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Integration of Dispatch Rules for JSSP: A Learning Approach

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Soft Computing: Theories and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 742))

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Abstract

Dispatching rules have been studied from past decades, and it has concluded that these rules are composing vertebrae for various industrial scheduling applications. Generally, it is a very tedious process to develop incipient dispatching rules in a given atmosphere by indulging and implementing dissimilar models under consideration and also to appraise them through extensive research. For determining effectual dispatching rules, automatically, a pioneering approach is presented. Proposed work addresses job shop scheduling problem (JSSP) NP hard in nature with the objective of minimizing mean flow time. For achieving this, two latest dispatching rules have been introduced. These latest rules will combine the process time and total work content of a job in queue under subsequent process. Additive and alternative approaches have been taken for combining the rules. For evaluating the performance of proposed dispatching rules, a rigorous study has been carried out against S.P.T rule, WINQ rule, S.P.T and WINQ rule, and the other best existing rules in common practice.

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References

  1. Blackstone, J.H., Phillips, D.T., Hogg, G.L.: A state-of-the-art survey of dispatching rules for manufacturing job shop operations. Int. J. Prod. Res. 20, 27–45 (1982)

    Article  Google Scholar 

  2. Haupt, R.: A survey of priority rule-based scheduling. OR Spektrum 11, 3–16 (1989)

    Article  MathSciNet  Google Scholar 

  3. Ramasesh, R.: Dynamic job shop scheduling: a survey of simulation research. OMEGA 18, 43–57 (1990)

    Article  Google Scholar 

  4. Conway, R.W.: Priority dispatching and job lateness in a job shop. J. Ind. Eng. 16, 228–237 (1965)

    Google Scholar 

  5. Rochette, R., Sadowski, R.P.: A statistical comparison of the performance of simple dispatching rules for a particular set of job shops. Int. J. Prod. Res. 14, 63–75 (1976)

    Article  Google Scholar 

  6. O’Grady, P.J., Harrison, C.: A general search sequencing rule for job shop sequencing. Int. J. Prod. Res. 23, 961–973 (1985)

    Article  Google Scholar 

  7. Rajan, Kumar, V.: Hybridization of genetic algorithm & variable neighborhood search (VNGA) approach to crack the flexible job shop scheduling problem: a framework. J. Ind. Pollut. Control 33(2), 90–99 (2017)

    Google Scholar 

  8. Anderson, E.J., Nyirendra, J.C.: Two new rules to minimize tardiness in a job shop. Int. J. Prod. Res. 28, 2277–2292 (1990)

    Article  Google Scholar 

  9. Baker, K.R., Kanet, J.J.: Job shop scheduling with modified due dates. J. Oper. Manag. 4, 11–22 (1983)

    Article  Google Scholar 

  10. Raghu, T.S., Rajendran, C.: An efficient dynamic dispatching rule for scheduling in a job shop. Int. J. Prod. Econ. 32, 301–313 (1993)

    Article  Google Scholar 

  11. Vepsalainen, A.P.J., Morton, T.E.: Priority rules for job shops with weighted tardiness costs. Manag. Sci. 33, 1035–1047 (1987)

    Article  Google Scholar 

  12. Rajpurohit, J., Sharma, T.K., Abraham, A., Vaishali, A.: Glossary of metaheuristic algorithms. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 9, 181–205 (2017)

    Google Scholar 

  13. Rajan, Kumar, V.: Flow shop & job shop scheduling: mathematical models. Int. J. R&D Eng. Sci. Manag. 5(7), 1–7 (2017)

    Google Scholar 

  14. Baker, K.R.: Introduction to sequencing and scheduling. Wiley, New York (1974)

    Google Scholar 

  15. Law, A.M., Kelton, W.D.: Confidence intervals for steady state simulation: I. A survey of fixed sample size procedures. Oper. Res. 32, 1221–1239 (1984)

    Article  Google Scholar 

  16. Fishman, G.S.: Estimating sample size in computing simulation experiments. Manag. Sci. 18, 21–38 (1971)

    Article  Google Scholar 

  17. Adams, J., Balas, E., Zawack, D.: The shifting bottleneck procedure for job shopscheduling. Manag. Sci. 34, 391–401 (1988)

