Dynamic Real-Time Scheduling for Multi-Processor Tasks Using Genetic Algorithm
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Computers and Industrial Engineering
Computers and Industrial Engineering
Computers and Operations Research
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Computers and Industrial Engineering
Computers and Industrial Engineering
Discrepancy search for the flexible job shop scheduling problem
Computers and Operations Research
An effective genetic algorithm for the flexible job-shop scheduling problem
Expert Systems with Applications: An International Journal
Population-based dynamic scheduling optimisation for complex production process
International Journal of Computer Applications in Technology
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The paper presents a new genetic algorithm (GA)-based discrete dynamic programming (DDP) approach for generating static schedules in a flexible manufacturing system (FMS) environment. This GA-DDP approach adopts a sequence-dependent schedule generation strategy, where a GA is employed to generate feasible job sequences and a series of discrete dynamic programs are constructed to generate legal schedules for a given sequence of jobs. In formulating the GA, different performance criteria could be easily included. The developed DDF algorithm is capable of identifying locally optimized partial schedules and shares the computation efficiency of dynamic programming. The algorithm is designed In such a way that it does not suffer from the state explosion problem inherent in pure dynamic programming approaches in FMS scheduling. Numerical examples are reported to illustrate the approach