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Metaheuristics for solving a multimodal home-healthcare scheduling problem

  • Gerhard Hiermann
  • Matthias Prandtstetter
  • Andrea Rendl
  • Jakob PuchingerEmail author
  • Günther R. Raidl
Original Paper

Abstract

We present a general framework for solving a real-world multimodal home-healthcare scheduling (MHS) problem from a major Austrian home-healthcare provider. The goal of MHS is to assign home-care staff to customers and determine efficient multimodal tours while considering staff and customer satisfaction. Our approach is designed to be as problem-independent as possible, such that the resulting methods can be easily adapted to MHS setups of other home-healthcare providers. We chose a two-stage approach: in the first stage, we generate initial solutions either via constraint programming techniques or by a random procedure. During the second stage, the initial solutions are (iteratively) improved by applying one of four metaheuristics: variable neighborhood search, a memetic algorithm, scatter search and a simulated annealing hyper-heuristic. An extensive computational comparison shows that the approach is capable of solving real-world instances in reasonable time and produces valid solutions within only a few seconds.

Keywords

Home health care Vehicle routing Optimization Metaheuristics 

Notes

Acknowledgments

This work is part of the project CareLog, partially funded by the Austrian Federal Ministry for Transport, Innovation and Technology (BMVIT) within the strategic programme I2VSplus under grant 826153. We thankfully acknowledge the CareLog project partners Verkehrsverbund Ost-Region GmbH (ITS Vienna Region), Sozial Global AG, and ilogs mobile software GmbH. We also thank our reviewers for their helpful comments.

