Multiobjective Scheduling of Jobs with Incompatible Families on Parallel Batch Machines

  • Dirk Reichelt
  • Lars Mönch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3906)


We consider scheduling heuristics for batching machines from semiconductor manufacturing. A batch is a collection of jobs that are processed at the same time on the same machine. The processing time of a batch is given by the identical processing time of the jobs within one incompatible family. We are interested in minimizing total weighted tardiness and makespan at the same time. In order to solve this problem, i.e. generate a Pareto-front, we suggest a multiobjective genetic algorithm. We present results from computational experiments on stochastically generated test instances that show the good solution quality of the suggested approach.


Local Search Local Search Procedure Total Weighted Tardiness Multiobjective Genetic Algorithm Parallel Machine Schedule Problem 
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  1. 1.
    Carlyle, W.M., Kim, B., Fowler, J.W., Gel, E.S.: Comparison of Multiple Objective Genetic Algorithms for Parallel Machine Scheduling Problems. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 472–485. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  2. 2.
    Cochran, J.K., Horng, S.-M., Fowler, J.W.: A Multi Population Genetic Algorithm to Solve Parallel Machine Scheduling Problems with Sequence Dependent Setups. Computers & Operations Research 30(7), 1087–1102 (2003)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Deb, K., Goel, T.: A Hybrid Multi-objective Evolutionary Approach to Engineering Shape Design. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 385–399. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  4. 4.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  5. 5.
    Graham, R.L., Lawler, E.L., Lenstra, J.K., Rinnooy Kann, A.H.G.: Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey. Annals of Discrete Mathematics 5, 287–326 (1979)MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Gupta, A.K., Sivakumar, A.I.: On-time Delivery Pareto Controllability for Batch Processing in Semiconductor Manufacturing. In: Proceedings 3rd International Conference on Modeling and Analysis of Semiconductor Manufacturing Singapore, pp. 13–20 (2005)Google Scholar
  7. 7.
    Jaszkiewicz, A.: Evaluation of Multiple Objective Metaheuristics. In: Metaheuristics for Multiobjective Optimisation. Lecture Notes in Economics and Mathematical Systems, vol. 535, pp. 65–89. Springer, Berlin (2004)CrossRefGoogle Scholar
  8. 8.
    Jaszkiewicz, A.: MOMHLib++: Multiple Objective MetaHeuristics Library in C++ (2005),
  9. 9.
    Mathirajan, M., Sivakumar, A.I.: Scheduling of Batch Processors in Semiconductor Manufacturing - a Review. In: Singapure MIT Alliance (SMA) 2003 Symposium National University of Singapure (2003),
  10. 10.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, New York (1996)CrossRefMATHGoogle Scholar
  11. 11.
    Mönch, L., Balasubramanian, H., Fowler, J.W., Pfund, M.: Heuristic Scheduling of Jobs on Parallel Batch Machines with Incompatible Job Families and Unequal Ready Times. Computers & Operations Research 32, 2731–2750 (2005)CrossRefMATHGoogle Scholar
  12. 12.
    Murata, T., Ishibuchi, H., Tanaka, H.: Multi-objective Genetic Algorithm and its Application to Flow Shop Scheduling. Computers and Industrial Engineering 30(4), 957–968 (1996)CrossRefGoogle Scholar
  13. 13.
    Pinedo, M.: Scheduling: Theory, Algorithms, and Systems, 2nd edn. Prentice-Hall, New Jersey (2002)MATHGoogle Scholar
  14. 14.
    Potts, C.N., Kovalyov, M.Y.: Scheduling with Batching: a Review. European Journal of Operational Research 120, 228–249 (2000)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    T’kindt, V., Billaut, J.-C.: Multicriteria Scheduling: Theory, Models and Algorithms. Springer, New York (2002)CrossRefMATHGoogle Scholar
  16. 16.
    Van Veldhuizen, D.A.: Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. Air Force Institute of Technology Department of Electrical and Computer Engineering Ohio (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dirk Reichelt
    • 1
  • Lars Mönch
    • 1
  1. 1.Institute of Information SystemsTechnical University of IlmenauIlmenauGermany

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