Reinforcement learning algorithms for average-payoff Markovian decision processes
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Fuzzy Control
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Foundations of Fuzzy Systems
Scheduling rules for dynamic shops that manufacture multi-level jobs
Computers and Industrial Engineering
Multiobjective Evolutionary Algorithms and Applications (Advanced Information and Knowledge Processing)
Development of scheduling strategies with Genetic Fuzzy systems
Applied Soft Computing
Robotics and Computer-Integrated Manufacturing
Fuzzy rule generation for adaptive scheduling in a dynamic manufacturing environment
Applied Soft Computing
Computers and Industrial Engineering
A survey of dynamic scheduling in manufacturing systems
Journal of Scheduling
Dynamic scheduling of maintenance tasks in the petroleum industry: A reinforcement approach
Engineering Applications of Artificial Intelligence
Development of genetic fuzzy logic controllers for complex production systems
Computers and Industrial Engineering
Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing
Computers and Industrial Engineering
Application of reinforcement learning for agent-based production scheduling
Engineering Applications of Artificial Intelligence
Hybrid pattern search and simulated annealing for fuzzy production planning problems
Computers & Mathematics with Applications
Machine scheduling in custom furniture industry through neuro-evolutionary hybridization
Applied Soft Computing
Self-organizing state aggregation for architecture design of Q-learning
Information Sciences: an International Journal
Information Sciences: an International Journal
Development of hybrid evolutionary algorithms for production scheduling of hot strip mill
Computers and Operations Research
Engineering Applications of Artificial Intelligence
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Evolving priority scheduling heuristics with genetic programming
Applied Soft Computing
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This article addresses the problem of dynamic job scheduling on a single machine with Poisson arrivals, stochastic processing times and due dates, in the presence of sequence-dependent setups. The objectives of minimizing mean earliness and mean tardiness are considered. Two approaches for dynamic scheduling are proposed, a Reinforcement Learning-based and one based on Fuzzy Logic and multi-objective evolutionary optimization. The performance of the two scheduling approaches is tested against the performance of 15 dispatching rules in four simulation scenarios with different workload and due date pressure conditions. The scheduling methods are compared in terms of Pareto optimal-oriented metrics, as well as in terms of minimizing mean earliness and mean tardiness independently. The experimental results demonstrate the merits of the proposed methods.