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Optimising Training for Service Delivery

Authors Ilankaikone Senthooran , Pierre Le Bodic , Peter J. Stuckey

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Ilankaikone Senthooran
  • Data Science & AI, Monash University, Clayton, Australia
Pierre Le Bodic
  • Data Science & AI, Monash University, Clayton, Australia
Peter J. Stuckey
  • Data Science & AI, Monash University, Clayton, Australia

Acknowledgements

We are grateful for our industry partner for this opportunity to work on a challenging real-life workforce planning problem and for the many discussions that have allowed us to conduct this work.

Cite AsGet BibTex

Ilankaikone Senthooran, Pierre Le Bodic, and Peter J. Stuckey. Optimising Training for Service Delivery. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 48:1-48:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.CP.2021.48

Abstract

We study the problem of training a roster of engineers, who are scheduled to respond to service calls that require a set of skills, and where engineers and calls have different locations. Both training an engineer in a skill and sending an engineer to respond a non-local service call incur a cost. Alternatively, a local contractor can be hired. The problem consists in training engineers in skills so that the quality of service (i.e. response time) is maximised and costs are minimised. The problem is hard to solve in practice partly because (1) the value of training an engineer in one skill depends on other training decisions, (2) evaluating training decisions means evaluating the schedules that are now made possible by the new skills, and (3) these schedules must be computed over a long time horizon, otherwise training may not pay off. We show that a monolithic approach to this problem is not practical. Instead, we decompose it into three subproblems, modelled with MiniZinc. This allows us to pick the approach that works best for each subproblem (MIP or CP) and provide good solutions to the problem. Data is provided by a multinational company.

Subject Classification

ACM Subject Classification
  • Theory of computation → Integer programming
  • Theory of computation → Constraint and logic programming
Keywords
  • Scheduling
  • Task Allocation
  • Training Optimisation

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References

  1. Mark Antunes, Vincent Armant, Kenneth N. Brown, Daniel A. Desmond, Guillaume Escamocher, Anne-Marie George, Diarmuid Grimes, Mike O'Keeffe, Yiqing Lin, Barry O'Sullivan, Cemalettin Ozturk, Luis Quesada, Mohamed Siala, Helmut Simonis, and Nic Wilson. Assigning and scheduling service visits in a mixed urban/rural setting. International Journal on Artificial Intelligence Tools, 29(03n04):2060007:1-2060007:31, 2020. URL: https://doi.org/10.1142/S0218213020600076.
  2. Gleb Belov, Peter J. Stuckey, Guido Tack, and Mark Wallace. Improved linearization of constraint programming models. In Michel Rueher, editor, Principles and Practice of Constraint Programming, Lecture Notes in Computer Science, pages 49-65. Springer, 2016. International Conference on Principles and Practice of Constraint Programming 2016, CP 2016 ; Conference date: 05-09-2016 Through 09-09-2016. URL: https://doi.org/10.1007/978-3-319-44953-1_4.
  3. John R. Birge and François Louveaux. Introduction to Stochastic programming (2nd edition). Springer, New York, NY, 2011. Google Scholar
  4. Michael J. Brusco. An exact algorithm for a workforce allocation problem with application to an analysis of cross-training policies. IIE Transactions, 40(5):495-508, 2008. URL: https://doi.org/10.1080/07408170701598124.
  5. G M Campbell. A two-stage stochastic program for scheduling and allocating cross-trained workers. Journal of the Operational Research Society, 62(6):1038-1047, 2011. URL: https://doi.org/10.1057/jors.2010.16.
  6. Gerard M. Campbell. Cross-utilization of workers whose capabilities differ. Management Science, 45(5):722-732, 1999. URL: https://doi.org/10.1287/mnsc.45.5.722.
  7. Philippe De Bruecker, Jeroen Beliën, Jorne Van den Bergh, and Erik Demeulemeester. A three-stage mixed integer programming approach for optimizing the skill mix and training schedules for aircraft maintenance. European Journal of Operational Research, 267(2):439-452, 2018. URL: https://doi.org/10.1016/j.ejor.2017.11.047.
  8. Philippe De Bruecker, Jorne Van den Bergh, Jeroen Beliën, and Erik Demeulemeester. Workforce planning incorporating skills: State of the art. European Journal of Operational Research, 243(1):1-16, 2015. URL: https://doi.org/10.1016/j.ejor.2014.10.038.
  9. Alexander Ek, Maria Garcia de la Banda, Andreas Schutt, Peter J. Stuckey, and Guido Tack. Modelling and solving online optimisation problems. Proceedings of the AAAI Conference on Artificial Intelligence, 34(02):1477-1485, April 2020. URL: https://doi.org/10.1609/aaai.v34i02.5506.
  10. A.T Ernst, H Jiang, M Krishnamoorthy, and D Sier. Staff scheduling and rostering: A review of applications, methods and models. European Journal of Operational Research, 153(1):3-27, 2004. Timetabling and Rostering. URL: https://doi.org/10.1016/S0377-2217(03)00095-X.
  11. Huei-Chuen Huang, Loo-Hay Lee, Haiqing Song, and Brian Thomas Eck. Simman—a simulation model for workforce capacity planning. Computers & Operations Research, 36(8):2490-2497, 2009. Constraint Programming. URL: https://doi.org/10.1016/j.cor.2008.10.003.
  12. Serdar Kadioglu, Mike Colena, Steven Huberman, and Claire Bagley. Optimizing the cloud service experience using constraint programming. In Gilles Pesant, editor, Principles and Practice of Constraint Programming, pages 627-637, Cham, 2015. Springer International Publishing. Google Scholar
  13. Haitao Li and Keith Womer. Scheduling projects with multi-skilled personnel by a hybrid milp/cp benders decomposition algorithm. J. Scheduling, 12:281-298, June 2009. URL: https://doi.org/10.1007/s10951-008-0079-3.
  14. Y. Naveh, Y. Richter, Y. Altshuler, D. L. Gresh, and D. P. Connors. Workforce optimization: Identification and assignment of professional workers using constraint programming. IBM Journal of Research and Development, 51(3.4):263-279, 2007. URL: https://doi.org/10.1147/rd.513.0263.
  15. N. Nethercote, P.J. Stuckey, R. Becket, S. Brand, G.J. Duck, and G. Tack. Minizinc: Towards a standard CP modelling language. In C. Bessiere, editor, Proceedings of the 13th International Conference on Principles and Practice of Constraint Programming, volume 4741 of LNCS, pages 529-543. Springer-Verlag, 2007. Google Scholar
  16. Haiqing Song and Huei-Chuen Huang. A successive convex approximation method for multistage workforce capacity planning problem with turnover. European Journal of Operational Research, 188(1):29-48, 2008. URL: https://doi.org/10.1016/j.ejor.2007.04.018.
  17. Jorne Van den Bergh, Jeroen Beliën, Philippe De Bruecker, Erik Demeulemeester, and Liesje De Boeck. Personnel scheduling: A literature review. European Journal of Operational Research, 226(3):367-385, 2013. URL: https://doi.org/10.1016/j.ejor.2012.11.029.
  18. Kum-Khiong Yang, Scott Webster, and Robert A. Ruben. An evaluation of worker cross training and flexible workdays in job shops. IIE Transactions, 39(7):735-746, 2007. URL: https://doi.org/10.1080/07408170701244687.
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