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A DSS for job scheduling under process interruptions

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Abstract

The primary objective of this research is to solve the job-shop scheduling problems (JSSPs), by minimizing the makespan, with and without process interruptions. In this paper, we first developed a genetic algorithm for solving JSSPs, and then improved the algorithm by integrating it with two simple priority rules and a hybrid rule. The performance of the developed algorithm was tested by solving 40 benchmark problems and comparing their results with that of a number of well-known algorithms. In addition, we have studied the job-shop scheduling under process interruptions such as machine unavailability and breakdown. For convenience of implementation, we have developed a decision support system (DSS). In the DSS, we built a graphical user interface for user friendly data inputs, model choices, and output generation. An overview of the DSS and an analysis of the experimental results are provided. The incorporation of priority rules, and a hybrid rule, not only improves the solutions but also reduces the overall computational time. The experimental results show that when the machine unavailability information is known in advance, the effect on the schedule is very little compared to the sudden machine breakdown scenario.

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References

  • Adams J, Balas E, Zawack D (1988) The shifting bottleneck procedure for job shop scheduling. Manage Sci 34:391–401

    Article  MathSciNet  MATH  Google Scholar 

  • Biegel JE, Davern JJ (1990) Genetic algorithms and job shop scheduling. Comput Ind Eng 19:81–91

    Article  Google Scholar 

  • Binato S, Hery WJ, Loewenstern D, Resende MGC (2001) A GRASP for job shop scheduling. In: Ribeiro CC, Hansen P (eds) Essays and surveys on metaheuristics, Kluwer Academic Publishers, Dordrecht, pp 59–79

    Google Scholar 

  • Blackstone JH Jr, Phillips DT, Hogg GL (1982) A state-of-the-art survey of dispatching rules for manufacturing job shop operations. Int J Prod Res 20(1):27

    Article  Google Scholar 

  • Croce FD, Tadei R, Volta G (1995) A genetic algorithm for the job shop problem. Comput Oper Res 22:15–24

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  • Eom HB, Lee SM (1990) A survey of decision support system applications (1971-April 1988). Interfaces 20:65–79

    Article  Google Scholar 

  • Eom SB, Lee SM, Kim EB, Somarajan C (1998) A survey of decision support system applications (1988–1994). J Oper Res Soc 49(2):109–120

    MATH  Google Scholar 

  • Garey MR, Johnson DS, Sethi R (1976) The Complexity of flowshop and jobshop scheduling. Math Oper Res 1:117–129

    Article  MathSciNet  MATH  Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Pub. Co, Reading

    MATH  Google Scholar 

  • Hasan SMK, Sarker R, Essam D, Cornforth D (2009) Memetic algorithms for solving job-shop scheduling problems. Memet Comput 1:69–83

    Article  Google Scholar 

  • Ishibuchi H, Murata T (1998) A multi-objective genetic local search algorithm and its application to flowshop scheduling. Syst Man Cybern C IEEE Trans 28:392–403

    Article  Google Scholar 

  • Kacem I (2009) Approximation algorithms for the makespan minimization with positive tails on a single machine with a fixed non-availability interval. J Comb Optim 17:117–133

    Article  MathSciNet  MATH  Google Scholar 

  • Kacem I, Chu C (2008) Efficient branch-and-bound algorithm for minimizing the weighted sum of completion times on a single machine with one availability constraint. Int J Prod Econ 112:138–150

    Article  Google Scholar 

  • Kacem I, Haouari M (2009) Approximation algorithms for single machine scheduling with one unavailability period. IEEE T Evolut Comput 7:79–92

    MathSciNet  MATH  Google Scholar 

  • Kumar M, Rajotia S (2006) Integration of process planning and scheduling in a job shop environment. Int J Adv Manuf Technol 28:109–116

    Article  Google Scholar 

  • Lawrence S (1984) Resource constrained project scheduling: an experimental investigation of heuristic scheduling techniques. Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh

    Google Scholar 

  • Lawrence D (1985) Job shop scheduling with genetic algorithms. In: First international conference on genetic algorithms. Lawrence Erlbaum Associates, Inc., Mahwah, NJ, USA

  • McKay KN, Buzacott JA (2000) The application of computerized production control systems in job shop environments. Comput Ind 42:79–97

    Article  Google Scholar 

  • McKay KN, Wiers VCS (2003) Integrated decision support for planning, scheduling, and dispatching tasks in a focused factory. Comput Ind 50:5–14

