Baker, K.R.: Workforce allocation in cyclical scheduling problems: a survey. J. Oper. Res. Soc. 27(1), 155–167 (1976)
Article
Google Scholar
Balakrishnan, N., Wong, R.T.: A network model for the rotating workforce scheduling problem. Networks 20(1), 25–42 (1990)
MathSciNet
Article
Google Scholar
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Article
Google Scholar
Chuin Lau, H.: On the complexity of manpower shift scheduling. Comput. Oper. Res. 23(1), 93–102 (1996)
Article
Google Scholar
Erkinger, C., Musliu, N.: Personnel scheduling as satisfiability modulo theories. In: International Joint Conference on Artificial Intelligence – IJCAI 2017, Melbourne, Australia, August 19-25, 2017. https://doi.org/10.24963/ijcai.2017/86, pp 614–621 (2017)
Falcón, R., Barrena, E., Canca, D., Laporte, G.: Counting and enumerating feasible rotating schedules by means of gröbner bases. Math. Comput. Simul. 125, 139–151 (2016)
Article
Google Scholar
Kang, Y., Hyndman, R., Smith-Miles, K.: Visualising forecasting algorithm performance using time series instance spaces. Int. J. Forecast 33(2), 345–358 (2017). https://doi.org/10.1016/j.ijforecast.2016.09.004
Article
Google Scholar
Kletzander, L., Musliu, N., Gärtner, J., Krennwallner, T., Schafhauser, W.: Exact methods for extended rotating workforce scheduling problems. In: Proceedings of the Twenty-Ninth International Conference on Automated Planning and Scheduling, vol. 29, pp. 519–527. American Association for Artificial Intelligence (AAAI) (2019)
Laporte, G.: The art and science of designing rotating schedules. J. Oper. Res. Soc. 50, 1011–1017 (1999)
Article
Google Scholar
Laporte, G., Nobert, Y., Biron, J.: Rotating schedules. Eur. J. Oper. Res. 4(1), 24–30 (1980)
Article
Google Scholar
Laporte, G., Pesant, G.: A general multi-shift scheduling system. J. Oper. Res. Soc. 55(11), 1208–1217 (2004)
Article
Google Scholar
Muñoz, M., Smith-Miles, K.: Performance analysis of continuous black-box optimization algorithms via footprints in instance space. Evol. Comput. 25(4), 529–554 (2017). https://doi.org/10.1162/EVCO_a_00194
Article
Google Scholar
Muñoz, M.A., Villanova, L., Baatar, D., Smith-Miles, K.: Instance spaces for machine learning classification. Mach. Learn. 107(1), 109–147 (2018)
MathSciNet
Article
Google Scholar
Musliu, N.: Combination of local search strategies for rotating workforce scheduling problem. In: International Joint Conference on Artificial Intelligence – IJCAI 2005, Edinburgh, Scotland, UK, July 30 - August 5, 2005, pp. 1529–1530. http://ijcai.org/Proceedings/05/Papers/post-0448.pdf (2005)
Musliu, N.: Heuristic methods for automatic rotating workforce scheduling. Int. J. Comput. Intell. Res. 2(4), 309–326 (2006)
Article
Google Scholar
Musliu, N., Gärtner, J., Slany, W.: Efficient generation of rotating workforce schedules. Discret. Appl. Math. 118(1-2), 85–98 (2002)
MathSciNet
Article
Google Scholar
Musliu, N., Schutt, A., Stuckey, P.J.: Solver independent rotating workforce scheduling. In: International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, pp 429–445. Springer (2018)
Oliveira, C., Aleti, A., Grunske, L., Smith-Miles, K.: Mapping the effectiveness of automated test suite generation techniques. IEEE Trans. Reliab. 67(3), 771–785 (2018)
Article
Google Scholar
Restrepo, M.I., Gendron, B., Rousseau, L.M.: Branch-and-price for personalized multiactivity tour scheduling. INFORMS J. Comput. 28(2), 334–350 (2016)
MathSciNet
Article
Google Scholar
Rice, J.: The algorithm selection problem. In: Advances in Computers. https://doi.org/10.1016/S0065-2458(08)60520-3, vol. 15, pp 65–118. Elsevier (1976)
Smith-Miles, K., Baatar, D., Wreford, B., Lewis, R.: Towards objective measures of algorithm performance across instance space. Comput. Oper. Res. 45, 12–24 (2014). https://doi.org/10.1016/j.cor.2013.11.015
MathSciNet
Article
MATH
Google Scholar
Smith-Miles, K., Bowly, S.: Generating new test instances by evolving in instance space. Comput. Oper. Res. 63, 102–113 (2015). 10.1016/j.cor.2015.04.022
MathSciNet
Article
MATH
Google Scholar
Smith-Miles, K., Lopes, L.: Measuring instance difficulty for combinatorial optimization problems. Comput. Oper. Res. 39(5), 875–889 (2012)
MathSciNet
Article
Google Scholar
Smith-Miles, K.A.: Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Computing Surveys (CSUR) 41(1), 6 (2009)
Article
Google Scholar
Triska, M., Musliu, N.: A constraint programming application for rotating workforce scheduling. In: Developing Concepts in Applied Intelligence, Studies in Computational Intelligence, vol. 363 , pp 83–88. Springer, Berlin (2011)