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An optimization-based heuristic for the machine reassignment problem

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Abstract

We address the machine reassignment problem proposed in the context of the ROADEF/EURO challenge 2012 in partnership with Google. The problem consists in reassigning a set of processes to a set of multiple-resource machines so as to minimize a weighted function of the machines load, the resources balance, and the costs of moving processes while satisfying numerous constraints. We propose an optimization-based heuristic that requires decomposing the problem into a sequence of small-sized instances that are iteratively solved using a general MIP solver. To speed-up the solution process several algorithmic expedients are embedded. Extensive computational experiments provide evidence that the proposed approach exhibits a very good performance.

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References

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

    Article  Google Scholar 

  • Archetti, C., Speranza, M. G., & Savelsbergh, M. W. P. (2008). An optimization-based heuristic for the split delivery vehicle routing problem. Transportation Science, 42(1), 22–31.

    Article  Google Scholar 

  • Baykasoglu, A., Ozbakir, L., & Tapkan, P. (2007). Artificial bee colony algorithm and its application to generalized assignment problem. Swarm Intelligence: Focus on Ant and Particle Swarm Optimization, 113–144.

  • Boschetti, M. A., Maniezzo, V., Roffilli, M., & Röhler, A. B. (2009). Matheuristics: Optimization, simulation and control. Hybrid metaheuristics (pp. 171–177). Berlin: Springer.

    Chapter  Google Scholar 

  • Caprara, A., & Toth, P. (2001). Lower bounds and algorithms for the 2-dimensional vector packing problem. Discrete Applied Mathematics, 111(3), 231–262.

    Article  Google Scholar 

  • Carlier, J., Haouari, M., Kharbeche, M., & Moukrim, A. (2010). An optimization-based heuristic for the robotic cell problem. European Journal of Operational Research, 202(3), 636–645.

    Article  Google Scholar 

  • Cremonesi, P., & Sansottera, A. (2012). Modeling response times in the Google ROADEF/EURO Challenge. Performance Evaluation Review, 40, 80–82.

    Article  Google Scholar 

  • Ferrer, A. J., HernáNdez, F., Tordsson, J., Elmroth, E., Ali-Eldin, A., Zsigri, C., et al. (2012). OPTIMIS: A holistic approach to cloud service provisioning. Future Generation Computer Systems, 28(1), 66–77.

    Article  Google Scholar 

  • Gavranović, H., Buljubasić, M., & Demirović, E. (2012). Variable neighborhood search for google machine reassignment problem. Electronic Notes in Discrete Mathematics, 39, 209–216.

    Article  Google Scholar 

  • Gharbi, A., & Haouari, M. (2007). An approximate decomposition algorithm for scheduling on parallel machines with heads and tails. Computers and Operations Research, 34, 868–883.

    Article  Google Scholar 

  • Google roadef/euro challenge. (2012). Final results. http://challenge.roadef.org/2012/en/results.php.

  • Google roadef/euro challenge. (2012). Machine reassignment. http://challenge.roadef.org/2012/files/problem_definition_v1.

  • Haddadi, S., & Ouzia, H. (2004). Effective algorithm and heuristic for the generalized assignment problem. European Journal of Operational Research, 153(1), 184–190.

    Article  Google Scholar 

  • Haouari, M., Gharbi, A., & Jemmali, M. (2006). Tight bounds for the identical parallel machine scheduling problem. International Transactions in Operational Research, 13(6), 529–548.

    Article  Google Scholar 

  • Kellerer, H., & Kotov, V. (2003). An approximation algorithm with absolute worst-case performance ratio 2 for two-dimensional vector packing. Operations Research Letters, 31(1), 35–41.

    Article  Google Scholar 

  • Kell, B., & van Hoeve, W.-J. (2013). An MDD approach to multidimensional bin packing. In C. Gomes & M. Sellmann (Eds.), CPAIOR 2013 LNCS (Vol. 7874, pp. 128–143). Heidelberg: Springer.

    Google Scholar 

  • Malitsky, Y., Mehta, D., O’Sullivan, B., & Simonis, H. (2013). Tuning parameters of large neighborhood search for the machine reassignment problem. Integration of AI and OR techniques in constraint programming for combinatorial optimization problems (pp. 176–192). Berlin: Springer.

    Chapter  Google Scholar 

  • Masson, R., Vidal, T., Michallet, J., Penna, P. H. V., Petrucci, V., Subramanian, A., et al. (2013). An iterated local search heuristic for multi-capacity bin packing and machine reassignment problems. Expert Systems with Applications, 40, 5266–5275.

