Skip to main content
SpringerLink
Log in

A comparison of three heuristics on a practical case of sub-daily staff scheduling

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Sub-daily personnel planning, which is the focus of our work offers considerable productivity reserves for companies in certain industries, such as logistics, retail and call centres. However, it also creates complex challenges for the planning software. We compare particle swarm optimisation (PSO), the evolution strategy (ES) and a constructive agent-based heuristic on a set of staff scheduling problems derived from a practical case in logistics. All heuristics significantly outperform conventional manual full-day planning, demonstrating the value of sub-daily scheduling heuristics. PSO delivers the best overall results in terms of solution quality and is the method of choice, when CPU-time is not limited. The approach based on artificial agents is competitive with ES and delivers solutions of almost the same quality as PSO, but is vastly quicker. This suggests that agents could be an interesting method for real-time scheduling or re-scheduling tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Abbink, E. J. W., Mobach, D. G. A., Fioole, P. J., Kroon, L. G., v. d. Heijden, E. H. T., & Wijngaards, N. J. E. (2009). Actor-agent application for train driver rescheduling. In Proceedings of the 8th int. conf. on autonomous agents and multiagent systems (Vol. 1, pp. 513–520).

    Google Scholar 

  • ATOSS Software AG, & FH Heidelberg (Eds.) (2006). Standort Deutschland 2006. Zukunftssicherung durch intelligentes Personalmanagement. München.

  • Azarmi, N., & Smith, R. (2007). Intelligent scheduling and planning systems for telecommunications resource management. BT Technology Journal, 25(3–4), 241–248.

    Article  Google Scholar 

  • Bäck, T. (1996). Evolutionary algorithms in theory and practice. New York: Oxford University Press.

    Google Scholar 

  • Bäck, T., Fogel, D. B., & Michalewicz, Z. (Eds.) (1997). Handbook of evolutionary computation. Bristol: Institute of Physics Publishing.

    Google Scholar 

  • Beyer, H. G., & Schwefel, H. P. (2002). Evolution strategies: a comprehensive introduction. Natural Computing, 1, 3–52.

    Article  Google Scholar 

  • Blöchlinger, I. (2004). Modeling staff scheduling problems. A tutorial. European Journal of Operational Research, 158, 533–542.

    Article  Google Scholar 

  • Brodersen, O. B. (2008). Eignung schwarmintelligenter Verfahren für die betriebliche Entscheidungsunterstützung. Göttingen: Cuvillier.

    Google Scholar 

  • Burke, E. K., De Causmaecker, P., Vanden Berghe, G., & Van Landeghem, H. (2004). The state of the art of nurse rostering. Journal of Scheduling, 7, 441–499.

    Article  Google Scholar 

  • Chu, S. C., Chen, Y. T., & Ho, J. H. (2006). Timetable scheduling using particle swarm optimization. In Proceedings of the international conference on innovative computing, information and control, ICICIC, Beijing, 2006 (Vol. 3, pp. 324–327).

    Google Scholar 

  • De Causmaecker, P., & Vanden Berghe, G. (2011). Categorisation of nurse rostering problems. Journal of Scheduling, 14, 3–16.

    Article  Google Scholar 

  • De Causmaecker, P., Ouelhadj, D., & Vanden Berghe, G. (2003). Agents in timetabling problems. In Proceedings of the multidisciplinary international scheduling conference, MISTA, 2003 (pp. 67–71).

    Google Scholar 

  • Ernst, A. T., Jiang, H., Krishnamoorthy, M., Owens, B., & Sier, D. (2002). An annotated bibliography of personnel scheduling and rostering. Annals of Operations Research, 127, 21–144.

    Article  Google Scholar 

  • Fukuyama, Y. (2003). Fundamentals of particle swarm optimization techniques. In K. Y. Lee & M. A. El-Sharkawi (Eds.), Modern heuristic optimization techniques with applications to power systems (pp. 24–51). New York: Wiley/IEEE Press.

    Google Scholar 

  • Garey, M. R., & Johnson, D. S. (1979). Computers and intractability. A guide to the theory of NP-completeness. San Francisco: Freeman.

