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Job Shop Scheduling with Transportation Delays and Layout Planning in Manufacturing Systems: A Multi-objective Evolutionary Approach

  • Kazi Shah Nawaz Ripon
  • Kyrre Glette
  • Mats Hovin
  • Jim Torresen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7326)

Abstract

The job shop scheduling problem (JSSP) and the facility layout planning (FLP) are two important factors influencing productivity and cost-controlling activities in any manufacturing system. In the past, a number of attempts have been made to solve these stubborn problems. Although, these two problems are strongly interconnected and solution of one significantly impacts the performance of other, so far, these problems are solved independently. Also, the majority of studies on JSSPs assume that the transportation delays among machines are negligible. In this paper, we introduce a general method using multi-objective genetic algorithm for solving the integrated problems of the FLP and the JSSP considering transportation delay having three objectives to optimize: makespan, total material handling costs, and closeness rating score. The proposed method makes use of Pareto dominance relationship to optimize multiple objectives simultaneously and a set of non-dominated solutions are obtained providing additional degrees of freedom for the production manager.

Keywords

Facility Layout Facility Layout Problem Material Handling Cost Transportation Delay Closeness Rating 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ripon, K.S.N., Siddique, N., Torresen, J.: Improved Precedence Preservation Crossover for Multi-Objective Job Shop Scheduling Problem. Evol. Syst. 2(2), 119–129 (2011)CrossRefGoogle Scholar
  2. 2.
    Sarker, R., Ray, T., Fonseca, J.: An Evolutionary Algorithm for Machine Layout and Job Assignment Problems. In: 2007 IEEE Congress on Evolutionary Computation (CEC 2007), pp. 3991–3997 (2007)Google Scholar
  3. 3.
    Tompkins, A.: Facilities Planning, 2nd edn. John Wiley & Sons, New York (2003)Google Scholar
  4. 4.
    Ławrynowic, A.: A Survey of Evolutionary Algorithms for Production and Logistics Optimization. Res. Logist. 1(2), 57–91 (2011)Google Scholar
  5. 5.
    Drira, A., Pierreval, H., Hajri-Gabouj, S.: Facility Layout Problems: A Survey. Annu. Rev. Control. 31(2), 255–267 (2007)CrossRefGoogle Scholar
  6. 6.
    Wang, L., Keshavarzmanesh, S., Feng, H.-Y.: A Hybrid Approach for Dynamic Assembly Shop Floor Layout. In: 2010 IEEE Conference on Automation Science and Engineering (CASE), pp. 604–609 (2010)Google Scholar
  7. 7.
    Aytug, H., Khouja, M., Vergara, F.E.: Use of Genetic Algorithms to Solve Production and Operations Management Problems: A Review. Int. J. Prod. Res. 41(17), 3955–4009 (2003)CrossRefGoogle Scholar
  8. 8.
    Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  9. 9.
    Grieco, A., Semeraro, Q., Tolio, T.: A Review of Different Approaches to the FMS Loading Problem. Int. J. Flex. Manuf. Sys. 13(4), 361–384 (2001)CrossRefGoogle Scholar
  10. 10.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA–II. IEEE T. Evolut. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  11. 11.
    Bierwirth, C.: A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms. OR Spectrum 17, 87–92 (1995)zbMATHCrossRefGoogle Scholar
  12. 12.
    Varela, R., Serrano, D., Sierra, M.: New Codification Schemas for Scheduling with Genetic Algorithms. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005, Part II. LNCS, vol. 3562, pp. 11–20. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Ripon, K.S.N., Glette, K., Mirmotahari, O., Høvin, M., Tørresen, J.: Pareto Optimal Based Evolutionary Approach for Solving Multi-Objective Facility Layout Problem. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009, Part II. LNCS, vol. 5864, pp. 159–168. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Suresh, G., Vinod, V.V., Sahu, S.: A Genetic Algorithm for Facility Layout. Int. J. Prod. Res. 33(12), 3411–3423 (1995)zbMATHCrossRefGoogle Scholar
  15. 15.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kazi Shah Nawaz Ripon
    • 1
  • Kyrre Glette
    • 1
  • Mats Hovin
    • 1
  • Jim Torresen
    • 1
  1. 1.Department of InformaticsUniversity of OsloNorway

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