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How to cite this paper
APA: Moreno-Velásquez, L., Díaz-Serna, F & Morillo-Torres, D. (2025). Dynamic multicriteria optimization for the nurse scheduling problem. Decision Science Letters, 14(2), 457-472.
Chicago/Turabian: Moreno-Velásquez, L., Díaz-Serna, F & Morillo-Torres, D. 2025. "Dynamic multicriteria optimization for the nurse scheduling problem." Decision Science Letters 14, no. 2 (2025): 457-472.
AMA: Moreno-Velásquez, L., Díaz-Serna, F & Morillo-Torres, D. Dynamic multicriteria optimization for the nurse scheduling problem. Decision Science Letters. 2025;14(2):457-472.
References
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Akbari, M., Zandieh, M., & Dorri, B. (2013). Scheduling part-time and mixed-skilled workers to maximize employee satisfaction. The International Journal of Advanced Manufacturing Technology, 64(5–8), 1017–1027. https://doi.org/10.1007/s00170-012-4032-4
Amindoust, A., Asadpour, M., & Shirmohammadi, S. (2021). A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor. Journal of Healthcare Engineering, 2021, 1–11. https://doi.org/10.1155/2021/5563651
Bilgin, B., Demeester, P., Misir, M., Vancroonenburg, W., & Vanden Berghe, G. (2012). One hyper-heuristic approach to two timetabling problems in health care. Journal of Heuristics, 18(3), 401–434. https://doi.org/10.1007/s10732-011-9192-0
Boland, N., Kalinowski, T., & Rigterink, F. (2016). New multi-commodity flow formulations for the pooling problem. Journal of Global Optimization, 66(4), 669–710. https://doi.org/10.1007/s10898-016-0404-x
Burke, E. K., & Curtois, T. (2014). New approaches to nurse rostering benchmark instances. European Journal of Operational Research, 237(1), 71–81. https://doi.org/10.1016/j.ejor.2014.01.039
Burke, E. K., Li, J., & Qu, R. (2010). A hybrid model of integer programming and variable neighbourhood search for highly-constrained nurse rostering problems. European Journal of Operational Research, 203(2), 484–493. https://doi.org/10.1016/j.ejor.2009.07.036
Ceschia, S., Di Gaspero, L., Mazzaracchio, V., Policante, G., & Schaerf, A. (2023). Solving a real-world nurse rostering problem by Simulated Annealing. Operations Research for Health Care, 36, 100379. https://doi.org/10.1016/j.orhc.2023.100379
Chen, Z., De Causmaecker, P., & Dou, Y. (2023). A combined mixed integer programming and deep neural network-assisted heuristics algorithm for the nurse rostering problem. Applied Soft Computing, 136, 109919. https://doi.org/10.1016/j.asoc.2022.109919
Constantino, A. A., Landa-Silva, D., de Melo, E. L., de Mendonça, C. F. X., Rizzato, D. B., & Romão, W. (2013). A heuristic algorithm based on multi-assignment procedures for nurse scheduling. Annals of Operations Research. https://doi.org/10.1007/s10479-013-1357-9
Di Martinelly, C., & Meskens, N. (2017). A bi-objective integrated approach to building surgical teams and nurse schedule rosters to maximise surgical team affinities and minimise nurses’ idle time. International Journal of Production Economics, 191, 323–334. https://doi.org/10.1016/j.ijpe.2017.05.014
El Adoly, A. A., Gheith, M., & Nashat Fors, M. (2018). A new formulation and solution for the nurse scheduling problem: A case study in Egypt. Alexandria Engineering Journal, 57(4), 2289–2298. https://doi.org/10.1016/j.aej.2017.09.007
EL-Rifai, O., Garaix, T., Augusto, V., & Xie, X. (2015). A stochastic optimization model for shift scheduling in emergency departments. Health Care Management Science, 18(3), 289–302. https://doi.org/10.1007/s10729-014-9300-4
Hamid, M., Tavakkoli-Moghaddam, R., Golpaygani, F., & Vahedi-Nouri, B. (2020). A multi-objective model for a nurse scheduling problem by emphasizing human factors. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 234(2), 179–199. https://doi.org/10.1177/0954411919889560
Jafari, H., Bateni, S., Daneshvar, P., Bateni, S., & Mahdioun, H. (2016). Fuzzy Mathematical Modeling Approach for the Nurse Scheduling Problem: A Case Study. International Journal of Fuzzy Systems, 18(2), 320–332. https://doi.org/10.1007/s40815-015-0051-2