    Google Scholar 

  • Günther, M. (2011). Hochflexibles Workforce Management: Herausforderungen und Lösungsverfahren. Dissertation, TU Ilmenau (in German).

  • Günther, M., & Nissen, V. (2009). A comparison of neighbourhood topologies for staff scheduling with particle swarm optimisation. In B. Mertsching et al. (Eds.), LNAI: Vol. 5803. Proceedings of KI 2009 (pp. 185–192). Berlin: Springer.

    Google Scholar 

  • Herdy, M. (1990). Application of the ‘Evolutionsstrategie’ to discrete optimization problems. In H. P. Schwefel & R. Männer (Eds.), Parallel problem solving from nature (pp. 188–192). Berlin: Springer.

    Google Scholar 

  • Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the IEEE int. conf. on neural networks (pp. 1942–1948).

    Google Scholar 

  • Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm intelligence. San Francisco: Morgan Kaufmann.

    Google Scholar 

  • Kragelund, L., & Kabel, T. (1998). Employee timetabling. An empirical study. Master’s thesis, Department of Computer Science, University of Aarhus, Denmark.

  • Krempels, K. H. (2002). Lösen von Scheduling-Konflikten durch Verhandlungen zwischen Agenten. In J. Sauer (Ed.), Proceedings of PuK 2002 (pp. 86–89).

    Google Scholar 

  • Li, R., Emmerich, M. T. M., Bovenkamp, E. G. P., Eggermont, J., Bäck, T., Dijkstra, J., & Reiber, J. H. C. (2006). Mixed integer evolution strategies and their application to intravascular ultrasound image analysis. In F. Rothlauf (Ed.), LNCS: Vol. 3907. Applications of evolutionary computation (pp. 415–426). Berlin: Springer.

    Google Scholar 

  • Meisels, A., & Schaerf, A. (2003). Modelling and solving employee timetabling. Annals of Mathematics and Artificial Intelligence, 39, 41–59.

    Article  Google Scholar 

  • Nissen, V. (1994). Solving the quadratic assignment problem with clues from nature. IEEE Transactions on Neural Networks, 5(1), 66–72.

    Article  Google Scholar 

  • Nissen, V., & Gold, S. (2008) Survivable network design with an evolution strategy. In A. Yang, Y. Shan, & L. T. Bui (Eds.), Studies in computational intelligence. Success in evolutionary computation (pp. 263–283). Berlin: Springer.

    Chapter  Google Scholar 

  • Nissen, V., & Günther, M. (2009). Staff scheduling with particle swarm optimization and evolution strategies. In C. Cotta & P. Cowling (Eds.), LNCS: Vol. 5482. Proceedings of EvoCOP 2009 (pp. 228–239). Berlin: Springer.

    Google Scholar 

  • Nissen, V., & Günther, M. (2010). Automatic generation of optimised working time models in personnel planning. In M. Dorigo et al. (Eds.), LNCS: Vol. 6234. Proceedings of ANTS 2010—7th int. conf. on swarm intelligence (pp. 384–391). Berlin: Springer.

    Chapter  Google Scholar 

  • Nissen, V., Günther, M., & Schumann, R. (2011). Integrated generation of working time models and staff schedules in workforce management. In C. Di Chio (Ed.), LNCS: Vol. 6625. Proceedings EvoApplications 2011 (pp. 491–500). Berlin: Springer.

    Google Scholar 

  • Parsopoulos, K. E., & Vrahatis, M. N. (2002). Recent approaches to global optimization problems through particle swarm optimization. Natural Computing, 1, 235–306.

    Article  Google Scholar 

  • Poli, R. (2007). An analysis of publications on particle swarm optimization (Report CSM-469). England: Dep. of Computer Science, University of Essex.

  • Proudfoot Consulting (2008). Global Productivity Report. Atlanta.

