Abstract
Rosters are often used for real-world staff scheduling requirements. Multiple design factors such as demand variability, shift type placement, annual leave requirements, staff well-being and the placement of trainees need to be considered when constructing good rosters. In the present work we propose a metaheuristic-based strategy for designing optimal cyclical rosters that can accommodate uneven demand patterns. A key part of our approach relies on integrating an efficient optimal trainee placement module within the metaheuristic-driven search. Results obtained on a real-life problem proposed by the Port of Aberdeen indicate that by incorporating a demand-informed random rota initialisation procedure, our strategy can generally achieve high-quality end-of-run solutions when using relatively simple base solvers like simulated annealing (SA) and evolution strategies (ES). While ES converge faster, SA outperforms quality-wise, with both approaches being able to improve the man-made baseline.
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Notes
- 1.
As we are operating on cyclical rotas, \(x_0=x_N\) and \(x_{N+1}=x_1\).
- 2.
Apart from the best penalties for ES 200gen, \(\lambda =100\).
References
Fyvie, M., McCall, J.A.W., Christie, L.A., Brown-lee, A.E.: Explaining a staff rostering genetic algorithm using sensitivity analysis and trajectory analysis. In: Genetic and Evolutionary Computation Conference Companion (GECCO 2023 Companion), 15–19 July 2023, Lisbon, Portugal (2023)
Burke, E.K., Petrovic, S.: Recent research directions in automated timetabling. Eur. J. Oper. Res. 140(2), 266–280 (2002)
Ernst, A.T., Jiang, H., Krishnamoorthy, M., Sier, D.: Staff scheduling and rostering: a review of applications, methods and models. Eur. J. Oper. Res. 153(1), 3–27 (2004)
Petrovic, S., Burke, E.K.: University timetabling. (2004)
Qu, R., Burke, E.K., McCollum, B., Merlot, L.T., Lee, S.Y.: A survey of search methodologies and automated system development for examination timetabling. J. Sched. 12, 55–89 (2009)
Kwan, R.S., Wren, A., Kwan, A.S.: Hybrid genetic algorithms for scheduling bus and train drivers. In: Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512), vol. 1, pp. 285–292. IEEE (2000)
Ribeiro, C.C.: Sports scheduling: problems and applications. Int. Trans. Oper. Res. 19(1–2), 201–226 (2012)
Li, Y., Kozan, E.: Rostering ambulance services. In: Industrial engineering and management society, pp. 795–801 (2009)
Taylor, P.E., Huxley, S.J.: A break from tradition for the San Francisco police: patrol officer scheduling using an optimization-based decision support system. Interfaces 19(1), 4–24 (1989)
Arabeyre, J., Fearnley, J., Steiger, F., Teather, W.: The airline crew scheduling problem: a survey. Transp. Sci. 3(2), 140–163 (1969)
Maenhout, B., Vanhoucke, M.: The impact of incorporating nurse-specific characteristics in a cyclical scheduling approach. J. Oper. Res. Soc. 60, 1683–1698 (2009)
Gans, N., Koole, G., Mandelbaum, A.: Telephone call centers: tutorial, review, and research prospects. Manuf. Serv. Oper. Manage. 5(2), 79–141 (2003)
Burke, E., De Causmaecker, P., Vanden Berghe, G.: A hybrid Tabu search algorithm for the nurse rostering problem. In: McKay, B., Yao, X., Newton, C.S., Kim, J.-H., Furuhashi, T. (eds.) SEAL 1998. LNCS (LNAI), vol. 1585, pp. 187–194. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48873-1_25
Maenhout, B., Vanhoucke, M.: A hybrid scatter search heuristic for personalized crew rostering in the airline industry. Eur. J. Oper. Res. 206(1), 155–167 (2010)
Parr, D., Thompson, J.M.: Solving the multi-objective nurse scheduling problem with a weighted cost function. Ann. Oper. Res. 155(1), 279 (2007)
Gutjahr, W.J., Rauner, M.S.: An ACO algorithm for a dynamic regional nurse-scheduling problem in Austria. Comput. Oper. Res. 34(3), 642–666 (2007)
Ainslie, R., McCall, J., Shakya, S., Owusu, G.: Tactical plan optimisation for large multi-skilled workforces using a bi-level model. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2018)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach (2003)
Kirkpatrick, S., Gelatt, C.D., Jr., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Beyer, H.G., Schwefel, H.P.: Evolution strategies-a comprehensive introduction. Nat. Comput. 1, 3–52 (2002)
Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18, 50–60 (1947)
Acknowledgment
The authors would like to acknowledge the support of staff members at the Port of Aberdeen that have kindly contributed to this research by providing the historical pilot roster pattern, demand data and feedback that informed the problem formalisation.
This work was supported by the Port of Aberdeen and InnovateUK through a Knowledge Transfer Partnership project (KTP reference number 12046).
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Collins, J., Zăvoianu, AC., McCall, J.A.W. (2023). Comparison of Simulated Annealing and Evolution Strategies for Optimising Cyclical Rosters with Uneven Demand and Flexible Trainee Placement. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XL. SGAI 2023. Lecture Notes in Computer Science(), vol 14381. Springer, Cham. https://doi.org/10.1007/978-3-031-47994-6_39
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