Priority Scheduling for a Flexible Job Shop with a Reconfigurable Manufacturing Cell

  • Received : 2015.10.22
  • Accepted : 2016.02.24
  • Published : 2016.03.30


This paper considers a scheduling problem in a flexible job shop with a reconfigurable manufacturing cell. The flexible job shop has both operation and routing flexibilities, which can be represented in the form of a multiple process plan, i.e. each part can be processed through alternative operations, each of which can be processed on alternative machines. The scheduling problem has three decision variables: (a) selecting operation/machine pairs for each part; (b) sequencing of parts to be fed into the reconfigurable manufacturing cell; and (c) sequencing of the parts assigned to each machine. Due to the reconfigurable manufacturing cell's ability of adjusting the capacity, functionality and flexibility to the desired levels, the priority scheduling approach is proposed in which the three decisions are made at the same time by combining operation/machine selection rules, input sequencing rules and part sequencing rules. To show the performances of various rule combinations, simulation experiments were done on various instances generated randomly using the experiences of the manufacturing experts, and the results are reported for the objectives of minimizing makespan, mean flow time and mean tardiness, respectively.


Flexible Job Shop;Reconfigurable Manufacturing Cell;Multiple Process Plan;Priority Rules


  1. Baykasoglu, A., Ozbakir, L., and SOnmez, A. I. (2004), Using multiple objective tabu search and grammars to model and solve multi-objective flexible jobshop scheduling problems, Journal of Intelligent Manufacturing, 15(6), 777-785.
  2. Bi, Z. M., Lang, S. Y. Y., Shen, W., and Wang, L. (2008), Recnfigurable manufacturing systems: the state of the art, International Journal of Production Research, 46(4), 967-992.
  3. Chakravarty, A. K. and Shtub, A. (1990), Production scheduling during the phased implementation of flexible manufacturing cells, IIE Transactions, 22(4), 292-298.
  4. Doh, H.-H., Yu, J.-M., Kim, J.-S., Lee, D.-H., and Nam, S.-H. (2013), A priority scheduling approach for flexible job shops with multiple process plans, International Journal of Production Research, 51(12), 3748-3764.
  5. Gao, J., Sun, L., and Gen, M. (2008), A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems, Computers and Operation Research, 35(9), 2892-2907.
  6. Gao, J., Gen, M. and Sun, L. (2006), Scheduling jobs and maintenances in flexible job shop with a hybrid genetic algorithm, Journal of Intelligent Manufacturing, 17(4), 493-507.
  7. He, Y. and Smith, M. L. (2007), A dynamic heuristic based algorithm to part input sequencing in flexible manufacturing systems for mass customization capability, International Journal of Flexible Manufacturing Systems, 19(4), 392-409.
  8. Ho, Y. C. and Moodie, C. L. (1996), Solving cell formation problems in a manufacturing environment with flexible processing and routing capabilities, International Journal of Production Research, 34(10), 2901-2923.
  9. Kim, H.-W., Yu, J.-M., Kim, J.-S., Doh, H.-H., Lee, D.-H., and Nam, S.-H. (2012), Loading algorithms for flexible manufacturing systems with partially grouped unrelated machines and additional tooling constraints, International Journal of Advanced Manufacturing Technology, 58(5-8), 683-691.
  10. Kim, Y.-D., Lee, D.-H., and Yoon, C.-M. (2001), Twostage heuristic algorithms for part input sequencing in flexible manufacturing systems, European Journal of Opeartion Research, 133(3), 624-634.
  11. Kim, Y.-K., Park, K., and Ko, J. (2003), A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling, Computers and Operations Research, 30(8), 1151-1171.
  12. Koren, Y., Heisel, U., Jovane, F., Moriwaki, T., Pritschow, G., Ulsoy, G., and Brussel, H. (1999), Reconfigurable manufacturing systems, Annals of the CIRP, 48(2), 527-540.
  13. Lee, D.-H. and Kim, Y.-D. (1996), Part-mix allocation in a hybrid manufacturing system with a flexible manufacturing cell and a convention al jobshop, International Journal of Production Research, 34(5), 1347-1360.
  14. Lee, D.-H. and Kim, Y.-D. (1999), Scheduling algorithms for flexible manufacturing systems with partially grouped machines, Journal of Manufacturing Systems, 18(4), 301-309.
  15. Liang, M. and Dutta, S. P. (1992), Combined part selection, loading sharing and machine loading problems in hybrid manufacturing systems, International Journal of Production Research, 30(10), 2335-2349.
  16. Lim, S.-K. and Kim, Y.-D. (1998), Capacity planning for phased im-plementation of flexible manufacturing system under budget constraints, European Journal of Operational Research, 104(1), 175-186.
  17. Loukil, T., Teghem, J., and Fortemps, P. (2007), A multiobjective production scheduling case study solved by simulated annealing, European Journal of Operation Research, 179(3), 709-722.
  18. Mehrabi, M. G., Ulsoy, A. G., and Koren, Y. (2000), Reconfigurable manufacturing systems: key to future manufacturing, Journal of Intelligent Manufacturing, 11(4), 403-419.
  19. Mehrabi, M. G., Ulsoy, A. G., Koren, Y., and Heytler, P. (2002), Trends and perspectives in flexible and reconfigurable manufacturing systems, Journal of Intelligent Manufacturing, 13(2),135-146.
  20. Ozguven, C., Ozbakir, L., and Yavuz, Y. (2010), Mathematical models for job-shop scheduling problems with routing and process plan flexibility, Applied Mathematical Modelling, 34(6), 1539-1548.
  21. Rachamadugu, R. V. and Morton, T. E. (1981), Myopic heuristics for the single machine weighted tardiness problem, Working paper No. 28-81-82, Graduate school of Industrial Administration, Carnegie-Mellon University.
  22. Scrich, C. R., Armentano, V. A., and Laguna, M. (2004), Tardiness minimization in a flexible job shop: a tabu search approach, Journal of Intelligent Manufacturing, 15(1), 103-115.
  23. Suresh, N. C. and Sarkis, J. (1989), An MIP formulation for the phased implementation of FMS modules, Proceedings of the Second ORSA/TIMS Conference of Flexible Manufacturing System: Operations Research Models and Applications, 41-46.
  24. Vilcot, G. and Billaut, J.-C. (2008), A tabu search and a genetic algorithm for solving a bicriteria general job shop scheduling problem, European Journal of Operations Research, 190(2), 398-411.
  25. Xia, W. and Wu, Z. (2005), An effective hybrid optimization approach for multi-objective flexible job shop scheduling problems, Computers and Industrial Engineering, 48(2), 409-425.
  26. Yu, J.-M., Doh, H.-H., Kim, J.-S., Kwon, Y.-J., Lee, D.-H., and Nam, S.-H. (2013), Input sequencing and scheduling for a reconfigurable manufacturing system with a limited number of fixtures, International Journal of Advanced Manufacturing Technology, 67(1-4), 157-169.

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Grant : Development of a jig-center class 5-axis horizontal machining system capable of over 24 hours continuous operation

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