    Article  Google Scholar 

  • Mehrotra, A., Johnson, E. L., & Nemhauser, G. L. (1998). An optimization based heuristic for political districting. Management Science, 44(8), 1100–1114.

    Article  Google Scholar 

  • Mehta, D., Sullivan, B. Ó., & Simonis, H. (2012). Comparing solution methods for the machine reassignment problem. In Lecture notes in computer science: principles and practice of constraint programming (pp. 782–797).

  • Moffitt, M. D. (2013). Multidimensional bin packing revisited. Principles and practice of constraint programming (pp. 513–528). Berlin: Springer.

    Chapter  Google Scholar 

  • Monaci, M., & Toth, P. (2006). A set-covering-based heuristic approach for bin-packing problems. INFORMS Journal on Computing, 18(1), 71–85.

    Article  Google Scholar 

  • Nauss, R. M. (2006). The generalized assignment problem. Integer Programming: Theory and Practice, 39–55.

  • Panigrahy, R., Talwar, K., Uyeda, L., & Wieder, U. (2011). Heuristics for vector bin packing. Microsoft: Technical Report.

  • Petrucci, V., Carrera, E. V., Loques, O., Leite, J. C. B., & Mosse, D. (2011). Optimized management of power and performance for virtualized heterogeneous server clusters. In Cluster, cloud and grid computing (CCGrid), 2011 11th IEEE/ACM international symposium on (pp. 23–32). IEEE.

  • Portal, G. M. (2012). An algorithmic study of the machine reassignment problem. Porto Alegre: Universidade Federal do Rio Grande do Sul.

    Google Scholar 

  • Romeijn, H. E., & Morales, D. R. (2000). A class of greedy algorithms for the generalized assignment problem. Discrete Applied Mathematics, 103(1), 209–235.

    Article  Google Scholar 

  • Savelsbergh, M. (1997). A branch-and-price algorithm for the generalized assignment problem. Operations Research, 45(6), 831–841.

    Article  Google Scholar 

  • Spieksma, F. C. R. (1994). A branch-and-bound algorithm for the two-dimensional vector packing problem. Computers and Operations Research, 21(1), 19–25.

    Article  Google Scholar 

  • Wauters, T. (2012). Reinforcement learning enhanced heuristic search for combinatorial optimization. University of Maastricht, The Netherlands, Ph.D. Dissertation (2012).

  • Wilcox, D., McNabb, A., & Seppi, K. (2011). Solving virtual machine packing with a reordering grouping genetic algorithm. In IEEE congress on evolutionary computation (CEC), pp. 362–369.

  • Yagiura, M., Ibaraki, T., & Glover, F. (2004). An ejection chain approach for the generalized assignment problem. INFORMS Journal on Computing, 16(2), 133–151.

    Article  Google Scholar 

  • Yagiura, M., Ibaraki, T., & Glover, F. (2006). A path relinking approach with ejection chains for the generalized assignment problem. European Journal of Operational Research, 169(2), 548–569.

    Article  Google Scholar 

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Acknowledgments

We do thank the ROADEF/EURO challenge organizers for their excellent job. Congratulations to the winners—Haris, Mirsad and Emir—for their bright achievement, and best wishes to all the competitors who made the contest as exciting as possible. Special thanks to the Dean of College of Engineering at King Saud University, Pr. Khalid Alhumaizi, for providing us with computer facilities, and to two anonymous referees whose insights substantially improved the quality of the paper. This research has been funded by the Deanship of Scientific Research at King Saud University through the Research Group Project no RGP-296.

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Authors and Affiliations

  1. Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh, Saudi Arabia

    Mehdi Mrad & Anis Gharbi

  2. Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha, Qatar

    Mohamed Haouari

  3. Qatar Road Safety Studies Center, Qatar University, Doha, Qatar

    Mohamed Kharbeche

Authors
  1. Mehdi Mrad
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  2. Anis Gharbi
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  3. Mohamed Haouari
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  4. Mohamed Kharbeche
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Correspondence to Anis Gharbi.

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Mrad, M., Gharbi, A., Haouari, M. et al. An optimization-based heuristic for the machine reassignment problem. Ann Oper Res 242, 115–132 (2016). https://doi.org/10.1007/s10479-015-2002-6

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  • DOI: https://doi.org/10.1007/s10479-015-2002-6

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