Production scheduling and rescheduling with genetic algorithms.
A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed at reasonable run-time costs.
Bierwirth
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C
Department of Economics, University of Bremen, Box 330440, D-28334 Bremen, Germany. chris@logistik.uni-bremen.de
Mattfeld
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DC
D000465:Algorithms; D001071:Appointments and Schedules; D005069:Evaluation Studies as Topic; D008957:Models, Genetic; D008962:Models, Theoretical; D013997:Time Factors