Jafari, H., & Salmasi, N. (2015). Maximizing the nurses’ preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm. Journal of Industrial Engineering International, 11(3), 439–458. https://doi.org/10.1007/s40092-015-0111-0
Jiang, Y., Zhang, X., Rong, Y., & Zhang, Z. (2014). A Multimodal Location and Routing Model for Hazardous Materials Transportation based on Multi-commodity Flow Model. Procedia - Social and Behavioral Sciences, 138, 791–799. https://doi.org/10.1016/j.sbspro.2014.07.262
Legrain, A., Bouarab, H., & Lahrichi, N. (2015). The Nurse Scheduling Problem in Real-Life. Journal of Medical Systems, 39(1), 160. https://doi.org/10.1007/s10916-014-0160-8
Letchford, A. N., & Salazar-González, J.-J. (2016). Stronger multi-commodity flow formulations of the (capacitated) sequential ordering problem. European Journal of Operational Research, 251(1), 74–84. https://doi.org/10.1016/j.ejor.2015.11.001
Liang, B., & Turkcan, A. (2016). Acuity-based nurse assignment and patient scheduling in oncology clinics. Health Care Management Science, 19(3), 207–226. https://doi.org/10.1007/s10729-014-9313-z
Lin, C.-C., Kang, J.-R., Liu, W.-Y., & Deng, D.-J. (2014). Modelling a Nurse Shift Schedule with Multiple Preference Ranks for Shifts and Days-Off. Mathematical Problems in Engineering, 2014, 1–10. https://doi.org/10.1155/2014/937842
Maharjan, B., & Matis, T. I. (2012). Multi-commodity flow network model of the flight gate assignment problem. Computers & Industrial Engineering, 63(4), 1135–1144. https://doi.org/10.1016/j.cie.2012.06.020
Mesquita, M., Moz, M., Paias, A., & Pato, M. (2015). A decompose-and-fix heuristic based on multi-commodity flow models for driver rostering with days-off pattern. European Journal of Operational Research, 245(2), 423–437. https://doi.org/10.1016/j.ejor.2015.03.030
M’Hallah, R., & Alkhabbaz, A. (2013). Scheduling of nurses: A case study of a Kuwaiti health care unit. Operations Research for Health Care, 2(1–2), 1–19. https://doi.org/10.1016/j.orhc.2013.03.003
Ohki, M., Uneme, S., & Kawano, H. (2010). Effective Mutation Operator and Parallel Processing for Nurse Scheduling. In Intelligent Systems: From Theory to Practice (pp. 229–242). https://doi.org/10.1007/978-3-642-13428-9_10
Rahimian, E., Akartunalı, K., & Levine, J. (2017). A hybrid Integer Programming and Variable Neighbourhood Search algorithm to solve Nurse Rostering Problems. European Journal of Operational Research, 258(2), 411–423. https://doi.org/10.1016/j.ejor.2016.09.030
Rudi, A., Fröhling, M., Zimmer, K., & Schultmann, F. (2016). Freight transportation planning considering carbon emissions and in-transit holding costs: a capacitated multi-commodity network flow model. EURO Journal on Transportation and Logistics, 5(2), 123–160. https://doi.org/10.1007/s13676-014-0062-4
Stimpfel, A. W., Sloane, D. M., & Aiken, L. H. (2012). The Longer The Shifts For Hospital Nurses, The Higher The Levels Of Burnout And Patient Dissatisfaction. Health Affairs, 31(11), 2501–2509. https://doi.org/10.1377/hlthaff.2011.1377
Svirsko, A. C., Norman, B. A., Rausch, D., & Woodring, J. (2019). Using Mathematical Modeling to Improve the Emergency Department Nurse-Scheduling Process. Journal of Emergency Nursing, 45(4), 425–432. https://doi.org/10.1016/j.jen.2019.01.013
Tassopoulos, I. X., Solos, I. P., & Beligiannis, G. N. (2015). Α two-phase adaptive variable neighborhood approach for nurse rostering. Computers & Operations Research, 60, 150–169. https://doi.org/10.1016/j.cor.2015.02.009
Yahia, Z., Eltawil, A. B., & Harraz, N. A. (2016). The operating room case-mix problem under uncertainty and nurses capacity constraints. Health Care Management Science, 19(4), 383–394. https://doi.org/10.1007/s10729-015-9337-z
Yilmaz, E. (2012). A Mathematical Programming Model for Scheduling of Nurses’ Labor Shifts. Journal of Medical Systems, 36(2), 491–496. https://doi.org/10.1007/s10916-010-9494-z
Zhang, X., Yang, Y., Zhu, Q., Lin, Q., Chen, W., Li, J., & Coello, C. A. C. (2024). Multi-agent deep Q-network-based metaheuristic algorithm for Nurse Rostering Problem. Swarm and Evolutionary Computation, 87, 101547. https://doi.org/10.1016/j.swevo.2024.101547
