Operational research applied to decisions in home health care: A systematic literature review

References

• Akao, Y. (2004). Quality function deployment: Integrating customer requirments into product design. Productivity Press.
   Google Scholar
• Akjiratikarl, C., Yenradee, P., & Drake, P. R. (2007). PSO-based algorithm for home care worker scheduling in the UK. Computers & Industrial Engineering, 53(4), 559–583. doi:https://doi.org/10.1016/j.cie.2007.06.002
   Web of Science ®Google Scholar
• An, Y.-J., Kim, Y.-D., Jeong, B. J., & Kim, S.-D. (2012). Scheduling healthcare services in a home healthcare system. Journal of the Operational Research Society, 63(11), 1589–1599. https://doi.org/10.1057/jors.2011.153
   Web of Science ®Google Scholar
• Argiento, R., Guglielmi, A., Lanzarone, E., & Nawajah, I. (2016). A Bayesian framework for describing and predicting the stochastic demand of home care patients. Flexible Services and Manufacturing Journal, 28(1-2), 254–279. https://doi.org/10.1007/s10696-014-9200-4
   Web of Science ®Google Scholar
• Bachouch, R. B., Guinet, A., & Hajri-Gabouj, S. (2011). A decision-making tool for home health care nurses’ planning. Supply Chain Forum: An International Journal, 12(1), 14–20. https://doi.org/10.1080/16258312.2011.11517250
   Google Scholar
• Bard, J. F., Shao, Y., & Jarrah, A. I. (2014). A sequential GRASP for the therapist routing and scheduling problem. Journal of Scheduling, 17(2), 109–133. https://doi.org/10.1007/s10951-013-0345-x
   Web of Science ®Google Scholar
• Bard, J. F., Shao, Y., Qi, X., & Jarrah, A. I. (2014). The traveling therapist scheduling problem. IIE Transactions, 46(7), 683–706. https://doi.org/10.1080/0740817X.2013.851434
   Web of Science ®Google Scholar
• Bard, J. F., Shao, Y., & Wang, H. (2013). Weekly scheduling models for traveling therapists. Socio-Economic Planning Sciences, 47(3), 191–204. https://doi.org/10.1016/j.seps.2012.07.001
   Web of Science ®Google Scholar
• Barua, A., Ravindran, S., & Whinston, A. B. (1997). Coordination in information exchange between organizational decision units. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 27(5), 690–698. https://doi.org/10.1109/3468.618268
   Web of Science ®Google Scholar
• Begur, S. V., Miller, D. M., & Weaver, J. R. (1997). An integrated spatial DSS for scheduling and routing home-health-care nurses. Interfaces, 27(4), 35–48. https://doi.org/10.1287/inte.27.4.35
   Web of Science ®Google Scholar
• Bennett, A. R., & Erera, A. L. (2011). Dynamic periodic fixed appointment scheduling for home health. IIE Transactions on Healthcare Systems Engineering, 1(1), 6–19. https://doi.org/10.1080/19488300.2010.549818
   Google Scholar
• Benzarti, E., Sahin, E., & Dallery, Y. (2013). Operations management applied to home care services: Analysis of the districting problem. Decision Support Systems, 55(2), 587–598. https://doi.org/10.1016/j.dss.2012.10.015
   Web of Science ®Google Scholar
• Bertels, S., & Fahle, T. (2006). A hybrid setup for a hybrid scenario: Combining heuristics for the home health care problem. Computers & Operations Research, 33(10), 2866–2890. https://doi.org/10.1016/j.cor.2005.01.015
   Web of Science ®Google Scholar
• Blais, M., Lapierre, S. D., & Laporte, G. (2003). Solving a home-care districting problem in an urban setting. Journal of the Operational Research Society, 54(11), 1141–1147. https://doi.org/10.1057/palgrave.jors.2601625
   Web of Science ®Google Scholar
• Boucher, X., Bonjour, E., & Grabot, B. (2007). Formalisation and use of competencies for industrial performance optimisation: A survey. Computers in Industry, 58(2), 98–117. https://doi.org/10.1016/j.compind.2006.09.004
