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A Constraint Programming Approach for Solving Patient Transportation Problems

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Principles and Practice of Constraint Programming (CP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11008))

Abstract

The Patient Transportation Problem (PTP) aims to bring patients to health centers and to take them back home once the care has been delivered. All the requests are known beforehand and a schedule is built the day before its use. It is a variant of the well-known Dial-a-Ride Problem (DARP) but it has nevertheless some characteristics that complicate the decision process. Three levels of decisions are considered: selecting which requests to service, assigning vehicles to requests and routing properly the vehicles. In this paper, we propose a Constraint Programming approach to solve the Patient Transportation Problem. The model is designed to be flexible enough to accommodate new constraints and objective functions. Furthermore, we introduce a generic search strategy to maximize efficiently the number of selected requests. Our results show that the model can solve real life instances and outperforms greedy strategies typically performed by human schedulers.

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References

  1. Cordeau, J.F., Laporte, G.: The dial-a-ride problem: models and algorithms. Ann. Oper. Res. 153, 29–46 (2007)

    Article  MathSciNet  Google Scholar 

  2. Melachrinoudis, E., Min, H.: A tabu search heuristic for solving the multi-depot, multi-vehicle, double request dial-a-ride problem faced by a healthcare organisation. Int. J. Oper. Res. 10, 214–239 (2011)

    Article  MathSciNet  Google Scholar 

  3. Liu, R., Xie, X., Augusto, V., Rodriguez, C.: Heuristic algorithms for a vehicle routing problem with simultaneous delivery and pickup and time windows in home health care. Eur. J. Oper. Res. 230, 475–486 (2013)

    Article  MathSciNet  Google Scholar 

  4. Detti, P., Papalini, F., de Lara, G.Z.M.: A multi-depot dial-a-ride problem with heterogeneous vehicles and compatibility constraints in healthcare. Omega 70, 1–14 (2017)

    Article  Google Scholar 

  5. Cordeau, J.F., Laporte, G.: A tabu search heuristic for the static multi-vehicle dial-a-ride problem. Transp. Res. Part B Methodol. 37, 579–594 (2003)

    Article  Google Scholar 

  6. Parragh, S.N.: Introducing heterogeneous users and vehicles into models and algorithms for the dial-a-ride problem. Transp. Res. Part C Emerg. Technol. 19, 912–930 (2011)

    Article  Google Scholar 

  7. Parragh, S.N., Cordeau, J.F., Doerner, K.F., Hartl, R.F.: Models and algorithms for the heterogeneous dial-a-ride problem with driver-related constraints. OR Spectr. 34, 593–633 (2012)

    Article  MathSciNet  Google Scholar 

  8. Psaraftis, H.N.: An exact algorithm for the single vehicle many-to-many dial-a-ride problem with time windows. Transp. Sci. 17, 351–357 (1983)

    Article  Google Scholar 

  9. Melachrinoudis, E., Ilhan, A.B., Min, H.: A dial-a-ride problem for client transportation in a health-care organization. Comput. Oper. Res. 34, 742–759 (2007)

    Article  Google Scholar 

  10. Cordeau, J.F., Gendreau, M., Laporte, G.: A tabu search heuristic for periodic and multi-depot vehicle routing problems. Networks 30, 105–119 (1997)

    Article  Google Scholar 

  11. Parragh, S.N., Doerner, K.F., Hartl, R.F., Gandibleux, X.: A heuristic two-phase solution approach for the multi-objective dial-a-ride problem. Networks 54, 227–242 (2009)

    Article  MathSciNet  Google Scholar 

  12. Berbeglia, G., Cordeau, J.F., Gribkovskaia, I., Laporte, G.: Static pickup and delivery problems: a classification scheme and survey. Top 15, 1–31 (2007)

    Article  MathSciNet  Google Scholar 

  13. Attanasio, A., Cordeau, J.F., Ghiani, G., Laporte, G.: Parallel tabu search heuristics for the dynamic multi-vehicle dial-a-ride problem. Parallel Comput. 30, 377–387 (2004)

    Article  Google Scholar 

  14. Berbeglia, G., Pesant, G., Rousseau, L.M.: Checking the feasibility of dial-a-ride instances using constraint programming. Transp. Sci. 45, 399–412 (2011)

    Article  Google Scholar 

  15. Berbeglia, G., Cordeau, J.F., Laporte, G.: A hybrid tabu search and constraint programming algorithm for the dynamic dial-a-ride problem. INFORMS J. Comput. 24, 343–355 (2012)

    Article  MathSciNet  Google Scholar 

  16. Parragh, S.N., Schmid, V.: Hybrid column generation and large neighborhood search for the dial-a-ride problem. Comput. Oper. Res. 40, 490–497 (2013)

    Article  MathSciNet  Google Scholar 

  17. Jain, S., Van Hentenryck, P.: Large neighborhood search for dial-a-ride problems. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 400–413. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23786-7_31

    Chapter  Google Scholar 

  18. Liu, C., Aleman, D.M., Beck, J.C.: Modelling and solving the senior transportation problem. In: van Hoeve, W.-J. (ed.) CPAIOR 2018. LNCS, vol. 10848, pp. 412–428. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93031-2_30

    Chapter  Google Scholar 

  19. Beldiceanu, N., Carlsson, M., Rampon, J.X.: Global constraint catalog, (revision a) (2012)

    Google Scholar 

  20. Gay, S., Hartert, R., Schaus, P.: Simple and scalable time-table filtering for the cumulative constraint. In: Pesant, G. (ed.) CP 2015. LNCS, vol. 9255, pp. 149–157. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23219-5_11

