Machine learning applications to job shop scheduling
IEA/AIE '88 Proceedings of the 1st international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 2
Evolution based learning in a job shop scheduling environment
Computers and Operations Research - Special issue on genetic algorithms
A review of machine learning in dynamic scheduling of flexible manufacturing systems
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
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Performing complex, informed priority rules might pose a challenge for traditional operator-driven systems. However, computer-integrated manufacturing systems may significantly benefit from the complex, informed rules such as state-dependent priority rules. A state-dependent priority rule can be defined as a list of IF-THEN priority rules that will be performed if certain system conditions are satisfied. Here, we propose a genetic algorithm based learning system for constructing interval-based, state-dependent priority rules for each interval of queue lengths in dynamic job shops. Our approach builds interval based state-dependent priority rules pairing the priority rules with the intervals of queue lengths, and determines priority rules and their corresponding length of intervals for a given objective. A genetic algorithm is developed for matching queue length intervals with appropriate priority rules during simulation. A system simulation evaluates the efficiencies of interval based state dependent priority rules. The experiments show that interval-based state dependent priority rules obtained by the proposed approach considerably outperform the priority rules including shortest processing time (SPT), minimum slack time (MST), earlier due date (EDD), modified due date (MDD), cost over time (COVERT), and critical ratio (CR) for total tardiness for most of the problems.