Non-greedy heuristics and augmented neural networks for the open-shop scheduling problem

In this paper we propose some non-greedy heuristics and develop an Augmented-Neural-Network (AugNN) formulation for solving the classical open-shop scheduling problem (OSSP). AugNN is a neural network based meta-heuristic approach that allows integration of domain-specific knowledge. The OSSP is framed as a neural network with multiple layers of jobs and machines. Input, output and activation functions are designed to enforce the problem constraints and embed known heuristics to generate a good feasible solution fast. Suitable learning strategies are applied to obtain better neighborhood solutions iteratively. The new heuristics and the AugNN formulation are tested on several benchmark problem instances in the literature and on some new problem instances generated in this study. The results are very competitive with other meta-heuristic approaches, both in terms of solution quality and computational times.