    Chapter  MATH  Google Scholar 

  21. Vilím, P.: Timetable edge finding filtering algorithm for discrete cumulative resources. In: Achterberg, T., Beck, J.C. (eds.) CPAIOR 2011. LNCS, vol. 6697, pp. 230–245. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21311-3_22

    Chapter  MATH  Google Scholar 

  22. Gay, S., Hartert, R., Schaus, P.: Time-table disjunctive reasoning for the cumulative constraint. In: Michel, L. (ed.) CPAIOR 2015. LNCS, vol. 9075, pp. 157–172. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18008-3_11

    Chapter  MATH  Google Scholar 

  23. Ouellet, P., Quimper, C.-G.: Time-table extended-edge-finding for the cumulative constraint. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 562–577. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40627-0_42

    Chapter  Google Scholar 

  24. Schutt, A., Feydy, T., Stuckey, P.J., Wallace, M.G.: Explaining the cumulative propagator. Constraints 16, 250–282 (2011)

    Article  MathSciNet  Google Scholar 

  25. Simonis, H., Cornelissens, T.: Modelling producer/consumer constraints. In: Montanari, U., Rossi, F. (eds.) CP 1995. LNCS, vol. 976, pp. 449–462. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-60299-2_27

    Chapter  Google Scholar 

  26. Laborie, P., Rogerie, J.: Reasoning with conditional time-intervals. In: FLAIRS Conference, pp. 555–560 (2008)

    Google Scholar 

  27. Laborie, P., Rogerie, J., Shaw, P., Vilím, P.: Reasoning with conditional time-intervals. Part II: an algebraical model for resources. In: FLAIRS Conference, pp. 201–206 (2009)

    Google Scholar 

  28. Laborie, P., Rogerie, J., Shaw, P., Vilím, P.: IBM ILOG CP optimizer for scheduling. Constraints, 1–41 (2018)

    Google Scholar 

  29. Dejemeppe, C., Van Cauwelaert, S., Schaus, P.: The unary resource with transition times. In: Pesant, G. (ed.) CP 2015. LNCS, vol. 9255, pp. 89–104. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23219-5_7

    Chapter  Google Scholar 

  30. Ngatchou, P., Zarei, A., El-Sharkawi, A.: Pareto multi objective optimization. In: 2005 Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, pp. 84–91. IEEE (2005)

    Google Scholar 

  31. Gay, S., Hartert, R., Lecoutre, C., Schaus, P.: Conflict ordering search for scheduling problems. In: Pesant, G. (ed.) CP 2015. LNCS, vol. 9255, pp. 140–148. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23219-5_10

    Chapter  Google Scholar 

  32. Shaw, P.: Using constraint programming and local search methods to solve vehicle routing problems. In: Maher, M., Puget, J.-F. (eds.) CP 1998. LNCS, vol. 1520, pp. 417–431. Springer, Heidelberg (1998). https://doi.org/10.1007/3-540-49481-2_30

    Chapter  Google Scholar 

  33. Lauriere, J.L.: A language and a program for stating and solving combinatorial problems. Artif. Intell. 10, 29–127 (1978)

    Article  MathSciNet  Google Scholar 

  34. Vilím, P., Laborie, P., Shaw, P.: Failure-directed search for constraint-based scheduling. In: Michel, L. (ed.) CPAIOR 2015. LNCS, vol. 9075, pp. 437–453. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18008-3_30

    Chapter  MATH  Google Scholar 

  35. OscaR Team: OscaR: Scala in OR (2012). https://bitbucket.org/oscarlib/oscar

  36. Thomas, C., Cappart, Q., Schaus, P., Rousseau, L.M.: CSPLib problem 082: Patient transportation problem. http://www.csplib.org/Problems/prob082

  37. Godard, D., Laborie, P., Nuijten, W.: Randomized large neighborhood search for cumulative scheduling. ICAPS 5, 81–89 (2005)

    Google Scholar 

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Acknowledgments

This research is financed by the Walloon Region (Belgium) as part of PRESupply Project. The problem has been proposed by the CSD, a Belgian non-profit organization operating at Liège.

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Authors and Affiliations

  1. Université catholique de Louvain, Louvain-la-Neuve, Belgium

    Quentin Cappart, Charles Thomas & Pierre Schaus

  2. Ecole Polytechnique de Montréal, Montréal, Canada

    Quentin Cappart & Louis-Martin Rousseau

  3. Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Montréal, Canada

    Quentin Cappart & Louis-Martin Rousseau

Authors
  1. Quentin Cappart
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  2. Charles Thomas
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  3. Pierre Schaus
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  4. Louis-Martin Rousseau
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Corresponding author

Correspondence to Quentin Cappart .

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Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    John Hooker

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Cappart, Q., Thomas, C., Schaus, P., Rousseau, LM. (2018). A Constraint Programming Approach for Solving Patient Transportation Problems. In: Hooker, J. (eds) Principles and Practice of Constraint Programming. CP 2018. Lecture Notes in Computer Science(), vol 11008. Springer, Cham. https://doi.org/10.1007/978-3-319-98334-9_32

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  • DOI: https://doi.org/10.1007/978-3-319-98334-9_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98333-2

  • Online ISBN: 978-3-319-98334-9

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