Optimization problems have been immuned to any attempt of combination with machine learning until a decade ago but it is now an active research field. This thesis has studied the potential implementation of a machine learning heuristic to improve the resolution of the optimization scheduling problems based on a Constraint Programming solver. Some scheduling problems, known as N P -hard problems, suffer from large computational cost (large number of jobs to schedule) and consequent human effort (well-suited heuristics need to be derived). Moreover industrial scheduling problems obviously evolves over time but a lot of features and the basic structure remain the same. Hence they have potential in the implementation a supervised-learning-based heuristic. First part of the study was to model a given benchmark of instances and im- plement some famous heuristics (as earliest due date, combined with the largest duration) in order to solve the benchmark. Based on the none-optimality of returned solutions, primaries instances were choosen to implement our method. The second part represents the procedure which has been set up to design a supervised-learning-based heuristic. An instance generator was first built to map the potential industrial evolutions of the instances. It returned secondaries instances representing the learning database. Then a CP-well-suited node ex- traction scheme was set up to collect relevant information from the resolution of the search tree. It will collect data from nodes of the search tree given a proper criteria. These nodes are next projected onto a constant-dimensional space which described the system, the underlying subtree and the impact of the affectations. Upon these features and designed target values statistical mod- els are implemented. A linear and a gradient boosting regressions have been implemented, calibrated and tuned upon the data. Last was to integrate the supervised-learning model into an heuristic framework. This has been done via a soft propagation module to try the instantiation of all the children of the considered node and apply the given module upon them. The selection decision rule was based upon a reconstructed score. Third part was to test the procedure implemented. New secondaries instances were generated and supervised- learning-based heuristic tested against the earliest due date one. The procedure was tested upon two different instances. The integrated heuristic returned positive results for both instances. For the first one (10 jobs to schedule) a gain in the first solution found (resp. the number of backtracks) of 18% (resp. 13% were realized. For the second instance (90 jobs to schedule) a gain in the first solution found of at least 16%. The results come to validate the procedure implemented and the methodology used.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-207471 |
| Date | January 2017 |
| Creators | Dabert, Geoffrey |
| Publisher | KTH, Optimeringslära och systemteori |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | TRITA-MAT-E ; 2017:20 |
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