    Article  Google Scholar 

  18. Aarts, E.H.L., Van Laarhoven, P.J.M., Lenstra, J.K., Ulder, N.L.J.: A computational study of local search algorithms for job shop scheduling. ORSA J. Comput. 6, 118–125 (1994)

    Article  Google Scholar 

  19. Balas, E., Lenstra, J.K., Vazacopoulos, A.: The one-machine problem with delayed precedence constraints and its use in job shop scheduling. Manag. Sci. 41, 94–109 (1995)

    Article  Google Scholar 

  20. Bierwirth, C.: A generalized permutation approach to job shop scheduling with genetic algorithms. OR Spektrum 17, 87–92 (1995)

    Article  Google Scholar 

  21. Blazewicz, J., Ecker, K., Schmidt, G., Weglarz, J.: Scheduling in Computer and Manufacturing Systems, 2nd edn. Springer, Berlin (1994)

    Book  Google Scholar 

  22. Brucker, P.: Scheduling Algorithms. Springer, Berlin (1995)

    Book  Google Scholar 

  23. Brucker, P., Jurisch, B., Sievers, B.: A branch & bound algorithm for the jobshop problem. Discret. Appl. Math. 49, 107–127 (1994)

    Article  Google Scholar 

  24. Conway, R.W., Johnson, B.M., Maxwell, W.L.: An experimental investigation of priority dispatching. J. Ind. Eng. 11, 221–230 (1960)

    Google Scholar 

  25. Dell’Amico, M., Trubian, M.: Applying tabu search to the job-shop scheduling problem. Ann. Oper. Res. 41, 231–252 (1993)

    Article  Google Scholar 

  26. Dorndorf, U., Pesch, E.: Evolution based learning in a job shop scheduling environment. Comput. Oper. Res. 22, 25–40 (1995)

    Article  Google Scholar 

  27. Lawler, E.L.: Recent results in the theory of machine scheduling. In: Bachem, A., Grötschel, M., Korte, B. (eds.) Mathematical Programming: The State of the Art, pp. 202–234. Springer, Berlin (1983)

    Chapter  Google Scholar 

  28. Lawler, E.L., Lenstra, J.K., Kan, A.H.G.R.: Recent developments in deterministic sequencing and scheduling: a survey. In: Dempster, M.A.H. (1982)

    Google Scholar 

  29. Lenstra, J.K., Kan, A.H.G.R. (eds.): Deterministic and Stochastic Scheduling, pp. 35–73. Reidel, Dordrecht

    Google Scholar 

  30. Lorenzen, T.J., Anderson, V.L.: Design of Experiments: A No-Name Approach. Marcel Dekker, New York (1993)

    Book  Google Scholar 

  31. Montgomery, D.C.: Design and Analysis of Experiments. Wiley, New York (1991)

    MATH  Google Scholar 

  32. Kan, A.H.G.R.: Machine Scheduling Problems. Martinus Nijhoff, The Hague (1976)

    Google Scholar 

  33. Van Laarhoven, P.J.M., Aarts, E.H.L., Lenstra, J.K.: Job shop scheduling by simulated annealing. Oper. Res. 40, 113–125 (1992)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, University Institute of Engineering & Technology, Maharishi Dayanand University, Rohtak, Haryana, India

    Rajan & Vineet Kumar

Authors
  1. Rajan
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  2. Vineet Kumar
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Corresponding author

Correspondence to Rajan .

Editor information

Editors and Affiliations

  1. Department of Physics, Amity School of Applied Sciences, Amity University Rajasthan, Jaipur, Rajasthan, India

    Kanad Ray

  2. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, Rajasthan, India

    Tarun K. Sharma

  3. Department of Electronics and Communication Engineering, SEEC, Manipal University Jaipur, Jaipur, Rajasthan, India

    Sanyog Rawat

  4. Institute of Basic Science, Bundelkhand University, Jhansi, Uttar Pradesh, India

    R. K. Saini

  5. Advanced Key Technologies Division, Nano Characterization Unit, Surface Characterization Group, National Institute for Materials Science, Tsukuba, Ibaraki, Japan

    Anirban Bandyopadhyay

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Rajan, Kumar, V. (2019). Integration of Dispatch Rules for JSSP: A Learning Approach. In: Ray, K., Sharma, T., Rawat, S., Saini, R., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 742. Springer, Singapore. https://doi.org/10.1007/978-981-13-0589-4_57

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