Abstract
Despite the success of constraint programming (CP ) for scheduling, the much wider penetration of mixed integer programming (MIP ) technology into business applications means that many practical scheduling problems are being addressed with MIP, at least as an initial approach. Furthermore, there has been impressive and well-documented improvements in the power of generic MIP solvers over the past decade. We empirically demonstrate that on an existing set of resource allocation and scheduling problems standard MIP and CP models are now competitive with the state-of-the-art manual decomposition approach. Motivated by this result, we formulate two tightly coupled hybrid models based on constraint integer programming (CIP ) and demonstrate that these models, which embody advances in CP and MIP, are able to out-perform the CP, MIP, and decomposition models. We conclude that both MIP and CIP are technologies that should be considered along with CP for solving scheduling problems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Achterberg, T., Berthold, T.: Hybrid Branching. In: van Hoeve, W.-J., Hooker, J.N. (eds.) CPAIOR 2009. LNCS, vol. 5547, pp. 309–311. Springer, Heidelberg (2009)
Achterberg, T.: Conflict analysis in mixed integer programming. Discrete Optimization 4(1), 4–20 (2007); special issue: Mixed Integer Programming
Achterberg, T.: Constraint Integer Programming. Ph.D. thesis, Technische Universität Berlin (2007)
Achterberg, T.: SCIP: Solving Constraint Integer Programs. Mathematical Programming Computation 1(1), 1–41 (2009)
Achterberg, T., Brinkmann, R., Wedler, M.: Property checking with constraint integer programming. ZIB-Report 07-37, Zuse Institute Berlin (2007)
Baptiste, P., Pape, C.L., Nuijten, W.: Constraint-based Scheduling. Kluwer Academic Publishers (2001)
Bartak, R., Salido, M.A., Rossi, F.: New trends on constraint satisfaction, planning, and scheduling: a survey. The Knowledge Engineering Review 25(3), 249–279 (2010)
Beck, J.C., Refalo, P.: A hybrid approach to scheduling with earliness and tardiness costs. Annals of Operations Research 118, 49–71 (2003)
Beck, J.C.: Checking-Up on Branch-and-Check. In: Cohen, D. (ed.) CP 2010. LNCS, vol. 6308, pp. 84–98. Springer, Heidelberg (2010)
Beck, J.C., Fox, M.S.: Constraint directed techniques for scheduling with alternative activities. Artificial Intelligence 121(1-2), 211–250 (2000)
Berthold, T., Heinz, S., Lübbecke, M.E., Möhring, R.H., Schulz, J.: A Constraint Integer Programming Approach for Resource-Constrained Project Scheduling. In: Lodi, A., Milano, M., Toth, P. (eds.) CPAIOR 2010. LNCS, vol. 6140, pp. 313–317. Springer, Heidelberg (2010)
Berthold, T., Heinz, S., Pfetsch, M.E.: Nonlinear Pseudo-Boolean Optimization: Relaxation or Propagation? In: Kullmann, O. (ed.) SAT 2009. LNCS, vol. 5584, pp. 441–446. Springer, Heidelberg (2009)
Berthold, T., Heinz, S., Vigerske, S.: Extending a CIP framework to solve MIQCPs. ZIB-Report 09-23, Zuse Institute Berlin (2009)
Debruyne, R., Bessière, C.: Some practicable filtering techniques for the constraint satisfaction problem. In: Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI 1997), pp. 412–417 (1997)
Heinz, S., Beck, J.C.: Solving resource allocation/scheduling problems with constraint integer programming. In: Salido, M.A., Barták, R., Policella, N. (eds.) Proceedings of the Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems (COPLAS 2011), pp. 23–30 (2011)
Heinz, S., Beck, J.C.: Reconsidering mixed integer programming and MIP-based hybrids for scheduling. ZIB-Report 12-05, Zuse Institute Berlin (2012)
Heinz, S., Schulz, J.: Explanations for the Cumulative Constraint: An Experimental Study. In: Pardalos, P.M., Rebennack, S. (eds.) SEA 2011. LNCS, vol. 6630, pp. 400–409. Springer, Heidelberg (2011)
Hooker, J.N.: Planning and Scheduling to Minimize Tardiness. In: van Beek, P. (ed.) CP 2005. LNCS, vol. 3709, pp. 314–327. Springer, Heidelberg (2005)
Hooker, J.N.: Integrated Methods for Optimization. Springer (2007)
Hooker, J.N.: Planning and scheduling by logic-based Benders decomposition. Operations Research 55, 588–602 (2007)
Hooker, J.N., Ottosson, G.: Logic-based Benders decomposition. Mathematical Programming 96, 33–60 (2003)
Koch, T., Achterberg, T., Andersen, E., Bastert, O., Berthold, T., Bixby, R.E., Danna, E., Gamrath, G., Gleixner, A.M., Heinz, S., Lodi, A., Mittelmann, H., Ralphs, T., Salvagnin, D., Steffy, D.E., Wolter, K.: MIPLIB 2010. Mathematical Programming Computation 3(2), 103–163 (2011)
Marques-Silva, J.P., Sakallah, K.A.: GRASP: A search algorithm for propositional satisfiability. IEEE Transactions on Computers 48(5), 506–521 (1999)
Martin, P., Shmoys, D.B.: A New Approach to Computing Optimal Schedules for the Job-Shop Scheduling Problem. In: Cunningham, W.H., Queyranne, M., McCormick, S.T. (eds.) IPCO 1996. LNCS, vol. 1084, pp. 389–403. Springer, Heidelberg (1996)
Milano, M., Van Hentenryck, P. (eds.): Hybrid Optimization: The Ten Years of CPAIOR. Springer (2010)
Schutt, A., Feydy, T., Stuckey, P., Wallace, M.: Explaining the cumulative propagator. Constraints, 1–33 (2010)
Wunderling, R.: Paralleler und objektorientierter Simplex-Algorithmus. Ph.D. thesis, Technische Universität Berlin (1996)
Yunes, T.H., Aron, I.D., Hooker, J.N.: An integrated solver for optimization problems. Operations Research 58(2), 342–356 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Heinz, S., Beck, J.C. (2012). Reconsidering Mixed Integer Programming and MIP-Based Hybrids for Scheduling. In: Beldiceanu, N., Jussien, N., Pinson, É. (eds) Integration of AI and OR Techniques in Contraint Programming for Combinatorial Optimzation Problems. CPAIOR 2012. Lecture Notes in Computer Science, vol 7298. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29828-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-29828-8_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29827-1
Online ISBN: 978-3-642-29828-8
eBook Packages: Computer ScienceComputer Science (R0)