Zhang, Z., Hao, Z., & Huang, H. (2011). Hybrid Swarm-Based Optimization Algorithm of GA & VNS for Nurse Scheduling Problem. In Information Computing and Applications (pp. 375–382). https://doi.org/10.1007/978-3-642-25255-6_48
Zolfagharinia, H., Najafi, M., Rizvi, S., & Haghighi, A. (2024). Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques. Algorithms, 17(6), 234. https://doi.org/10.3390/a17060234
Akbari, M., Zandieh, M., & Dorri, B. (2013). Scheduling part-time and mixed-skilled workers to maximize employee satisfaction. The International Journal of Advanced Manufacturing Technology, 64(5–8), 1017–1027. https://doi.org/10.1007/s00170-012-4032-4
Amindoust, A., Asadpour, M., & Shirmohammadi, S. (2021). A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor. Journal of Healthcare Engineering, 2021, 1–11. https://doi.org/10.1155/2021/5563651
Bilgin, B., Demeester, P., Misir, M., Vancroonenburg, W., & Vanden Berghe, G. (2012). One hyper-heuristic approach to two timetabling problems in health care. Journal of Heuristics, 18(3), 401–434. https://doi.org/10.1007/s10732-011-9192-0
Boland, N., Kalinowski, T., & Rigterink, F. (2016). New multi-commodity flow formulations for the pooling problem. Journal of Global Optimization, 66(4), 669–710. https://doi.org/10.1007/s10898-016-0404-x
Burke, E. K., & Curtois, T. (2014). New approaches to nurse rostering benchmark instances. European Journal of Operational Research, 237(1), 71–81. https://doi.org/10.1016/j.ejor.2014.01.039
Burke, E. K., Li, J., & Qu, R. (2010). A hybrid model of integer programming and variable neighbourhood search for highly-constrained nurse rostering problems. European Journal of Operational Research, 203(2), 484–493. https://doi.org/10.1016/j.ejor.2009.07.036
Ceschia, S., Di Gaspero, L., Mazzaracchio, V., Policante, G., & Schaerf, A. (2023). Solving a real-world nurse rostering problem by Simulated Annealing. Operations Research for Health Care, 36, 100379. https://doi.org/10.1016/j.orhc.2023.100379
Chen, Z., De Causmaecker, P., & Dou, Y. (2023). A combined mixed integer programming and deep neural network-assisted heuristics algorithm for the nurse rostering problem. Applied Soft Computing, 136, 109919. https://doi.org/10.1016/j.asoc.2022.109919
Constantino, A. A., Landa-Silva, D., de Melo, E. L., de Mendonça, C. F. X., Rizzato, D. B., & Romão, W. (2013). A heuristic algorithm based on multi-assignment procedures for nurse scheduling. Annals of Operations Research. https://doi.org/10.1007/s10479-013-1357-9
Di Martinelly, C., & Meskens, N. (2017). A bi-objective integrated approach to building surgical teams and nurse schedule rosters to maximise surgical team affinities and minimise nurses’ idle time. International Journal of Production Economics, 191, 323–334. https://doi.org/10.1016/j.ijpe.2017.05.014
El Adoly, A. A., Gheith, M., & Nashat Fors, M. (2018). A new formulation and solution for the nurse scheduling problem: A case study in Egypt. Alexandria Engineering Journal, 57(4), 2289–2298. https://doi.org/10.1016/j.aej.2017.09.007
EL-Rifai, O., Garaix, T., Augusto, V., & Xie, X. (2015). A stochastic optimization model for shift scheduling in emergency departments. Health Care Management Science, 18(3), 289–302. https://doi.org/10.1007/s10729-014-9300-4
Hamid, M., Tavakkoli-Moghaddam, R., Golpaygani, F., & Vahedi-Nouri, B. (2020). A multi-objective model for a nurse scheduling problem by emphasizing human factors. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 234(2), 179–199. https://doi.org/10.1177/0954411919889560
Jafari, H., Bateni, S., Daneshvar, P., Bateni, S., & Mahdioun, H. (2016). Fuzzy Mathematical Modeling Approach for the Nurse Scheduling Problem: A Case Study. International Journal of Fuzzy Systems, 18(2), 320–332. https://doi.org/10.1007/s40815-015-0051-2
Jafari, H., & Salmasi, N. (2015). Maximizing the nurses’ preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm. Journal of Industrial Engineering International, 11(3), 439–458. https://doi.org/10.1007/s40092-015-0111-0
Jiang, Y., Zhang, X., Rong, Y., & Zhang, Z. (2014). A Multimodal Location and Routing Model for Hazardous Materials Transportation based on Multi-commodity Flow Model. Procedia - Social and Behavioral Sciences, 138, 791–799. https://doi.org/10.1016/j.sbspro.2014.07.262
