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A Hybrid Intelligent Algorithm and Rescheduling Technique for Dynamic JSP

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Effective Methods for Integrated Process Planning and Scheduling

Part of the book series: Engineering Applications of Computational Methods ((EACM,volume 2))

Abstract

In most real manufacturing environments, schedules are usually inevitable with the presence of various unexpected disruptions. In this study, a new rescheduling technique based on a hybrid intelligent algorithm is developed for solving job shop scheduling problems with random job arrivals and machine breakdowns. According to the dynamic feature of this problem, a new initialization method is proposed to improve the performance of the hybrid intelligent algorithm, which combines the advantage of a genetic algorithm and tabu search. In order to solve the difficulty of using the mathematical model to express the unexpected disruptions, a simulator is designed to generate the disruptions. The performance measures investigated respectively are as follows: mean flow time, maximum flow time, mean tardiness, maximum tardiness, and the number of tardy jobs. Moreover, many experiments have been designed to test and evaluate the effect of different initializations in several disruption scenarios. Finally, the performance of the new rescheduling technique is compared with other rescheduling technologies in various shop floor conditions. The experimental results show that the proposed rescheduling technique is superior to other rescheduling techniques with respect to five objectives, different shop load level, and different due date tightness. The results also illustrate that the proposed rescheduling technique has good robustness in the dynamic manufacturing environment.

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References

  1. Adibi MA, Zandieh M, Amiri M (2010) Multi-objective scheduling of dynamic job shop using variable neighborhood search. Expert Syst Appl 37:282–287

    Article  Google Scholar 

  2. Chryssolouris G, Subramaniam V (2001) Dynamic scheduling of manufacturing job shops using genetic algorithms. J Intell Manuf 12:281–293

    Article  Google Scholar 

  3. Damodaran P, Hirani NS, Velez-Gallego MC (2009) Scheduling identical parallel batch processing machines to minimize makespan using genetic algorithms. Eur J Ind Eng 3:187–206

    Article  Google Scholar 

  4. Dominic PDD, Kaliyamoorthy S, Kumar MS (2004) Efficient dispatching rules for dynamic job shop scheduling. Int J Adv Manuf Technol 24:70–75

    Google Scholar 

  5. Gao L, Zhang GH, Zhang LP, Li XY (2011) An efficient memetic algorithm for solving the job shop scheduling problem. Comput Ind Eng 60:699–705

    Article  Google Scholar 

  6. Lei D (2011) Scheduling stochastic job shop subject to random breakdown to minimize makespan. Int J AdvManuf Technol 55:1183–1192

    Article  Google Scholar 

  7. Lin SC, Goodman ED, Punch WF (1997) A genetic algorithm approach to dynamic job shop scheduling problems. In: The 7th International Conference on Genetic Algorithm. Morgan Kaufmann, San Francisco

    Google Scholar 

  8. Liu L, Gu HY, Xi YG (2007) Robust and stable scheduling of a single machine with random machine breakdowns. Int J Adv Manuf Technol 31:645–654

    Article  Google Scholar 

  9. Liu SQ, Ong HL, Ng KM (2005) A fast tabu search algorithm for the group shop scheduling problem. Adv Eng Softw 36:533–539

    Article  Google Scholar 

  10. Lou P, Liu Q, Zhou Z, Wang H, Sun SX (2012) Multi-agent-based proactive—reactive scheduling for a job shop. Int J Adv Manuf Technol 59:311–324

    Article  Google Scholar 

  11. Malve S, Uzsoy R (2007) A genetic algorithm for minimizing maximum lateness on parallel identical batch processing machines with dynamic job arrivals and incompatible job families. Comput Oper Res 34:3016–3028

    Article  MathSciNet  Google Scholar 

  12. Megala N, Rajendran C, Gopalan R (2008) An ant colony algorithm for cell-formation in cellular manufacturing systems. Eur J Ind Eng 2:298–336

    Article  Google Scholar 

  13. Nie L, Shao X, Gao L, Li W (2010) Evolving scheduling rules with gene expression programming for dynamic single-machine scheduling problems. Int J Adv Manuf Technol 50:729–747

    Article  Google Scholar 

  14. Ouelhadj D, Petrovic S (2009) A survey of dynamic scheduling in manufacturing systems. J Sched 12:417–431

    Article  MathSciNet  Google Scholar 

  15. Pan QK, Wang L (2008) A novel differential evolution algorithm for the no-idle permutation flow shop scheduling problems. Eur J Ind Eng 2:279–297