   Web of Science ®Google Scholar
• Bowers, J., Cheyne, H., Mould, G., & Page, M. (2015). Continuity of care in community midwifery. Health Care Management Science, 18(2), 195–204. https://doi.org/10.1007/s10729-014-9285-z
   PubMed Web of Science ®Google Scholar
• Braekers, K., Hartl, R. F., Parragh, S. N., & Tricoire, F. (2016). A bi-objective home care scheduling problem: Analyzing the trade-off between costs and client inconvenience. European Journal of Operational Research, 248(2), 428–443. https://doi.org/10.1016/j.ejor.2015.07.028
   Web of Science ®Google Scholar
• Burgess, J. F. (2012). Innovation and efficiency in health care: Does anyone really know what they mean? Health Systems, 1(1), 7–12. https://doi.org/10.1057/hs.2012.6
   Google Scholar
• Büyüktahtakın, I. E., & Haight, R. G. (2018). A review of operations research models in invasive species management: State of the art, challenges, and future directions. Annals of Operations Research, 271(2), 357–403. https://doi.org/10.1007/s10479-017-2670-5
   Web of Science ®Google Scholar
• Cappanera, P., & Scutellà, M. G. (2015). Joint assignment, scheduling, and routing models to home care optimization: A pattern-based approach. Transportation Science, 49(4), 830–852. https://doi.org/10.1287/trsc.2014.0548
   Web of Science ®Google Scholar
• Cappanera, P., Scutellà, M. G., Nervi, F., & Galli, L. (2018). Demand uncertainty in robust Home Care optimization. Omega, 80, 95–110. https://doi.org/10.1016/j.omega.2017.08.012
   Web of Science ®Google Scholar
• Carello, G., & Lanzarone, E. (2014). A cardinality-constrained robust model for the assignment problem in home care services. European Journal of Operational Research, 236(2), 748–762. https://doi.org/10.1016/j.ejor.2014.01.009
   Web of Science ®Google Scholar
• Carello, G., Lanzarone, E., & Mattia, S. (2018). Trade-off between stakeholders’ goals in the home care nurse-to-patient assignment problem. Operations Research for Health Care, 16, 29–40. https://doi.org/10.1016/j.orhc.2017.12.002
   Web of Science ®Google Scholar
• Cattafi, M., Herrero, R., Gavanelli, M., Nonato, M., & Malucelli, F. (2015). An application of constraint solving for home health care. AI Communications, 28(2), 215–237. https://doi.org/10.3233/AIC-140632
   Web of Science ®Google Scholar
• Chen, S.-J., & Lin, L. (2004). Modeling team member characteristics for the formation of a multifunctional team in concurrent engineering. IEEE Transactions on Engineering Management, 51(2), 111–124. https://doi.org/10.1109/TEM.2004.826011
   Web of Science ®Google Scholar
• Chrysopoulos, A., & Mitkas, P. A. (2018). Customized time-of-use pricing for small-scale consumers using multi-objective particle swarm optimization. Advances in Building Energy Research, 12(1), 25–47. https://doi.org/10.1080/17512549.2017.1325397
   Web of Science ®Google Scholar
• Cissé, M., Yalçındağ, S., Kergosien, Y., Şahin, E., Lenté, C., & Matta, A. (2017). OR problems related to home health care: A review of relevant routing and scheduling problems. Operations Research for Health Care, 13-14, 1–22. https://doi.org/10.1016/j.orhc.2017.06.001
   Web of Science ®Google Scholar
• De Angelis, V. (1998). Planning home assistance for AIDS patients in the City of Rome. Interfaces, 28(3), 75–83. https://doi.org/10.1287/inte.28.3.75
   Web of Science ®Google Scholar
• Decerle, J., Grunder, O., El Hassani, A. H., & Barakat, O. (2016). A two-phases matheuristic for the home care routing and scheduling problem. IFAC-PapersOnLine, 49(12), 1484–1489. https://doi.org/10.1016/j.ifacol.2016.07.781
   Google Scholar
