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An iterated greedy metaheuristic for the blocking job shop scheduling problem

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Abstract

In this paper we consider a job shop scheduling problem with blocking (BJSS) constraints. Blocking constraints model the absence of buffers (zero buffer), whereas in the traditional job shop scheduling model buffers have infinite capacity. There are two known variants of this problem, namely the blocking job shop scheduling with swap allowed (BWS) and the one with no swap allowed (BNS). This scheduling problem is receiving an increasing interest in the recent literature, and we propose an Iterated Greedy (IG) algorithm to solve both variants of the problem. IG is a metaheuristic based on the repetition of a destruction phase, which removes part of the solution, and a construction phase, in which a new solution is obtained by applying an underlying greedy algorithm starting from the partial solution. A comparison with recent published results shows that the iterated greedy algorithm outperforms other state-of-the-art algorithms on benchmark instances. Moreover it is conceptually easy to implement and has a broad applicability to other constrained scheduling problems.

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References

  • Adams, J., Balas, E., Zawack, D.: The shifting bottleneck procedure for job shop scheduling. Manag. Sci. 34(3), 391–401 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  • Applegate, D., Cook, W.: A computational study of the job shop scheduling problem. ORSA J. Comput. 3(2), 149–156 (1991)

    Article  MATH  Google Scholar 

  • Cesta, A., Oddi, A., Smith, S.F.: Iterative flattening: a scalable method for solving multi-capacity scheduling problems. In: Proceedings of the National Conference on Artificial Intelligence, pp. 742–747 (2000)

  • D’Ariano, A., Pacciarelli, D., Pranzo, M.: A branch and bound algorithm for scheduling trains in a railway network. Eur. J. Oper. Res. 183(2), 643–657 (2007)

    Article  MATH  Google Scholar 

  • D’Ariano, A., D’Urgolo, P., Pacciarelli, D.: Optimal sequencing of aircrafts take-off and landing at a busy airport. In: Proceedings of the 13th IEEE Conference on Intelligent Transportation Systems, pp. 1569–1574 (2010)

  • Fisher, H., Thompson, G.L.: Probabilistic learning combinations of local job-shop scheduling rules. In: Muth, J.F., Thompson, G.L. (eds.) Industrial Scheduling. Prentice-Hall, New Jersey, Englewood Cliffs (1963)

    Google Scholar 

  • Gabel, T., Riedmiller, M.: On a successful application of multi-agent reinforcement learning to operations research benchmarks. In: Proceedings of the 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning (ADPRL), pp. 68–75 (2007)

  • Grabowski, J., Pempera, J.: Sequencing of jobs in some production system. Eur. J. Oper. Res. 125, 535–550 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  • Gröflin, H., Pham, D.N., Bürgy, R.: The flexible blocking job shop with transfer and set-up times. J. Comb. Optim. 22(2), 121–144 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  • Gröflin, H., Klinkert, A.: A new neighborhood and tabu search for the blocking job shop. Discret. Appl. Math. 157(17), 3643–3655 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  • Hall, N.G., Sriskandarajah, C.: A survey of machine scheduling problems with blocking and no-wait in process. Oper. Res. 44, 510–525 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  • Laborie, P.: Algorithms for propagating resource constraints in AI planning and scheduling: existing approaches and new results. Artif. Intell. 143, 151–188 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  • Laborie, P., Godard, D.: Self-adapting large neighborhood search: application to single-mode scheduling problems. In: Proceedings MISTA-07 (2007)

  • Lawrence, S.: Supplement to Resource Constrained Project Scheduling: An Experimental Investigation of Heuristic Scheduling Techniques. GSIA, Carnagie Mellon University, Pittsburg, PA (1984)

    Google Scholar 

  • Levner, E., Kats, V.B., Levit, V.E.: An improved algorithm for a cyclic robotic scheduling problem. Eur. J. Oper. Res. 97, 500–508 (1997)

    Article  MATH  Google Scholar 

  • Liu, S.Q., Kozan, E.: Scheduling trains with priorities: a no-wait blocking parallel-machine job-shop scheduling model. Transp. Sci. 45(2), 175–198 (2011)

    Article  Google Scholar 

  • Marchiori, E., Steenbeek, A.: An evolutionary algorithm for large set covering problems with applications to airline crew scheduling. Real-World Applications of Evolutionary Computing, EvoWorkshops. Lecture Notes in Computer Science, pp. 367–381 (2000)

