Simulation Modeling and Analysis
Simulation Modeling and Analysis
Heuristics for hybrid flow shops with controllable processing times and assignable due dates
Computers and Operations Research
Advances in evolutionary computing: theory and applications
Advances in evolutionary computing: theory and applications
Fitness Function Optimized in Genetic Algorithm for Fabric Dynamic Simulation
PACIIA '08 Proceedings of the 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application - Volume 01
Simulation-based two-phase genetic algorithm for the capacitated re-entrant line scheduling problem
Computers and Industrial Engineering
Agent-Directed Simulation and Systems Engineering
Agent-Directed Simulation and Systems Engineering
Computers and Operations Research
An effective genetic algorithm for the flexible job-shop scheduling problem
Expert Systems with Applications: An International Journal
Using workflow for reconfigurable simulation-based planning and scheduling system
International Journal of Computer Integrated Manufacturing
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A successful implementation of a simulation-optimization (SO) methodology is presented. Based on evolutionary algorithms with a multicriteria fitness function, the new SO is used to developed weekly schedules at a ship building factory that manufactures around 60 jobs per week. Simulation modeling is used to account for randomness on the input data, as well as to correctly abstract the complex operations carried out in the real system. A variant of genetic algorithms is used to search for the appropriate schedule. Its fitness function is a multicriteria process capability index that aggregates three individual criteria, namely, makespan, line blockage and idleness of resources. The index is based on the satisfaction of thresholds for each and every criterion, thresholds that are tightened as improved schedules are found. The thresholds are also used to reject non-promising alternatives without having to perform the same number of runs as for the candidates that stand out for implementation. The name of the methodology is meSO: multicriteria evolutionary SO.