Addressing consistency and demand uncertainty in the Home Care planning problem

Abstract

Optimizing Home Care Services is receiving a great attention in Operations Research. We address arrival time consistency, person-oriented consistency and demand uncertainty in Home Care, while jointly optimizing assignment, scheduling and routing decisions over a multiple-day time horizon. Consistent time schedules are very much appreciated by patients who, in this setting, are very sensitive to changes in their daily routines. Also person-oriented consistency positively impacts on service quality, guaranteeing that almost the same set of caregivers take care of a patient in the planning horizon. Demand uncertainty plays a pivotal role, too, since both the set of patients under treatment and their care plan can change over time. To the best of our knowledge, this is the first paper dealing with all these aspects in Home Care via a robust approach. We present a mathematical model to the problem, and a pattern-based algorithmic framework to solve it. The framework is derived from the model via decomposition, i.e. suitably fixing the scheduling decisions through the concept of pattern. We propose alternative policies to generate patterns, taking into account consistency and demand uncertainty; when embedding them in the general framework, alternative pattern based algorithms originate. The results of a rich computational experience show that introducing consistency and demand uncertainty in pattern generation policies is crucial to efficiently compute very good quality solutions, in terms of robustness and balancing of the caregiver workload. In addition, a comparison with a simpler model, where no kind of consistency is imposed, shows the importance of considering consistency in pursuing a valuable patient-centered perspective, with a positive effect also on the efficiency of the solution approach.

Integrated Home Care Staffing and Capacity Planning: Stochastic Optimization Approaches

“…It assumes that the decision maker is risk-averse and has no distributional knowledge about the underlying uncertainty, except for its support (partial information), and the model minimizes the worstcase cost over an uncertainty set (Ben-Tal et al, 2009). In home care service area, several studies propose robust approaches to address the uncertainty (Carello & Lanzarone, 2014;Cappanera et al, 2018;Shi et al, 2019;Cappanera & Scutellà, 2021). Notably, Cappanera et al (2018) addressed uncertainty of patient demand over a multiple-day time horizon and jointly studied the assignment, scheduling and routing decisions via a non-standard cardinalityconstrained robust approach.…”

PROASSIST4.0: An Optimized Tele-Assistance System for the Future Healthcare

“…The design of efficient and high added-value home care services requires the coordination of a complex set of intertwined decisions ( [5]) and outputs: (i) the operator (or operators) assigned to each patient (assignment decisions), (ii) the scheduling of patient visits in the planning horizon (scheduling decisions), and (iii) the sequencing with which each operator, every day of the planning horizon, visits the patients assigned to him or her (routing decisions). These decisions can be made guaranteeing possible constraints of care continuity or loyalty and respecting temporal constraints such as those imposing that the service is given within time windows provided by the patients or according to a certain regularity [6] (for example, visits always made in the morning or in the afternoon to facilitate the family organisation of the patient). In addition, service design may require the coordination/synchronization among several operators at the patient's promise and/or the management of specific devices shared among patients [7].…”

Dynamic scheduling of home care patients to medical providers

“…Demirbilek et al (2019Demirbilek et al ( , 2021 demonstrate using a simulation study that a scenario-based approach to encoding demand uncertainty can outperform a myopic policy. Finally, Shahnejat-Bushehri et al (2021) and Cappanera and Scutellà (2022) use a robust optimization formulation to study the effects of temporal precedence and synchronization constraints as well as service consistency (arrival time, continuity of care), respectively. As compared to this literature, our model is dynamic and allows for multiple patient-HP assignments per period.…”

“…Finally, Shahnejat‐Bushehri et al. (2021) and Cappanera and Scutellà (2022) use a robust optimization formulation to study the effects of temporal precedence and synchronization constraints as well as service consistency (arrival time, continuity of care), respectively. As compared to this literature, our model is dynamic and allows for multiple patient–HP assignments per period.…”