  • Mascis, A., Pacciarelli, D.: Job-shop scheduling with blocking and no-wait constraints. Eur. J. Oper. Res. 143(3), 498–517 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  • Mati, Y., Rezg, N., Xie, X.L.: Geometric approach and taboo search for scheduling flexible manufacturing systems. IEEE Trans. Robot. Autom. 17(6), 805–818 (2001)

    Article  Google Scholar 

  • Mati, Y., Rezg, N., Xie, X.L.: A taboo search approach for deadlock-free scheduling of automated manufacturing systems. J. Intell. Manuf. 12(5–6), 535–552 (2001)

    Article  Google Scholar 

  • Mati, Y., Xie, X.: Multiresource shop scheduling with resource flexibility and blocking. IEEE Trans. Autom. Sci. Eng. 8(1), 175–189 (2011)

    Article  Google Scholar 

  • Meersmans, P.J.M.: Optimization of container handling systems. Ph.D. Thesis, Erasmus University Rotterdam (2002)

  • Meloni, C., Pacciarelli, D., Pranzo, M.: A rollout metaheuristic for job shop scheduling problems. Ann. Oper. Res. 131(1–4), 215–235 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  • Oddi, A., Rasconi, R., Cesta, A., Smith, S.F.: Iterative improvement algorithms for the blocking job shop. In: Twenty-Second International Conference on Automated Planning and Scheduling (2012)

  • Pacciarelli, D.: Alternative graph formulation for solving complex factory-scheduling problems. Int. J. Prod. Res. 40(15), 3641–3653 (2004)

    Article  MATH  Google Scholar 

  • Pacciarelli, D., Pranzo, M.: Production scheduling in a steelmaking-continuous casting plant. Comput. Chem. Eng. 28(12), 2823–2835 (2004)

    Article  Google Scholar 

  • Pham, D.-N., Klinkert, A.: Surgical case scheduling as a generalized job shop scheduling problem. Eur. J. Oper. Res. 185(3), 1011–1025 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  • Pranzo, M., Meloni, C., Pacciarelli, D.: A new class of greedy heuristics for job shop scheduling problems. Proceedings of the 4th International Conference on Experimental and Efficient Algorithms 2647, 223–236 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  • Ribas, I., Companys, R., Tort-Martorell, X.: An iterated greedy algorithm for the flowshop scheduling problem with blocking. Omega Int. J. Manag. Sci. 39(3), 293–301 (2011)

    Article  Google Scholar 

  • Ropke, S., Pisinger, D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transp. Sci. 40(4), 455–472 (2006)

    Article  Google Scholar 

  • Ropke, S., Pisinger, D.: A unified heuristic for a large class of vehicle routing problems with backhauls. Eur. J. Oper. Res. 171(3), 750–775 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  • Ruiz, R., Stützle, T.: A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. Eur. J. Oper. Res. 177(3), 2033–2049 (2007)

    Article  MATH  Google Scholar 

  • Ruiz, R., Stützle, T.: An iterated greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives. Eur. J. Oper. Res. 187(3), 1143–1159 (2008)

    Article  MATH  Google Scholar 

  • Schrimpf, G., Schneider, J., Stamm-Wilbrandt, H., Dueck, G.: Record breaking optimization results using the ruin and recreate principle. J. Comput. Phys. 159(2), 139–171 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  • Shaw, P.: A new local search algorithm providing high quality solutions to vehicle routing problems. Departement of Computer Sciences, University of Strathclyde, Glasgow, Scotland, Technical Report, APES group (1997)

  • Zhu, J., Li, X.P.: An efficient metaheuristic for the blocking job shop problem with the makespan minimization. In: International IEEE Conference on Machine Learning and Cybernetics (ICMLC), pp. 1352–1357 (2011)

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Correspondence to Marco Pranzo.

Appendix

Appendix

See Tables 5, 6, 7, 8 and 9.

Table 5 Speed of the proposed IG algorithms
Table 6 Comparisons focused on different instance sizes between GPB, CP-OPT, IFS, IG.RW and IG.SA algorithms for the BWS
Table 7 Details on the performance of the IG algorithms for the BWS case on the Lawrence testbed
Table 8 Comparisons focused on different instance sizes between MX, IG.RW, IG.SA algorithms for the BNS
Table 9 Details on the performance of the IG algorithms for the BNS case on the Lawrence testbed

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Pranzo, M., Pacciarelli, D. An iterated greedy metaheuristic for the blocking job shop scheduling problem. J Heuristics 22, 587–611 (2016). https://doi.org/10.1007/s10732-014-9279-5

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  • DOI: https://doi.org/10.1007/s10732-014-9279-5

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