• Decerle, J., Grunder, O., El Hassani, A. H., & Barakat, O. (2018). A memetic algorithm for a home health care routing and scheduling problem. Operations Research for Health Care, 16, 59–71. https://doi.org/10.1016/j.orhc.2018.01.004
   Web of Science ®Google Scholar
• deLlano-Paz, F., Calvo-Silvosa, A., Antelo, S. I., & Soares, I. (2017). Energy planning and modern portfolio theory: A review. Renewable and Sustainable Energy Reviews, 77, 636–651. https://doi.org/10.1016/j.rser.2017.04.045
   Web of Science ®Google Scholar
• Du, G., Liang, X., & Sun, C. (2017). Scheduling optimization of home health care service considering patients’ priorities and time windows. Sustainability, 9(2), 253. https://doi.org/10.3390/su9020253
   Web of Science ®Google Scholar
• Durugbo, C., Tiwari, A., & Alcock, J. R. (2013). Modelling information flow for organisations: A review of approaches and future challenges. International Journal of Information Management, 33(3), 597–610. https://doi.org/10.1016/j.ijinfomgt.2013.01.009
   Web of Science ®Google Scholar
• En-Nahli, L., Allaoui, H., & Nouaouri, I. (2015). A multi-objective modelling to human resource assignment and routing problem for home health care services. IFAC-PapersOnLine, 48(3), 698–703. https://doi.org/10.1016/j.ifacol.2015.06.164
   Google Scholar
• Erdem, M., & Bulkan, S. (2017). A two-stage solution approach for the large-scale home healthcare routeing and scheduling problem. South African Journal of Industrial Engineering, 28(4), 133–149. https://doi.org/10.7166/28-4-1754
   Web of Science ®Google Scholar
• Errarhout, A., Kharraja, S., & Corbier, C. (2016). Two-stage stochastic assignment problem in the home health care. IFAC-PapersOnLine, 49(12), 1152–1157. https://doi.org/10.1016/j.ifacol.2016.07.659
   Google Scholar
• Eveborn, P., Flisberg, P., & Rönnqvist, M. (2006). Laps care—an operational system for staff planning of home care. European Journal of Operational Research, 171(3), 962–976. https://doi.org/10.1016/j.ejor.2005.01.011
   Web of Science ®Google Scholar
• Eveborn, P., Rönnqvist, M., Einarsdóttir, H., Eklund, M., Lidén, K., & Almroth, M. (2009). Operations research improves quality and efficiency in home care. Interfaces, 39(1), 18–34. https://doi.org/10.1287/inte.1080.0411
   Web of Science ®Google Scholar
• Fikar, C., & Hirsch, P. (2015). A matheuristic for routing real-world home service transport systems facilitating walking. Journal of Cleaner Production, 105, 300–310. https://doi.org/10.1016/j.jclepro.2014.07.013
   Web of Science ®Google Scholar
• Fikar, C., & Hirsch, P. (2017). Home health care routing and scheduling: A review. Computers & Operations Research, 77, 86–95. https://doi.org/10.1016/j.cor.2016.07.019
   Web of Science ®Google Scholar
• Fikar, C., Juan, A. A., Martinez, E., & Hirsch, P. (2016). A discrete-event driven metaheuristic for dynamic home service routing with synchronised trip sharing. European J. of Industrial Engineering, 10(3), 323. https://doi.org/10.1504/EJIE.2016.076382
   Web of Science ®Google Scholar
• Green, L. V., Soares, J., Giglio, J. F., & Green, R. A. (2006). Using queueing theory to increase the effectiveness of emergency department provider staffing. Academic Emergency Medicine, 13(1), 61–68. https://doi.org/10.1197/j.aem.2005.07.034
   PubMed Web of Science ®Google Scholar
• Guericke, D., & Suhl, L. (2017). The home health care problem with working regulations. Or Spectrum, 39(4), 977–1010. https://doi.org/10.1007/s00291-017-0481-3
   Web of Science ®Google Scholar
• Gutiérrez, E. V., & Vidal, C. J. (2013). Home health care logistics management problems: A critical review of models and methods. Revista Facultad de Ingeniería Universidad de Antioquia, 68, 160–175.
