Learning IF-THEN priority rules for dynamic job shops using genetic algorithms
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 (MOD) cost over time (COVERT) and critical ratio (CR) for total tardiness for most of the problems (C) 2010 Elsevier Ltd All rights reserved
if-then priority rule dynamic job shops genetic algorithm state dependent priority rule queue intervals machine learning