Improving iterative repair strategies for scheduling with the SVM

The resource constraint project scheduling problem (RCPSP) is an NP-hard benchmark problem in scheduling which takes into account the limitation of resources' availabilities in real life production processes and subsumes open-shop, job-shop, and flow-shop scheduling as special cases. We present here an application of machine learning to adapt simple greedy strategies for the RCPSP. Iterative repair steps are applied to an initial schedule which neglects resource constraints. The rout-algorithm of reinforcement learning is used to learn an appropriate value function which guides the search. We propose three different ways to define the value function and we use the support vector machine (SVM) for its approximation. The specific properties of the SVM allow to reduce the size of the training set and SVM shows very good generalization behavior also after short training. We compare the learned strategies to the initial greedy strategy for different benchmark instances of the RCPSP.