   Google Scholar
• Hertz, A., & Lahrichi, N. (2009). A patient assignment algorithm for home care services. Journal of the Operational Research Society, 60(4), 481–495. https://doi.org/10.1057/palgrave.jors.2602574
   Web of Science ®Google Scholar
• Hewitt, M., Nowak, M., & Nataraj, N. (2016). Planning strategies for home health care delivery. Asia-Pacific Journal of Operational Research, 33(05), 1650041. https://doi.org/10.1142/S021759591650041X
   Web of Science ®Google Scholar
• Hiermann, G., Prandtstetter, M., Rendl, A., Puchinger, J., & Raidl, G. R. (2015). Metaheuristics for solving a multimodal home-healthcare scheduling problem. Central European Journal of Operations Research, 23(1), 89–113. https://doi.org/10.1007/s10100-013-0305-8
   Web of Science ®Google Scholar
• Hindle, T., Hindle, A., & Spollen, M. (2000). Resource allocation modelling for home-based health and social care services in areas having differential population density levels: A case study in Northern Ireland. Health Services Management Research, 13(3), 164–169. https://doi.org/10.1177/095148480001300304
   PubMedGoogle Scholar
• Hindle, T., Hindle, G., & Spollen, M. (2009). Travel-related costs of population dispersion in the provision of domiciliary care to the elderly: A case study in English Local Authorities. Health Services Management Research, 22(1), 27–32. https://doi.org/10.1258/hsmr.2008.008012
   PubMedGoogle Scholar
• Hulshof, P. J. H., Kortbeek, N., Boucherie, R. J., Hans, E. W., & Bakker, P. J. M. (2012). Taxonomic classification of planning decisions in health care: A structured review of the state of the art in OR/MS. Health Systems, 1(2), 129–175. https://doi.org/10.1057/hs.2012.18
   Google Scholar
• Ingolfsson, A., Campello, F., Wu, X., & Cabral, E. (2010). Combining integer programming and the randomization method to schedule employees. European Journal of Operational Research, 202(1), 153–163. https://doi.org/10.1016/j.ejor.2009.04.026
   Web of Science ®Google Scholar
• Koeleman, P. M., Bhulai, S., & van Meersbergen, M. (2012). Optimal patient and personnel scheduling policies for care-at-home service facilities. European Journal of Operational Research, 219(3), 557–563. https://doi.org/10.1016/j.ejor.2011.10.046
   Web of Science ®Google Scholar
• Lanzarone, E., & Matta, A. (2012). A cost assignment policy for home care patients. Flexible Services and Manufacturing Journal, 24(4), 465–495. https://doi.org/10.1007/s10696-011-9121-4
   Web of Science ®Google Scholar
• Lanzarone, E., Matta, A., & Sahin, E. (2012). Operations management applied to home care services: The problem of assigning human resources to patients. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 42(6), 1346–1363. https://doi.org/10.1109/TSMCA.2012.2210207
   Web of Science ®Google Scholar
• Lanzarone, E., Matta, A., & Scaccabarozzi, G. (2010). A patient stochastic model to support human resource planning in home care. Production Planning & Control, 21(1), 3–25. https://doi.org/10.1080/09537280903232362
   Web of Science ®Google Scholar
• Lin, C.-C., Hung, L.-P., Liu, W.-Y., & Tsai, M.-C. (2018). Jointly rostering, routing, and rerostering for home health care services: A harmony search approach with genetic, saturation, inheritance, and immigrant schemes. Computers & Industrial Engineering, 115, 151–166. https://doi.org/10.1016/j.cie.2017.11.004
   Web of Science ®Google Scholar
• Lin, M., Chin, K. S., Wang, X., & Tsui, K. L. (2016). The therapist assignment problem in home healthcare structures. Expert Systems with Applications, 62, 44–62. https://doi.org/10.1016/j.eswa.2016.06.010
   Web of Science ®Google Scholar
