Healthcare Systems. Группа авторов

12, 568–581.

      Decerle, J., Grunder, O., Hajjam El Hassani, A., Barakat, O. (2018). A memetic algorithm for a home health care routing and scheduling problem. Operations Research for Health Care, 16, 59–71.

      Di Mascolo, M., Martinez, C., Espinouse, M.L. (2021). Routing and scheduling in home health care: A literature survey and bibliometric analysis. Computers & Industrial Engineering, 158, 107255.

      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.

      Fathollahi-Fard, A.M., Hajiaghaei-Keshteli, M., Mirjalili, S. (2019). A set of efficient heuristics for a home healthcare problem. Neural Computing and Applications, 32, 6185–6205.

      Fikar, C. and Hirsch, P. (2015). A matheuristic for routing real-world home service transport systems facilitating walking. Journal of Cleaner Production, 105, 300–310.

      Fikar, C. and Hirsch, P. (2017). Home health care routing and scheduling: A review. Computers & Operations Research, 77, 86–95.

      Gomes, M.I. and Ramos, T.R.P. (2019). Modelling and (re-)planning periodic home social care services with loyalty and non-loyalty features. European Journal of Operational Research, 277, 284–299.

      Grenouilleau, F., Legrain, A., Lahrichi, N., Rousseau, L.-M. (2017). A set partitioning heuristic for the home health care routing and scheduling problem [Online]. Available at: http://www.cirrelt.ca/DocumentsTravail/CIRRELT-2017-70.pdf.

      Grieco, L., Utley, M., Crowe, S. (2020). Operational research applied to decisions in home health care: A systematic literature review. Journal of the Operational Research Society, 1–32.

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      Heching, A., Hooker, J.N., Kimura, R. (2019). A logic-based benders approach to home healthcare delivery. Transportation Science, 53, 510–522.

      Issaoui, B., Zidi, I., Marcon, E., Ghedira, K. (2015). New multi-objective approach for the home care service problem based on scheduling algorithms and variable neighborhood descent. Electronic Notes in Discrete Mathematics, 47, 181–188.

      Kandakoglu, A., Sauré, A., Michalowski, M., Aquino, M., Graham, J., McCormick, B. (2020). A decision support system for home dialysis visit scheduling and nurse routing. Decision Support Systems, 130, 113224.

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      Lin, S. and Kernighan, B.W. (1973). An effective heuristic algorithm for the traveling-salesman problem. Operations Research, 21, 498–516.

      Martinez, C., Espinouse, M.-L., Di Mascolo, M. (2019). Re-planning in home healthcare: A decomposition approach to minimize idle times for workers while ensuring continuity of care. Proceedings of the IFAC Conference on Manufacturing Modeling, Management and Control, Berlin, Germany.

      Mosquera, F., Smet, P., Vanden Berghe, G. (2019). Flexible home care scheduling. Omega, 83, 80–95.

      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, 574–587.

      Rizzi, R., Sacomoto, G., Sagot, M.-F. (2015). Efficiently listing bounded length st-paths. In Combinatorial Algorithms, Lecture Notes in Computer Science, Jan, K., Miller, M., Froncek, D. (eds). Springer, Berlin.

      Szander, N., Ros-McDonnell, L.B., Bogataj, M. (2018). A feasible nurse routing plan for the elderly: Quality and spatial trade-offs. In Closing the Gap Between Practice and Research in Industrial Engineering, Lecture Notes in Management and Industrial Engineering, Viles, E., Ormazábal, M., Lleó, A. (eds). Springer, Berlin.

      Chapter written by Cléa MARTINEZ, Maria DI MASCOLO, Marie-Laure ESPINOUSE and Jérôme RADUREAU.

      2

      Home Healthcare Scheduling Activities

      2.1. Introduction

      Home healthcare (HHC) is a field of research which, due to its complexity in terms of organization and diversity of players, not to mention in the medical and social specificities of patients, opens up several fields of investigation. We can identify the optimization challenges inherent to the management of the HHC structure, which in turn leads to significant research activity on problems concerning nurse rostering and drug deliveries. The optimization problem in HHC must take into account several constraints related to the organization of the structure, the patient, road traffic, emergencies, etc. The purpose is to ensure visits to patients according to their care plans. One of the major constraints in the organization of care is taking into account emergencies as well as uncertainties such as changes in the duration of care and also the disturbances caused by the external environment (road traffic, for example).

      In this chapter, we present a solution for the HHC planning problem by integrating uncertainties and dynamic rescheduling. We first present a literature review with recent works that have focused on HHC planning. Section 2.3 focus on the proposed approach, which is based on a genetic algorithm with the consideration of real-time disturbances and uncertainties. Section 2.4 presents the experiments and the obtained results. We finish this chapter with a conclusion and some perspectives.

2.2. State of the art

      The increase of the number of elderly people in France has led to a substantial increase in the demand for HHC services (e.g. home support structures, home hospitalization, etc.). This demand is due to two main causes:

       – home care is less expensive for health insurance companies and helps to relieve congestion in hospitals;

       – people who are still autonomous want to stay at home and benefit from HHC services (cleaning, reading, meal delivery, etc.).

      HHC structures play an important role in the development of the silver economy and offer better well-being for elderly people. However, the organization of HHC is complex and difficult to control due to the multiplicity and diversity of players, their status (private or public), as well as their function (medical, paramedical, social, administrative). Moreover, each treatment is specific and must be personalized insofar as it relies on the patient’s level of dependency.

      HHC services require coordinated organization between all the stakeholders involved in the patient care plan. This coordination must take into account organizational and geographical constraints, working hours and constraints related to the patients (availability, preferences, activities of daily living, etc.).

      Several constraints must be taken into account in HHC scheduling problems (Fikar and Hirsch 2017). Three classes of constraints have been identified: time constraints, assignment constraints and geographic constraints. The first class concerns the constraints associated with temporal relationships, that is, at what time the HHC service can take place or the time at which a caregiver can provide a service according to their schedule. Assignment constraints are linked to the relationships