    Article  MathSciNet  Google Scholar 

  16. Park BJ, Choi HR, Kim HS (2003) A hybrid genetic algorithm for the job shop scheduling problems. Comput Ind Eng 45(4):597–613

    Article  Google Scholar 

  17. Pessan C, Bouquard JL, Neron E (2008) An unrelated parallel machines model for an industrial production resetting problem. Eur J Ind Eng 2:153–171

    Article  Google Scholar 

  18. Rangsaritratsamee R, Ferrel JWG, Kurtz MB (2004) Dynamic rescheduling that simultaneously considers efficiency and stability. Comput Ind Eng 46:1–15

    Article  Google Scholar 

  19. Renna P (2010) Job shop scheduling by pheromone approach in a dynamic environment. Int J Comput Integr Manuf 23:412–424

    Article  Google Scholar 

  20. Sabuncuoglu I, Bayiz M (2000) Analysis of reactive scheduling problems in a job shop environment. Eur J Oper Res 126:567–586

    Article  Google Scholar 

  21. Sabuncuoglu I, Goren S (2009) Hedging production schedules against uncertainty in manufacturing environment with a review of robustness and stability research. Int J Comput Integr Manuf 22(2):138–157

    Article  Google Scholar 

  22. Shafaei R, Brunn P (1999) The performance of heuristic scheduling rules in a dynamic job shop environment using a rolling time horizon approach. Int J Prod Res 37:3913–3925

    Article  Google Scholar 

  23. Singh A, Mehta NK, Jain PK (2007) Multicriteria dynamic scheduling by swapping of dispatching rules. Int J Adv Manuf Technol 34:988–1007

    Article  Google Scholar 

  24. Subramaniam V, Lee GK, Ramesh T, Hong GS, Wong YS (2000) Machine selection rules in a dynamic job shop. Int J Adv Manuf Technol 16(12):902–908

    Article  Google Scholar 

  25. Van Hulle MM (1991) A goal programming network for mixed integer linear programming: a case study for the job-shop scheduling problem. Int J Neural Netw 2(3):201–209

    Article  Google Scholar 

  26. Vinod V, Sridharan R (2008) Dynamic job-shop scheduling with sequence-dependent setup times: simulation modeling and analysis. Int J Adv Manuf Technol 36(3–4):355–372

    Article  Google Scholar 

  27. Vinod V, Sridharan R (2011) Simulation modeling and analysis of due-date assignment methods and scheduling decision rules in a dynamic job shop production system. Int J Prod Econ 129:127–146

    Article  Google Scholar 

  28. Wu SS, Li BZ, Yang JG (2010) A three-fold approach to solve dynamic job shop scheduling problems by artificial immune algorithm. Adv Mater Res 139–141:1666–1669

    Article  Google Scholar 

  29. Xiang W, Lee HP (2008) Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Eng Appl Artif Intell 21:73–85

    Article  Google Scholar 

  30. Zandieh M, Adibi MA (2010) Dynamic job shop scheduling using variable neighbourhood search. Int J Prod Res 48:2449–2459

    Article  Google Scholar 

  31. Zhang CY, Rao YQ, Li PG (2008) An effective hybrid genetic algorithm for the job shop scheduling problem. Int J Adv Manuf Technol 39:965–974

    Article  Google Scholar 

  32. Zhou R, Lee HP, Nee AYC (2008) Applying ant colony optimization (ACO) algorithm to dynamic job shop scheduling problems. Int J Manuf Res 3(3):301–320

    Article  Google Scholar 

  33. Zhou R, Nee AYC, Lee HP (2009) Performance of an ant colony optimization algorithm in dynamic job shop scheduling problems. Int J Prod Res 47:2903–2920

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Mechanical Science and Engineering, HUST, State Key Laboratory of Digital Manufacturing and Equipment Technology, Wuhan, Hubei, China

    Xinyu Li

  2. School of Mechanical Science and Engineering, HUST, State Key Laboratory of Digital Manufacturing and Equipment Technology, Wuhan, Hubei, China

    Liang Gao

Authors
  1. Xinyu Li
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  2. Liang Gao
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Correspondence to Xinyu Li .

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Li, X., Gao, L. (2020). A Hybrid Intelligent Algorithm and Rescheduling Technique for Dynamic JSP. In: Effective Methods for Integrated Process Planning and Scheduling. Engineering Applications of Computational Methods, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-55305-3_17

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  • DOI: https://doi.org/10.1007/978-3-662-55305-3_17

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-55303-9

  • Online ISBN: 978-3-662-55305-3

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