• Lin, M., Chin, K.-S., Fu, C., & Tsui, K.-L. (2017). An effective greedy method for the meals-on-wheels service districting problem. Computers & Industrial Engineering, 106, 1–19. https://doi.org/10.1016/j.cie.2017.01.022
   Web of Science ®Google Scholar
• Liu, M., Yang, D., Su, Q., & Xu, L. (2018). Bi-objective approaches for home healthcare medical team planning and scheduling problem. Computational and Applied Mathematics, 37(4), 4443–4474. https://doi.org/10.1007/s40314-018-0584-8
   Web of Science ®Google Scholar
• Liu, R., Xie, X., Augusto, V., & Rodriguez, C. (2013). Heuristic algorithms for a vehicle routing problem with simultaneous delivery and pickup and time windows in home health care. European Journal of Operational Research, 230(3), 475–486. https://doi.org/10.1016/j.ejor.2013.04.044
   Web of Science ®Google Scholar
• Liu, R., Xie, X., & Garaix, T. (2014). Hybridization of tabu search with feasible and infeasible local searches for periodic home health care logistics. Omega, 47, 17–32. https://doi.org/10.1016/j.omega.2014.03.003
   Web of Science ®Google Scholar
• Liu, R., Yuan, B., & Jiang, Z. (2017). Mathematical model and exact algorithm for the home care worker scheduling and routing problem with lunch break requirements. International Journal of Production Research, 55(2), 558–575. https://doi.org/10.1080/00207543.2016.1213917
   Web of Science ®Google Scholar
• López-Santana, E. R., Espejo-Díaz, J. A., & Méndez-Giraldo, G. A. (2016). Multi-agent approach for solving the dynamic home health care routing problem. In J. C. Figueroa-García, E. R. López-Santana, & R. Ferro-Escobar (Eds.), Applied computer sciences in engineering. (Vol. 657, pp. 188–200). Cham: Springer. https://doi.org/10.1007/978-3-319-50880-1_17
   Google Scholar
• Manerba, D., & Mansini, R. (2016). The nurse routing problem with workload constraints and incompatible services. IFAC-PapersOnLine, 49(12), 1192–1197. https://doi.org/10.1016/j.ifacol.2016.07.670
   Google Scholar
• Mankowska, D. S., Meisel, F., & Bierwirth, C. (2014). The home health care routing and scheduling problem with interdependent services. Health Care Management Science, 17(1), 15–30. https://doi.org/10.1007/s10729-013-9243-1
   PubMed Web of Science ®Google Scholar
• Marcon, E., Chaabane, S., Sallez, Y., Bonte, T., & Trentesaux, D. (2017). A multi-agent system based on reactive decision rules for solving the caregiver routing problem in home health care. Simulation Modelling Practice and Theory, 74, 134–151. https://doi.org/10.1016/j.simpat.2017.03.006
   Web of Science ®Google Scholar
• Masmoudi, M., & Abdelaziz, F. B. (2018). Portfolio selection problem: A review of deterministic and stochastic multiple objective programming models. Annals of Operations Research, 267(1-2), 335–352. https://doi.org/10.1007/s10479-017-2466-7
   Web of Science ®Google Scholar
• Maya Duque, P. A., Castro, M., Sörensen, K., & Goos, P. (2015). Home care service planning. The case of Landelijke Thuiszorg. European Journal of Operational Research, 243(1), 292–301. https://doi.org/10.1016/j.ejor.2014.11.008
   Web of Science ®Google Scholar
• Monitor. (2015). Moving healthcare closer to home. https://www.gov.uk/guidance/moving-healthcare-closer-to-home
   Google Scholar
• Mutingi, M., Mbohwa, C. (2013). Home healthcare worker scheduling: A group genetic algorithm approach [Poster presentation]. Proceedings of the World Congress on Engineering 2013, I, 721–725. http://www.iaeng.org/publication/WCE2013/WCE2013_pp721-725.pdf
   Google Scholar
• Mutingi, M., & Mbohwa, C. (2014). A fuzzy-based particle swarm optimisation approach for task assignment in home healthcare. South African Journal of Industrial Engineering, 25(3), 84–95. https://doi.org/10.7166/25-3-777
   Web of Science ®Google Scholar