References

  1. Bai R, Burke EK, Kendall G, Li J, McCollum B (2010) A hybrid evolutionary approach to the nurse rostering problem. IEEE Trans Evol Comput 14(4):580–590CrossRefGoogle Scholar
  2. Bai R, Blazewicz J, Burke EK, Kendall G, Mccollum B (2012) A simulated annealing hyper-heuristic methodology for flexible decision support. 4OR: Q J Oper Res 10(1):43–66CrossRefGoogle Scholar
  3. Begur SV, Miller DM, Weaver JR (1997) An integrated spatial DSS for scheduling and routing home-health-care nurses. Interfaces 27(4):35–48CrossRefGoogle Scholar
  4. Bertels S, Fahle T (2006) A hybrid setup for a hybrid scenario: combining heuristics for the home health care problem. Comput Oper Res 33:2866–2890CrossRefGoogle Scholar
  5. Bräysy O, Gendreau M (2005) Vehicle routing problem with time windows, part i: route construction and local search algorithms. Transp Sci 39(1):104–118CrossRefGoogle Scholar
  6. Bräysy O, Gendreau M (2005) Vehicle routing problem with time windows, part ii: metaheuristics. Transp Sci 39(1):119–139CrossRefGoogle Scholar
  7. Burke EK, Kendall G, Newall J, Hart E, Ross P, Schulenburg S (2003) Hyper-heuristics: an emerging direction in modern search technology. In: Glover G, Kochenberger K (eds) Handbook of metaheuristics, international series in operations research & management science, Chap. 16. Springer, Berlin, pp 457–474Google Scholar
  8. Burke EK, De Causmaecker P, Berghe GV, Van Landeghem H (2004) The state of the art of nurse rostering. J Sched 7(6):441–499CrossRefGoogle Scholar
  9. Burke EK, Curtois T, Qu R, Berghe GV (2010) A scatter search approach to the nurse rostering problem. J Oper Res Soc 61(11):1667–1679Google Scholar
  10. Cheng E, Rich JL (1998) A home health care routing and scheduling problem. Technical Report CAAM TR98-049, Rice UniversityGoogle Scholar
  11. Di Gaspero L, Schaerf A (2002) Multi-neighbourhood local search with application to course timetabling. In: Burke EK, Causmaecker PD (eds) PATAT, LNCS, vol 2740. Springer, Berlin, pp 262–275Google Scholar
  12. Eveborn P, Flisberg P, Rönnqvist M (2006) LAPS CARE—an operational system for staff planning. Eur J Oper Res 171:962–976CrossRefGoogle Scholar
  13. Eveborn P, Rönnqvist M, Einarsdóttir H, Eklund M, Lidén K, Almroth M (2009) Operations research improves quality and efficiency in home care. Interfaces 39(1):18–34CrossRefGoogle Scholar
  14. Glover F, Laguna M, Martí R (2000) Fundamentals of scatter search and path relinking. Control Cybern 29(3):653–684Google Scholar
  15. Hansen P, Mladenović N (2003) Variable neighborhood search. In: Glover FW, Kochenberger GA (eds) Handbook of metaheuristics. Kluwer, New York, pp 145–184Google Scholar
  16. Krzysztof K, Szymanek R (2011) Jacop java constraint solver. http://www.jacop.eu
  17. Lundy M, Mees A (1986) Convergence of an annealing algorithm. Math Program 34:111–124CrossRefGoogle Scholar
  18. Matta A, Chahed S, Sahin E, Dallery Y (2012) Modelling home care organisations from an operations management perspective. Flexi Serv Manuf J 1:1–25Google Scholar
  19. Moscato P (1999) Memetic algorithms: a short introduction. McGraw-Hill, MaidenheadGoogle Scholar
  20. Nikolaev AG, Jacobson SH (2010) Simulated annealing. In: Gendreau M, Potvin JY, Hillier FS (eds) Handbook of metaheuristics, international series in operations research & management science, vol 146. Springer, Berlin, pp 1–39Google Scholar
  21. Prandtstetter M, Raidl GR, Misar T (2009) A hybrid algorithm for computing tours in a spare parts warehouse. In: Cotta C, Cowling P (eds) Evolutionary computation in combinatorial optimization—EvoCOP 2009, LNCS, vol 5482. Springer, Berlin, pp 25–36CrossRefGoogle Scholar
  22. Prandtstetter M, Rendl A, Puchinger J (2012) The influence of accurate travel times on a home health care scheduling problem. In: Proceedings of the 5th international workshop on freight transportation and logistics. Mykonos, GreeceGoogle Scholar
  23. Rasmussen MS, Justesen T, Dohn A, Larsen J (2010) The home care crew scheduling problem: Preference-based visit clustering and temporal dependencies. Tech. Rep. 11–2010, DTU Management EngineeringGoogle Scholar
  24. Rendl A, Prandtstetter M, Hiermann G, Puchinger J, Raidl G (2012) Hybrid heuristics for multimodal homecare scheduling. In: Beldiceanu N, Jussien N, Pinson E (eds) 9th International conference on integration of AI and OR techniques in constraint programming for combinatorial optimization problems (CPAIOR’12), LNCS, vol 7298., Springer, Nantes, France, pp 339–355Google Scholar
  25. Rest KD, Hirsch P (2012) A tabu search approach for daily scheduling of home health care services using multi-modal transport. In: Odysseus 2012—5th international workshop on freight transportation and logistics, Mykonos, Greece, pp 373–377Google Scholar
  26. Rest KD, Trautsamwieser A, Hirsch P (2012) Trends and risks in home health care. J Humanit Logist Supply Chain Manag 2:34–53CrossRefGoogle Scholar
  27. Rossi F, van Beek P, Walsh T (2006) Handbook of constraint programming (foundations of artificial intelligence). Elsevier, New YorkGoogle Scholar
  28. Toplak W, Koller H, Dragaschnig M, Bauer D, Asamer J (2010) Novel road classifications for large scale traffic networks. In: Proceedings of the 13th international IEEE conference on intelligent transportation systems, pp 1264–1270Google Scholar
  29. Trautsamwieser A, Hirsch P (2011) Optimization of daily scheduling for home health care services. J Appl Oper Res 3:124–136Google Scholar
  30. Trautsamwieser A, Gronalt M, Hirsch P (2011) Securing home health care in times of natural disasters. OR Spectr 33:787–813Google Scholar
  31. Whitley D, Kauth J (1988) GENITOR: a different genetic algorithm. In: Proceedings of the 1988 Rocky Mountain conference on artificial intelligence, pp 118–130Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gerhard Hiermann
    • 1
  • Matthias Prandtstetter
    • 2
  • Andrea Rendl
    • 2
  • Jakob Puchinger
    • 2
    Email author
  • Günther R. Raidl
    • 1
  1. 1.Institute of Computer Graphics and AlgorithmsVienna University of TechnologyViennaAustria
  2. 2.Mobility Department, Dynamic Transportation SystemsAIT Austrian Institute of Technology GmbHViennaAustria

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