Advertisement

A Classification Schema for the Job Shop Scheduling Problem with Transportation Resources: State-of-the-Art Review

  • Houssem Eddine NouriEmail author
  • Olfa Belkahla Driss
  • Khaled Ghédira
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 464)

Abstract

The Job Shop scheduling Problem (JSP) is one of the most known problems in the domain of the production task scheduling. The Job Shop scheduling Problem with Transportation resources (JSPT) is a generalization of the classical JSP consisting of two sub-problems: the job scheduling problem and the generic vehicle scheduling problem. In this paper, we make a state-of-the-art review of the different works proposed for the JSPT, where we present a new classification schema according to seven criteria such as the transportation resource number, the transportation resource type, the job complexity, the routing flexibility, the recirculation constraint, the optimization criteria and the implemented approaches.

Keywords

Scheduling Transport Job shop Robot Flexible manufacturing system 

References

  1. 1.
    Anwar, M.F., Nagi, R.: Integrated scheduling of material handling and manufacturing activities for just-in-time production of complex assemblies. Int. J. Prod. Res. 36(3), 653–681 (1998)CrossRefzbMATHGoogle Scholar
  2. 2.
    Babu, A.G., Jerald, J., Noorul Haq, A., Muthu Luxmi, V., Vigneswaralu, T.P.: Scheduling of machines and automated guided vehicles in fms using differential evolution. Int. J. Prod. Res. 48(16), 4683–4699 (2010)CrossRefzbMATHGoogle Scholar
  3. 3.
    Bilge, U., Ulusoy, G.: A time window approach to simultaneous scheduling of machines and material handling system in an fms. Oper. Res. 43(6), 1058–1070 (1995)CrossRefzbMATHGoogle Scholar
  4. 4.
    Billaut, J., Tacquard, C., Martineau, P.: Modeling fms scheduling problems as hybrid flowshop scheduling problems. Stud. Inf. Control. 6(1), 25–30 (1997)Google Scholar
  5. 5.
    Braga, R.A.M., Rossetti, R.J.F., Reis, L.P., Oliveira, E.C.: Applying multi-agent systems to simulate dynamic control in flexible manufacturing scenarios. In: European Meeting on Cybernetics and Systems Research, vol. 2, pp. 488–493. A.S.C.S (2008)Google Scholar
  6. 6.
    Caumond, A., Lacomme, P., Moukrim, A., Techernev, N.: An milp for scheduling problems in an fms with one vehicle. Eur. J. Oper. Res. 199(3), 706–722 (2009)CrossRefzbMATHGoogle Scholar
  7. 7.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: nsga-ii. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  8. 8.
    Deroussi, L., Gourgand, M.: Un couplage métaheuristique/simulation appliqué au problème du job shop avec transport. Revue électronique Sciences et Technologies de l’Automatique. 4(2), 21–26 (2007)Google Scholar
  9. 9.
    Deroussi, L., Gourgand, M., Tchernev, N.: A simple metaheuristic approach to the simultaneous scheduling of machines and automated guided vehicles. Int. J. Prod. Res. 46(8), 2143–2164 (2008)CrossRefzbMATHGoogle Scholar
  10. 10.
    Deroussi, L., Norre, S.: Simultaneous scheduling of machines and vehicles for the flexible job shop problem. In: International Conference on Metaheuristics and Nature Inspired Computing, pp. 1–2, Djerba, Tunisia (2010)Google Scholar
  11. 11.
    El Khoukhi, F., Lamoudan, T., Boukachour, J., Alaoui, A.E.H.: Ant colony algorithm for just-in-time job shop scheduling with transportation times and multirobots. ISRN Appl. Math. 2011, 1–19 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Elmi, A., Solimanpur, M., Topaloglu, S., Elmi, A.: A simulated annealing algorithm for the job shop cell scheduling problem with intercellular moves and reentrant parts. Comput. Ind. Eng. 61(1), 171–178 (2011)CrossRefGoogle Scholar
  13. 13.
    Erol, R., Sahin, C., Baykasoglu, A., Kaplanoglu, V.: A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems. Appl. Soft Comput. 12(6), 1720–1732 (2012)CrossRefGoogle Scholar
  14. 14.
    Giffler, B., Thompson, G.L.: Algorithms for solving production scheduling problems. Oper. Res. 8(4), 487–503 (1960)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Graham, R.L., Lawler, E.L., Lenstra, J.K.: Rinnooy Kan, A.H.G.: Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann. Discret. Math. 5(2), 287–326 (1979)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Hurink, J., Knust, S.: A tabu search algorithm for scheduling a single robot in a job-shop environment. Discrete Appl. Math. 119(1–2), 181–203 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Hurink, J., Knust, S.: Tabu search algorithms for job-shop problems with a single transport robot. Eur. J. Oper. Res. 162(1), 99–111 (2005)CrossRefzbMATHGoogle Scholar
  18. 18.
    Hyunchul, K., Byungchul, A.: A new evolutionary algorithm based on sheep flocks heredity model. In: Pacific Rim Conference on Communications, Computers and Signal Processing, vol 2, pp. 514–517, Victoria, BC. IEEE Press (2001)Google Scholar