    Article  Google Scholar 

  • Nakano R, Yamada T (1991) Conventional genetic algorithm for job shop problems. In: Fourth international conference on genetic algorithms, Morgan Kaufmann, San Mateo, California, pp. 474–479

  • Ombuki BM, Ventresca M (2004) Local search genetic algorithms for the job shop scheduling problem. Appl Intell 21:99–109

    Article  MATH  Google Scholar 

  • Paredis J (1992) Handbook of evolutionary computation. Parallel problem solving from nature 2. Institute of Physics Publishing and Oxford University Press, Brussels

    Google Scholar 

  • Paredis J, Back T, Fogel D, Michalewicz Z (1997) Exploiting constraints as background knowledge for evolutionary algorithms. Handbook of Evolutionary Computation. Institute, pp G1.2:1–G1.2:16

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

    Article  Google Scholar 

  • Petrovic D, Duenas A, Petrovic S (2007) Decision support tool for multi-objective job shop scheduling problems with linguistically quantified decision functions. Decis Support Syst 43:1527–1538

    Article  Google Scholar 

  • Pezzella F, Morganti G, Ciaschetti G (2008) A genetic algorithm for the flexible job-shop scheduling problem. Comput Oper Res 35:3202–3212

    Article  MATH  Google Scholar 

  • Ponnambalam SG, Aravindan P, Rao PS (2001) Comparative evaluation of genetic algorithms for job-shop scheduling. Prod Plan Control 12:560–674

    Article  Google Scholar 

  • Shigenobu K, Isao O, Masayuki Y (1995) An efficient genetic algorithm for job shop scheduling problems. In: Proceedings of the 6th international conference on genetic algorithms. Morgan Kaufmann Publishers Inc

  • Silva C, Roque L, Almeida A (2006) MAPP–A web-based decision support system for the mould industry. Decis Support Syst 42:999–1014

    Article  Google Scholar 

  • Speranza MG, Woerlee AP (1991) A decision support system for operational production scheduling. Eur J Oper Res 55:329–343

    Article  Google Scholar 

  • Turban E (1993) Decision support systems: an overview. In: Stewart C (ed) Decision support and expert systems: management support systems. Macmillan, New York, pp 83–129

    Google Scholar 

  • Turban E (2007) Decision support and business intelligence systems. Pearson Prentice Hall, Upper Saddle River

    Google Scholar 

  • Viviers F (1983) A decision support system for job shop scheduling. Eur J Oper Res 14:95–103

    Article  Google Scholar 

  • Wang W, Brunn P (2000) An effective genetic algorithm for job shop scheduling. In: Proceedings of the institution of mechanical engineers—part b—engineering manufacture 214, pp 293–300

  • Wu DS, Storer RH, Pei-Chann C (1993) One-machine rescheduling heuristics with efficiency and stability as criteria. Comput Oper Res 20(1):1–14

    Article  MATH  Google Scholar 

  • Yamada T (2003) Studies on metaheuristics for jobshop and flowshop scheduling problems. Department of Applied Mathematics and Physics. Kyoto University, Kyoto, p 120

    Google Scholar 

  • Yamada T, Nakano R (1997a) Genetic algorithms for job-shop scheduling problems. In: Modern heuristic for decision support, UNICOM seminar, London, pp 67–81

  • Yamada T, Nakano R (1997b) Job-shop scheduling. In: Zalzala AMS, Fleming PJ (eds) Genetic algorithms in engineering systems. The Institution of Electrical Engineers, London, pp 134–160

    Google Scholar 

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Authors and Affiliations

  1. School of Engineering and Information Technology, University of New South Wales at the Australian Defence Force Academy, Northcott Drive, Canberra, ACT, 2600, Australia

    S. M. Kamrul Hasan, Ruhul Sarker & Daryl Essam

  2. Département Informatiqu, Université Paul Verlaine Metz, UFR M.I.M., LITA, Ile du Saulcy, 57000, Metz, France

    Imed Kacem

Authors
  1. S. M. Kamrul Hasan
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  2. Ruhul Sarker
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  3. Daryl Essam
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  4. Imed Kacem
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Correspondence to Ruhul Sarker.

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Hasan, S.M.K., Sarker, R., Essam, D. et al. A DSS for job scheduling under process interruptions. Flex Serv Manuf J 23, 137–155 (2011). https://doi.org/10.1007/s10696-011-9094-3

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