Constraint satisfaction in logic programming
Constraint satisfaction in logic programming
Introduction to algorithms
A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
Computers and Industrial Engineering
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling
Computers and Industrial Engineering
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A new adaptive neural network and heuristics hybrid approach for job-shop scheduling
Computers and Operations Research
Proceedings of the 5th International Conference on Genetic Algorithms
A Heuristic Combination Method for Solving Job-Shop Scheduling Problems
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Comparison of Genetic Algorithms for the Static Job Shop Scheduling Problem
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Evolutionary Scheduling: A Review
Genetic Programming and Evolvable Machines
A review on evolution of production scheduling with neural networks
Computers and Industrial Engineering
Production scheduling and rescheduling with genetic algorithms
Evolutionary Computation
IEEE Transactions on Neural Networks
A Hopfield neural network applied to the fuzzy maximum cut problem under credibility measure
Information Sciences: an International Journal
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This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.