Implementing heuristics as an optimization criterion in neural networks for job-shop scheduling

Abstract

Recently neural network architectures have been developed that are capable of solving deterministic job-shop scheduling problems, part of the large class of NP-complete problems. In these architectures, however, no valid optimization criterion has been implemented. In this paper an enhanced neural network architecture for job-shop scheduling is proposed in which general rules of thumb for job-shop scheduling have been incorporated as a local optimization criterion. Implementation of the rules of thumb, by adaptation of the network architecture, results in a network that actually incorporates the optimization criterion, enabling parallel hardware implementation. Owing to the implemented local optimization criterion the performance of the network architecture is superior to previously presented architectures. Comparison with advanced heuristic sequential schedulers showed equal performance with respect to the quality of the solutions and better performance with respect to calculation speed.