  19. 19.
    Jones, A., Rabelo, L.C.: Survey of job shop scheduling techniques. Technical report, National Institute of Standards and Technology, Gaithersburg, USA (1998)Google Scholar
  20. 20.
    Knust, S.: Shop-scheduling problems with transportation. PhD thesis, Fachbereich Mathematik/Informatik Universitat Osnabruck (1999)Google Scholar
  21. 21.
    Lacomme, P., Larabi, M., Tchernev, N.: A disjunctive graph for the job-shop with several robots. In: The 3rd Multidisciplinary International Conference on Scheduling : Theory and Applications, pp. 285–292. Paris, France (2007)Google Scholar
  22. 22.
    Lacomme, P., Larabi, M., Tchernev, N.: Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles. Int. J. Prod. Econ. 143(1), 24–34 (2013)CrossRefGoogle Scholar
  23. 23.
    Lenstra, J.K., Rinnooy kan, A.H.G.: Computational complexity of scheduling under precedence constraints. Oper. Res. 26(1), 22–35 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Liu, J., MacCarthy, B.L.: The classification of fms scheduling problems. Int. J. Prod. Res. 34(3), 647–656 (1996)CrossRefzbMATHGoogle Scholar
  25. 25.
    Mastrolilli, M., Gambardella, L.M.: Effective neighbourhood functions for the flexible job shop problem. J. Sched. 3(1), 3–20 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Miyamoto, T., Nakatyou, K., Kumagai, S.: Agent based planning method for an on-demand transportation system. In: International Symposium on Intelligent Control, pp. 620–625. IEEE Press, Houston, TX, USA (2003)Google Scholar
  27. 27.
    Morihiro, Y., Miyamoto, T., Kumagai, S.: An initial task assignment method for autonomous distributed vehicle systems with finite buffer capacity. In: International Conference on Emerging Technologies and Factory Automation, pp. 805–812. IEEE Press, Prague (2006)Google Scholar
  28. 28.
    Nageswararao, M., Narayanarao, K., Ranagajanardhana, G.: Simultaneous scheduling of machines and agvs in flexible manufacturing system with minimization of tardiness criterion. In: International Conference on Advances in Manufacturing and Materials Engineering. Procedia Materials Science. vol. 5, pp. 1492–1501 (2014)Google Scholar
  29. 29.
    Nowicki, E., Smutnicki, C.: A fast taboo search algorithm for the job shop problem. Manage. Sci. 42(6), 797–813 (1996)CrossRefzbMATHGoogle Scholar
  30. 30.
    Orloff, C.S.: Route constrained fleet scheduling. Transport. Sci. 10(2), 149–168 (1976)CrossRefGoogle Scholar
  31. 31.
    Pandian, P., Sankar, S., Ponnambalam, S.G.: Victor Raj, M.: Scheduling of automated guided vehicle and flexible jobshop using jumping genes genetic algorithm. Amer. J. Appl. Sci. 9(10), 1706–1720 (2012)CrossRefGoogle Scholar
  32. 32.
    Reddy, B.S.P., Rao, C.S.P.: A hybrid multi-objective ga for simultaneous scheduling of machines and agvs in fms. Int. J. Adv. Manuf. Technol. 31(5–6), 602–613 (2006)CrossRefGoogle Scholar
  33. 33.
    Rossi, A., Dini, G.: Flexible job-shop scheduling with routing flexibility and separable setup times using ant colony optimisation method. Robot. Comput. Integr. Manuf. 23(5), 503–516 (2007)CrossRefGoogle Scholar
  34. 34.
    Sabuncuoglu, I., Karabuk, S.: A beam search-based algorithm and evaluation of scheduling approaches for flexible manufacturing systems. IIE Trans. 30(2), 179–191 (1998)Google Scholar
  35. 35.
    Storn, R., Price, K.: Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report, International Computer Science Institute, Berkeley (1995)Google Scholar
  36. 36.
    Subbaiah, K.V., Nageswara Rao, M., Narayana Rao, K.: Scheduling of agvs and machines in fms with makespan criteria using sheep flock heredity algorithm. Int. J. Phys. Sci. 4(2), 139–148 (2009)Google Scholar
  37. 37.
    Ulusoy, G.: Sivrikaya Erifolu, F., Bilge, U.: A genetic algorithm approach to the simultaneous scheduling of machines and automated guided vehicles. Comput. Oper. Res. 24(4), 335–351 (1997)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Vignier, A., Billaut, J.C., Proust, C.: Solving k-stage hybrid flowshop scheduling problems. In: IMACS Multiconference : computational engineering in systems applications, pp. 250–258. Gerf EC Lille, Villeneuve d’Ascq, FRANCE (1996)Google Scholar
  39. 39.
    Zhang, Q., Manier, H., Manier, M.A.: A genetic algorithm with tabu search procedure for flexible job shop scheduling with transportation constraints and bounded processing times. Comput. Oper. Res. 39(7), 1713–1723 (2012)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Houssem Eddine Nouri
    • 1
    Email author
  • Olfa Belkahla Driss
    • 1
  • Khaled Ghédira
    • 1
  1. 1.Stratégies D’Optimisation et Informatique IntelligentEInstitut Supérieur de Gestion de Tunis, Université de TunisTunisTunisia

Personalised recommendations