A Genetic Algorithm for Job Shop Scheduling with Load Balancing

  • Sanja Petrovic
  • Carole Fayad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)


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.


Job shop scheduling fuzzy logic and fuzzy sets genetic algorithms lot-sizing load balancing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Fayad, C., Petrovic, S.: A Genetic Algorithm for Real-World Job Shop Scheduling. In: Ali, M., Esposito, M. (eds.) The 18th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Bari, Italy, 22-25 June. LNCS (LNAI), vol. 3533. Springer, Heidelberg (2005)Google Scholar
  2. Greene, W.: Dynamic Load-Balancing via a Genetic Algorithm. In: 13th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2001), Dallas, US, pp. 121–129 (2001)Google Scholar
  3. Kranzlmuller, D.: Scheduling and Load Balancing. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds.) PPAM 2004. LNCS, vol. 3019. Springer, Heidelberg (2003)Google Scholar
  4. Lee, S.-H., Lee, D.-W.: GA based adaptive load balancing approach for a distributed system. In: Zhang, J., He, J.-H., Fu, Y. (eds.) CIS 2004. LNCS, vol. 3314, pp. 182–187. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. Moon, D.H., Kim, D.K., Jung, J.Y.: An Operator Load-Balancing problem in a Semi-Automatic Parallel Machine Shop. Computers & Industrial Engineering 46, 355–362 (2004)CrossRefGoogle Scholar
  6. Petrovic, S., Fayad, C., Petrovic, D.: Job Shop Scheduling with Lot-Sizing and Batching in an Uncertain Real-World Environment. In: 2nd Multidisciplinary Conference on Scheduling: Theory and Applications (MISTA), NY, USA, July 18-21 (2005)Google Scholar
  7. Pinedo, M.: Scheduling Theory, Algorithms, and Systems, 2nd edn. Prentice Hall, Englewood Cliffs (2002)zbMATHGoogle Scholar
  8. Reeves, C.: Genetic Algorithms and Combinatorial Optimisation: Applications of Modern Heuristic Techniques. In: Rayward-Smith, V.J. (ed.), Alfred Waller Ltd, Henley-on-Thames (2005)Google Scholar
  9. Zomaya, A., Teh, Y.H.: Observations on Using Genetic Algorithms for Dynamic Load-Balancing. IEEE Transactions on Parallel and Distributed Systems 12(9), 899–911 (2001)CrossRefGoogle Scholar
  10. Wang, T., Fu, Y.: Application of An Improved Genetic Algorithm for Shop Floor Scheduling. Computer Integrated Manufacturing Systems 8(5), 392–420 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sanja Petrovic
    • 1
  • Carole Fayad
    • 1
  1. 1.School of Computer Science and Information TechnologyUniversity of NottinghamNottinghamUK

Personalised recommendations