A genetic algorithm for job shop scheduling with load balancing

Authors:
Sanja Petrovic;Carole Fayad
Affiliations:
School of Computer Science and Information Technology, University of Nottingham, Nottingham, UK;School of Computer Science and Information Technology, University of Nottingham, Nottingham, UK
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

Citing 5
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Abstract

This paper deals with the load-balancing of machines in a real-world job-shop scheduling problem with identical machines. The load-balancing algorithm allocates jobs, split into lots, on identical machines, with objectives to reduce job total throughput time and to improve machine utilization. A genetic algorithm is developed, whose fitness function evaluates the load-balancing in the generated schedule. This load-balancing algorithm is used within a multi-objective genetic algorithm, which minimizes average tardiness, number of tardy jobs, setup times, idle times of machines and throughput times of jobs. The performance of the algorithm is evaluated using real-world data and compared to the results obtained with no load-balancing.