• Nasir, J. A., & Dang, C. (2016). Identifying quantitative thresholds for the home health care problem [Paper presentation]. 2016 IEEE Symposium on Computers and Communication (ISCC), 220–225. https://doi.org/10.1109/ISCC.2016.7543743
   Google Scholar
• Nasir, J. A., & Dang, C. (2018). Solving a more flexible home health care scheduling and routing problem with joint patient and nursing staff selection. Sustainability, 10(2), 148. https://doi.org/10.3390/su10010148
   Web of Science ®Google Scholar
• Nasir, J. A., Hussain, S., & Dang, C. (2018). An integrated planning approach towards home health care, telehealth and patients group based care. Journal of Network and Computer Applications, 117, 30–41. https://doi.org/10.1016/j.jnca.2018.05.009
   Web of Science ®Google Scholar
• NHS England. (2014). The NHS five year forward view. https://www.england.nhs.uk/publication/nhs-five-year-forward-view/
   Google Scholar
• Nickel, S., Schröder, M., & Steeg, J. (2012). Mid-term and short-term planning support for home health care services. European Journal of Operational Research, 219(3), 574–587. https://doi.org/10.1016/j.ejor.2011.10.042
   Web of Science ®Google Scholar
• Palmer, R., Fulop, N. J., & Utley, M. (2018). A systematic literature review of operational research methods for modelling patient flow and outcomes within community healthcare and other settings. Health Systems, 7(1), 29–50. https://doi.org/10.1057/s41306-017-0024-9
   PubMed Web of Science ®Google Scholar
• Quintana, D., Cervantes, A., Saez, Y., & Isasi, P. (2017). Clustering technique for large-scale home care crew scheduling problems. Applied Intelligence, 47(2), 443–455. https://doi.org/10.1007/s10489-017-0908-1
   Web of Science ®Google Scholar
• Rasmussen, M. S., Justesen, T., Dohn, A., & Larsen, J. (2012). The home care crew scheduling problem: Preference-based visit clustering and temporal dependencies. European Journal of Operational Research, 219(3), 598–610. https://doi.org/10.1016/j.ejor.2011.10.048
   Web of Science ®Google Scholar
• Redjem, R., & Marcon, E. (2016). Operations management in the home care services: A heuristic for the caregivers’ routing problem. Flexible Services and Manufacturing Journal, 28(1-2), 280–303. https://doi.org/10.1007/s10696-015-9220-8
   Web of Science ®Google Scholar
• Rest, K.-D., & Hirsch, P. (2015). Supporting urban home health care in daily business and times of disasters. IFAC-PapersOnLine, 48(3), 686–691. https://doi.org/10.1016/j.ifacol.2015.06.162
   Google Scholar
• Rest, K.-D., & Hirsch, P. (2016). Daily scheduling of home health care services using time-dependent public transport. Flexible Services and Manufacturing Journal, 28(3), 495–525. https://doi.org/10.1007/s10696-015-9227-1
   Web of Science ®Google Scholar
• Rodriguez, C., Garaix, T., Xie, X., & Augusto, V. (2015). Staff dimensioning in homecare services with uncertain demands. International Journal of Production Research, 53(24), 7396–7410. https://doi.org/10.1080/00207543.2015.1081427
   Web of Science ®Google Scholar
• Sahin, E., & Matta, A. (2015). A contribution to operations management-related issues and models for home care structures. International Journal of Logistics Research and Applications, 18(4), 355–385. https://doi.org/10.1080/13675567.2014.946560
   Web of Science ®Google Scholar
• Shao, Y., Bard, J. F., & Jarrah, A. I. (2012). The therapist routing and scheduling problem. IIE Transactions, 44(10), 868–893. https://doi.org/10.1080/0740817X.2012.665202
   Web of Science ®Google Scholar
• Shi, Y., Boudouh, T., & Grunder, O. (2017a). A hybrid genetic algorithm for a home health care routing problem with time window and fuzzy demand. Expert Systems with Applications, 72, 160–176. https://doi.org/10.1016/j.eswa.2016.12.013