Legrain, A., Bouarab, H., & Lahrichi, N. (2015). The Nurse Scheduling Problem in Real-Life. Journal of Medical Systems, 39(1), 160. https://doi.org/10.1007/s10916-014-0160-8
Letchford, A. N., & Salazar-González, J.-J. (2016). Stronger multi-commodity flow formulations of the (capacitated) sequential ordering problem. European Journal of Operational Research, 251(1), 74–84. https://doi.org/10.1016/j.ejor.2015.11.001
Liang, B., & Turkcan, A. (2016). Acuity-based nurse assignment and patient scheduling in oncology clinics. Health Care Management Science, 19(3), 207–226. https://doi.org/10.1007/s10729-014-9313-z
Lin, C.-C., Kang, J.-R., Liu, W.-Y., & Deng, D.-J. (2014). Modelling a Nurse Shift Schedule with Multiple Preference Ranks for Shifts and Days-Off. Mathematical Problems in Engineering, 2014, 1–10. https://doi.org/10.1155/2014/937842
Maharjan, B., & Matis, T. I. (2012). Multi-commodity flow network model of the flight gate assignment problem. Computers & Industrial Engineering, 63(4), 1135–1144. https://doi.org/10.1016/j.cie.2012.06.020
Mesquita, M., Moz, M., Paias, A., & Pato, M. (2015). A decompose-and-fix heuristic based on multi-commodity flow models for driver rostering with days-off pattern. European Journal of Operational Research, 245(2), 423–437. https://doi.org/10.1016/j.ejor.2015.03.030
M’Hallah, R., & Alkhabbaz, A. (2013). Scheduling of nurses: A case study of a Kuwaiti health care unit. Operations Research for Health Care, 2(1–2), 1–19. https://doi.org/10.1016/j.orhc.2013.03.003
Ohki, M., Uneme, S., & Kawano, H. (2010). Effective Mutation Operator and Parallel Processing for Nurse Scheduling. In Intelligent Systems: From Theory to Practice (pp. 229–242). https://doi.org/10.1007/978-3-642-13428-9_10
Rahimian, E., Akartunalı, K., & Levine, J. (2017). A hybrid Integer Programming and Variable Neighbourhood Search algorithm to solve Nurse Rostering Problems. European Journal of Operational Research, 258(2), 411–423. https://doi.org/10.1016/j.ejor.2016.09.030
Rudi, A., Fröhling, M., Zimmer, K., & Schultmann, F. (2016). Freight transportation planning considering carbon emissions and in-transit holding costs: a capacitated multi-commodity network flow model. EURO Journal on Transportation and Logistics, 5(2), 123–160. https://doi.org/10.1007/s13676-014-0062-4
Stimpfel, A. W., Sloane, D. M., & Aiken, L. H. (2012). The Longer The Shifts For Hospital Nurses, The Higher The Levels Of Burnout And Patient Dissatisfaction. Health Affairs, 31(11), 2501–2509. https://doi.org/10.1377/hlthaff.2011.1377
Svirsko, A. C., Norman, B. A., Rausch, D., & Woodring, J. (2019). Using Mathematical Modeling to Improve the Emergency Department Nurse-Scheduling Process. Journal of Emergency Nursing, 45(4), 425–432. https://doi.org/10.1016/j.jen.2019.01.013
Tassopoulos, I. X., Solos, I. P., & Beligiannis, G. N. (2015). Α two-phase adaptive variable neighborhood approach for nurse rostering. Computers & Operations Research, 60, 150–169. https://doi.org/10.1016/j.cor.2015.02.009
Yahia, Z., Eltawil, A. B., & Harraz, N. A. (2016). The operating room case-mix problem under uncertainty and nurses capacity constraints. Health Care Management Science, 19(4), 383–394. https://doi.org/10.1007/s10729-015-9337-z
Yilmaz, E. (2012). A Mathematical Programming Model for Scheduling of Nurses’ Labor Shifts. Journal of Medical Systems, 36(2), 491–496. https://doi.org/10.1007/s10916-010-9494-z
Zhang, X., Yang, Y., Zhu, Q., Lin, Q., Chen, W., Li, J., & Coello, C. A. C. (2024). Multi-agent deep Q-network-based metaheuristic algorithm for Nurse Rostering Problem. Swarm and Evolutionary Computation, 87, 101547. https://doi.org/10.1016/j.swevo.2024.101547
Zhang, Z., Hao, Z., & Huang, H. (2011). Hybrid Swarm-Based Optimization Algorithm of GA & VNS for Nurse Scheduling Problem. In Information Computing and Applications (pp. 375–382). https://doi.org/10.1007/978-3-642-25255-6_48
Zolfagharinia, H., Najafi, M., Rizvi, S., & Haghighi, A. (2024). Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques. Algorithms, 17(6), 234. https://doi.org/10.3390/a17060234