An Investigation of Heuristic Decomposition to Tackle Workforce Scheduling and Routing with Time-Dependent Activities Constraints

  • Wasakorn LaesanklangEmail author
  • Dario Landa-Silva
  • J. Arturo Castillo-Salazar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 695)


This paper presents an investigation into the application of heuristic decomposition and mixed-integer programming to tackle workforce scheduling and routing problems (WSRP) that involve time-dependent activities constraints. These constraints refer to time-wise dependencies between activities. The decomposition method investigated here is called repeated decomposition with conflict repair (RDCR) and it consists of repeatedly applying a phase of problem decomposition and sub-problem solving, followed by a phase dedicated to conflict repair. In order to deal with the time-dependent activities constraints, the problem decomposition puts all activities associated to the same location and their dependent activities in the same sub-problem. This is to guarantee the satisfaction of time-dependent activities constraints as each sub-problem is solved exactly with an exact solver. Once the assignments are made, the time windows of dependent activities are fixed even if those activities are subject to the repair phase. The paper presents an experimental study to assess the performance of the decomposition method when compared to a tailored greedy heuristic. Results show that the proposed RDCR is an effective approach to harness the power of mixed integer programming solvers to tackle the difficult and highly constrained WSRP in practical computational time. Also, an analysis is conducted in order to understand how the performance of the different solution methods (the decomposition, the tailored heuristic and the MIP solver) is affected by the size of the problem instances and other features of the problem. The paper concludes by making some recommendations on the type of method that could be more suitable for different problem sizes.


Workforce scheduling and routing problem Time-dependent activities constraints Mixed integer programming Problem decomposition. 



The authors are grateful for access to the University of Nottingham High Performance Computing Facility. Also, the first author thanks the DPST Thailand for partial financial support of this research.


  1. 1.
    Hiermann, G., Prandtstetter, M., Rendl, A., Puchinger, J., Raidl, G.R.: Metaheuristics for solving a multimodal home-healthcare scheduling problem. CEJOR 23, 89–113 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Borsani, V., Andrea, M., Giacomo, B., Francesco, S.: A home care scheduling model for human resources. In: 2006 International Conference on Service Systems and Service Management, pp. 449–454 (2006)Google Scholar
  3. 3.
    Eveborn, P., Flisberg, P., Rönnqvist, M.: Laps care-an operational system for staff planning of home care. Eur. J. Oper. Res. 171, 962–976 (2006)CrossRefzbMATHGoogle Scholar
  4. 4.
    Angelis, V.D.: Planning home assistance for AIDS patients in the city of Rome, Italy. Interfaces 28, 75–83 (1998)CrossRefGoogle Scholar
  5. 5.
    Leigh, J., Jackson, L., Dunnett, S.: Police officer dynamic positioning for incident response and community presence. In: Proceedings of the 5th International Conference on Operations Research and Enterprise Systems (ICORES 2016), pp. 261–270 (2016)Google Scholar
  6. 6.
    Misir, M., Smet, P.V.B.G.: An analysis of generalised heuristics for vehicle routing and personnel rostering problems. J. Oper. Res. Soc. 66, 858–870 (2015)CrossRefGoogle Scholar
  7. 7.
    Jean-François, C., Gilbert, L., Federico, P., Stefan, R.: Scheduling technicians and tasks in a telecommunications company. J. Sched. 13, 393–409 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Laugier, A., Anne-marie, B., Telecom, R. F.: Technicians and interventions scheduling for telecommunications. Francetelecom, 1–7 (2006)Google Scholar
  9. 9.
    Lesaint, D., Voudouris, C., Azarmi, N.: Dynamic workforce scheduling for British telecommunications plc. Interfaces 30, 45–56 (2000)CrossRefGoogle Scholar
  10. 10.
    Castillo-Salazar, J., Landa-Silva, D., Qu, R.: Workforce scheduling and routing problems: literature survey and computational study. Ann. Oper. Res. (2014)Google Scholar
  11. 11.
    Rasmussen, M.S., Justesen, T., Dohn, A., Larsen, J.: The home care crew scheduling problem: preference-based visit clustering and temporal dependencies. Eur. J. Oper. Res. 219, 598–610 (2012)CrossRefzbMATHGoogle Scholar
  12. 12.
    Castillo-Salazar, J.A., Landa-Silva, D., Qu, R.: A greedy heuristic for workforce scheduling and routing with time-dependent activities constraints. In: Proceedings of the 4th International Conference on Operations Research and Enterprise Systems (ICORES 2015) (2015)Google Scholar
  13. 13.
    Mankowska, D., Meisel, F., Bierwirth, C.: The home health care routing and scheduling problem with interdependent services. Health Care Manage. Sci. 17, 15–30 (2014)CrossRefGoogle Scholar
  14. 14.
    Xu, J., Chiu, S.: Effective heuristic procedures for a field technician scheduling problem. J. Heuristics 7, 495–509 (2001)CrossRefzbMATHGoogle Scholar
  15. 15.
    Dantzig, G.B., Wolfe, P.: Decomposition principle for linear programs. Oper. Res. 8, 101–111 (1960)CrossRefzbMATHGoogle Scholar
  16. 16.
    Laesanklang, W., Landa-Silva, D., Castillo-Salazar, J.A.: Mixed integer programming with decomposition to solve a workforce scheduling and routing problem. In: Proceedings of the 4th International Conference on Operations Research and Enterprise Systems (ICORES 2015), pp. 283–293 (2015)Google Scholar
  17. 17.
    Laesanklang, W., Pinheiro, R.L., Algethami, H., Landa-Silva, D.: Extended decomposition for mixed integer programming to solve a workforce scheduling and routing problem. In: Werra, D., Parlier, G.H., Vitoriano, B. (eds.) ICORES 2015. CCIS, vol. 577, pp. 191–211. Springer, Cham (2015). doi: 10.1007/978-3-319-27680-9_12 CrossRefGoogle Scholar
  18. 18.
    Reimann, M., Doerner, K., Hartl, R.F.: D-Ants: savings based ants divide and conquer the vehicle routing problem. Comput. Oper. Res. 31, 563–591 (2004)CrossRefzbMATHGoogle Scholar
  19. 19.
    Laesanklang, W., Landa-Silva, D., Castillo-Salazar, J.A.: Mixed integer programming with decomposition for workforce scheduling and routing with time-dependent activities constraints. In: Proceedings of 5th the International Conference on Operations Research and Enterprise Systems, pp. 330–339 (2016)Google Scholar
  20. 20.
    Misir, M., Smet, P., Verbeeck, K., Vanden Berghe, G.: Security personnel routing and rostering: a hyper-heuristic approach. In: Proceedings of the 3rd International Conference on Applied Operational Research, ICAOR 2011, pp. 193–205 (2011)Google Scholar
  21. 21.
    Solomon, M.M.: Algorithms for the vehicle routing and scheduling problem with time window constraints. Oper. Res. 35 (1987)Google Scholar
  22. 22.
    Castro-Gutierrez, J., Landa-Silva, D., Moreno, P.J.: Nature of real-world multi-objective vehicle routing with evolutionary algorithms. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 257–264 (2011)Google Scholar
  23. 23.
    Field, A.: Discovering Statistics Using IBM SPSS Statistics, 4th edn. SAGE Publication Ltd., London (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wasakorn Laesanklang
    • 1
    Email author
  • Dario Landa-Silva
    • 1
  • J. Arturo Castillo-Salazar
    • 1
  1. 1.School of Computer Science, ASAP Research GroupThe University of NottinghamNottinghamUK

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