   Web of Science ®Google Scholar
• Shi, Y., Boudouh, T., & Grunder, O. (2017b). A home health care routing problem with stochastic travel and service time. IFAC-PapersOnLine, 50(1), 13987–13992. https://doi.org/10.1016/j.ifacol.2017.08.2419
   Google Scholar
• Shi, Y., Boudouh, T., Grunder, O., & Wang, D. (2018). Modeling and solving simultaneous delivery and pick-up problem with stochastic travel and service times in home health care. Expert Systems with Applications, 102, 218–233. https://doi.org/10.1016/j.eswa.2018.02.025
   Web of Science ®Google Scholar
• Sommerville, J., & Dalziel, S. (1998). Project teambuilding—the applicability of Belbin’s team-role self-perception inventory. International Journal of Project Management, 16(3), 165–171. https://doi.org/10.1016/S0263-7863(97)00054-9
   Google Scholar
• Trautsamwieser, A., Gronalt, M., & Hirsch, P. (2011). Securing home health care in times of natural disasters. Or Spectrum, 33(3), 787–813. https://doi.org/10.1007/s00291-011-0253-4
   Web of Science ®Google Scholar
• Trautsamwieser, A., & Hirsch, P. (2011). Optimization of daily scheduling for home healthcare services. Journal of Applied Operational Research, 3(3), 124–136.
   Google Scholar
• Trautsamwieser, A., & Hirsch, P. (2014). A branch-price-and-cut approach for solving the medium-term home health care planning problem. Networks, 64(3), 143–159. https://doi.org/10.1002/net.21566
   Web of Science ®Google Scholar
• Wirnitzer, J., Heckmann, I., Meyer, A., & Nickel, S. (2016). Patient-based nurse rostering in home care. Operations Research for Health Care, 8, 91–102. https://doi.org/10.1016/j.orhc.2015.08.005
   Web of Science ®Google Scholar
• Yalçındağ, S., Cappanera, P., Grazia Scutellà, M., Şahin, E., & Matta, A. (2016). Pattern-based decompositions for human resource planning in home health care services. Computers & Operations Research, 73, 12–26. https://doi.org/10.1016/j.cor.2016.02.011
   Web of Science ®Google Scholar
• Yan, H.-B., Cai, S., & Li, M. (2016). A two-stage fuzzy quality function deployment model for service design. In V.-N. Huynh, M. Inuiguchi, B. Le, B. N. Le, & T. Denoeux (Eds.), Integrated uncertainty in knowledge modelling and decision making. (Vol. 9978, pp. 110–123). Cham: Springer. https://doi.org/10.1007/978-3-319-49046-5_10
   Google Scholar
• Yuan, B., & Jiang, Z. (2017). Disruption management for the real-time home caregiver scheduling and routing problem. Sustainability, 9(12), 2178. https://doi.org/10.3390/su9122178.
   Web of Science ®Google Scholar
• Yuan, B., Liu, R., & Jiang, Z. (2015). A branch-and-price algorithm for the home health care scheduling and routing problem with stochastic service times and skill requirements. International Journal of Production Research, 53(24), 7450–7464. https://doi.org/10.1080/00207543.2015.1082041
   Web of Science ®Google Scholar
• Yuan, B., Liu, R., & Jiang, Z. (2018). Daily scheduling of caregivers with stochastic times. International Journal of Production Research, 56(9), 3245–3261. https://doi.org/10.1080/00207543.2018.1445879
   Web of Science ®Google Scholar
• Zhang, R., Fry, M. J., & Krishnan, H. (2015). Efficiency and Equity in Healthcare: An Analysis of Resource Allocation Decisions in a Long-Term Home Care Setting. INFOR: Information Systems and Operational Research, 53(3), 97–112. https://doi.org/10.3138/infor.53.3.97
   Web of Science ®Google Scholar
• Zhang, S., Xu, J., Gou, H., & Tan, J. (2017). A research review on the key technologies of intelligent design for customized products. Engineering, 3(5), 631–640. https://doi.org/10.1016/J.ENG.2017.04.005
   Web of Science ®Google Scholar