte-Carlo searchvariants. We furthermore show that, by modifying the evaluation metric inH-MCTS, it is possible to achieve other optimization objectives than what thescheduling heuristics are designed for -- e.g., minimizing the total completiontime instead of the makespan. Our experimental observations have been alsovalidated in real-life cases, and our H-MCTS approach has been implemented in aproduction plant's controller.

Monte-Carlo Tree-Search for Leveraging Performance of Blackbox Job-Shop Scheduling Heuristics