Investigating Constraint Programming for Real World Industrial Test Laboratory Scheduling
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In this paper we deal with a complex real world scheduling problem closely related to the well-known Resource-Constrained Project Scheduling Problem (RCPSP). The problem concerns industrial test laboratories in which a large number of tests has to be performed by qualified personnel using specialised equipment, while respecting deadlines and other constraints. We present different constraint programming models and search strategies for this problem. Our approaches are evaluated using CP solvers and a MIP solver on a set of generated instances of different sizes. With our best approach we could find feasible and several optimal solutions for instances that are generated based on real-world test laboratory problems.
The financial support by the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development is gratefully acknowledged. We would also like to thank the anonymous reviewers for their feedback, in particular regarding CP-modelling.
- 4.Chu, G.: Improving combinatorial optimization. Ph.D. thesis, University of Melbourne, Australia (2011). http://hdl.handle.net/11343/36679
- 6.Drezet, L.E., Billaut, J.C.: A project scheduling problem with labour constraints and time-dependent activities requirements. Int. J. Prod. Econ. 112(1), 217–225 (2008). https://doi.org/10.1016/j.ijpe.2006.08.021. Special Section on Recent Developments in the Design, Control, Planning and Scheduling of Productive SystemsCrossRefGoogle Scholar
- 8.Feydy, T., Goldwaser, A., Schutt, A., Stuckey, P.J., Young, K.D.: Priority search with MiniZinc. In: ModRef 2017: The Sixteenth International Workshop on Constraint Modelling and Reformulation at CP 2017 (2017)Google Scholar
- 10.IBM, CPLEX: 12.8.0 IBM ILOG CPLEX optimization studio CP optimizer user’s manual (2017). https://www.ibm.com/analytics/cplex-cp-optimizer
- 11.IBM, CPLEX: 12.8.0 IBM ILOG CPLEX optimization studio CPLEX user’s manual (2017). https://www.ibm.com/analytics/cplex-optimizer
- 12.Mika, M., Waligóra, G., Wȩglarz, J.: Overview and state of the art. In: Schwindt, C., Zimmermann, J. (eds.) Handbook on Project Management and Scheduling. International Handbooks on Information Systems, vol. 1, pp. 445–490. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-05443-8_21CrossRefGoogle Scholar
- 13.Mischek, F., Musliu, N.: A local search framework for industrial test laboratory scheduling. In: Proceedings of the 12th International Conference on the Practice and Theory of Automated Timetabling (PATAT-2018), Vienna, Austria, 28–31 August 2018, pp. 465–467 (2018)Google Scholar
- 14.Mischek, F., Musliu, N.: The test laboratory scheduling problem. Technical report, Christian Doppler Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling, TU Wien, CD-TR 2018/1 (2018)Google Scholar
- 19.Schulte, C., Lagerkvist, M., Tack, G.: Gecode 6.10 reference documentation (2018). https://www.gecode.org