  • Puppe, F., Klügl, F., Herrler, R., Kirn, S., & Heine, C. (2000). Konzeption einer flexiblen Agentenkomponente für Schedulingaufgaben im Krankenhausumfeld. In Proceedings of 2. Koll. “Intelligente Softwareagenten und betriebswirtschaftliche Anwendungsszenarien”.

    Google Scholar 

  • Rudolph, G. (1994). An evolutionary algorithm for integer programming. In Y. Davidor, H. P. Schwefel, & R. Männer (Eds.), LNCS: Vol. 866. Proceedings of parallel problem solving from nature, PPSN (Vol. III, pp. 139–148). Berlin: Springer.

    Chapter  Google Scholar 

  • Sauer, J., & Schumann, R. (2007). Modelling and solving workforce scheduling problems. In J. Sauer, S. Edelkamp, & B. Schattenberg (Eds.), Proceedings of 21st workshop Planen und Konfigurieren, PuK, Osnabrück, 2007 (pp. 93–101).

    Google Scholar 

  • Scherf, B. (2005). Wirtschaftliche Nutzenaspekte der Personaleinsatzplanung. In M. Fank & B. Scherf (Eds.), Handbuch Personaleinsatzplanung (pp. 55–83). Frechen: Datakontext.

    Google Scholar 

  • Schindler, B., Rothlauf, F., & Pesch, E. (2002). Evolution strategies, network random keys, and the one-max tree problem. In LNCS: Vol. 2279. Applications of evolutionary computing: proceedings of EvoWorkshops 2002 (pp. 29–40). Berlin: Springer.

    Chapter  Google Scholar 

  • Stolletz, R. (2010). Operational workforce planning for check-in counters at airports. Transportation Research. Part E, Logistics and Transportation Review, 46(3), 414–425.

    Article  Google Scholar 

  • Sub-Daily Staff Scheduling Data Sets and Benchmarks. http://www.tu-ilmenau.de/en/department-of-commercial-information-technology-for-services/research/test-data-sub-daily-staff-scheduling/.

  • Tasgetiren, M. F., Sevkli, M., Liang, Y. C., & Gencyilmaz, G. (2004). Particle swarm optimization algorithm for single machine total weighted tardiness problem. In Proceedings of the congress on evolutionary computation, CEC, 2004 (pp. 1412–1419). New York: IEEE Press.

    Google Scholar 

  • Tien, J., & Kamiyama, A. (1982). On manpower scheduling algorithms. SIAM Review, 24(3), 275–287.

    Article  Google Scholar 

  • Vanden Berghe, G. (2002). An advanced model and novel meta-heuristic solution methods to personnel scheduling in healthcare. Thesis, University of Gent.

  • Veeramachaneni, K. (2003). Optimization using particle swarm with near neighbor interactions. In LNCS: Vol. 2723. Proceedings of the genetic and evolutionary computation conference, GECCO, 2003 (pp. 110–121). Berlin: Springer.

    Google Scholar 

  • Veeramachaneni, K., Osadciw, L., & Kamath, G. (2007). Probabilistically driven particle swarms for optimization of multi-valued discrete problems: design and analysis. In Proceedings of the IEEE swarm intelligence symposium, SIS, Honolulu, 2007 (pp. 141–149).

    Google Scholar 

  • Wauters, T., Verbeeck, K., Vanden Berghe, G., & de Causmaecker, P. (2009). A multi-agent learning approach for the multi-mode resource-constrained project scheduling problem. Paper presented at the 2nd int. workshop on optimisation in multi-agent systems, OptMas, AAMAS, Budapest.

Download references

Author information

Authors and Affiliations

  1. Faculty of Economic Sciences, Chair of Information Systems in Services (WI2), Ilmenau University of Technology, Postfach 100565, 98684, Ilmenau, Germany

    Maik Günther & Volker Nissen

Authors
  1. Maik Günther
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Volker Nissen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Volker Nissen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Günther, M., Nissen, V. A comparison of three heuristics on a practical case of sub-daily staff scheduling. Ann Oper Res 218, 201–219 (2014). https://doi.org/10.1007/s10479-012-1259-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-012-1259-2

Keywords

Search

Navigation