Home Care Scheduling Research Articles

An Adaptive Large Neighbourhood Search algorithm for a real-world Home Care Scheduling Problem with time windows and dynamic breaks

This paper presents a Home Care Scheduling Problem (from now on HCSP) based on a real case of a care company for elderly and dependent people located in the North of Spain. The problem incorporates many of the common features addressed in the HCSP literature, such as soft and hard time windows for the services, available working time of the caregivers or affinity levels between users and caregivers. However, it also includes other novel characteristics that increase the difficulty of the problem significantly, since the breaks between services will play a key role in the quality of the solutions. To evaluate the solutions, the users welfare will be prioritized over the cost associated with the schedule.The problem has been formulated as a Mixed Integer Linear Programming (MILP) one but, due to the complexity of the model, it is not possible to solve it for real size instances. Therefore, we have designed a method that combines the Adaptive Large Neighbourhood Search (ALNS) methodology with a heuristic approach necessary to evaluate the objective functions. To analyse the behaviour of the algorithm, a set of computational experiments are carried out under different configurations. First, the MILP formulation and the algorithm have been compared over some standard instances from the literature. Finally, the performance of the algorithm is evaluated over a real case study based on the timetables of the company during some weeks from 2016 to 2017.

Home Health Care Scheduling Problem Under Uncertainty: Robust Optimization Approaches

This paper deals with the home health care service (HHCS) which is defined as a set of medical, paramedical and social services delivered to patients in their domicile rather than in hospital. To support decision making in HHC, optimization models have been used. However, several of those models are deterministic and do not address the dynamical and uncertainty aspects of the system and variability of some patient data. The HHC scheduling problems are facing more and more complex and specific constraints. These constraints have to be respected, meanwhile the problem objective is optimized under parameters uncertainties. This paper aims to formulate a model that integrated home care scheduling problem while taking into account human aspect -- the behavior of patients, and also another aspect like travel time uncertainty and dynamic behavior of involved medical team and social actors in HHC. Robust approaches are adopted to model and handle this uncertainty.

A Matheuristic Approach for the Home Care Scheduling Problem With Chargeable Overtime and Preference Matching

Home care (HC) services represent an effective solution to face the health issues related to population aging. However, several scheduling problems arise in HC, and the providers must make several scheduling and routing decisions, e.g., the assignment of caregivers to clients, in order to balance operating costs and client satisfaction. Starting from the analysis of a real HC provider operating in New York City, NY, USA, this article addresses a scheduling problem with chargeable overtime and preference matching and formulates it as an integer programming model. The objective is to minimize a cost function that includes traveling costs, the overtime cost paid by the provider, the preference mismatch, and a penalty related to the continuity of care violation. To solve this problem, we design a matheuristic algorithm that integrates a specific variable neighborhood search with a set covering model. The results demonstrate the applicability and efficiency of our approach to solving real-size instances. Sensitivity analyses are also performed to discuss practical insights. Note to Practitioners-This article provides a decision support tool to HC managers, which appropriately assigns caregivers to clients and makes routing decisions over a long horizon. Chargeable overtimes and preference matching enclosed in this tool are rarely considered in the literature, despite matching is relevant in HC caregiver-to-client assignments and chargeable overtime has potential in tailoring the service level based on the specific client. We formulate the scheduling problem as a mathematical model. Then, we propose a matheuristic algorithm to efficiently solve the problem in real-size instances. The results show the applicability and efficiency of our method. Thus, HC managers can exploit it to efficiently make assignment and routing decisions and to analyze the impact on other operating costs when adjusting any of them.

A multistage optimisation algorithm for the large vehicle routing problem with time windows and synchronised visits

We propose a multistage algorithm for the vehicle routing problem with time windows and synchronised visits, which is capable of solving large problem instances arising in home health care applications. The algorithm is based on a constraint programming formulation of the daily home care scheduling and routing problem. It contains visits with hard time windows and pairwise synchronisation to be staffed by carers who have different skills and work custom shift patterns with contractual breaks. In a computational study, we first experiment with a benchmark set from the literature for the vehicle routing problem with time windows and synchronised visits. Our algorithm reproduced the majority of the best-known solutions, and strictly improved results for several other instances. Most importantly, we demonstrate that the algorithm can effectively solve real scheduling instances obtained from a UK home care provider. Their size significantly surpass similar scheduling problems considered in the literature. The multistage algorithm solved each of these instances in a matter of minutes, and outperformed human planners, scheduling more visits and significantly reducing total travel time.

New decomposition methods for home care scheduling with predefined visits

The continuous aging of the population and the desire of the elderly to stay in their own homes as long as possible has led to a considerable increase in the demand for home visits. In this context, home care agencies try to serve more patients while maintaining a high level of service. They must regularly decide which patients they can accept and how the patients will be scheduled (care provider, visit days, visit times). In this paper we aim to maximize the number of new patients accepted while ensuring a single provider-to-patient assignment and a consistency of the visits times for every patient through the week. To solve this problem, we propose an extension to an existing logic-based Benders decomposition. Moreover, we present a new pattern-based logic-based Benders decomposition and a matheuristic using a large neighborhood search. The experiments demonstrate the efficiency of the proposed approaches and show that the matheuristic can solve all the benchmark instances in less than 20 s.

IACS-HCSP: Improved ant colony optimization for large-scale home care scheduling problems

The home care and scheduling problem (HCSP) consists on the design of a set of routes to be used by caregivers that provide daily assistance at specific times to patients located in a definite geographic area. In this study we propose a modified version of Ant Colony Optimization (ACO), called IACS-HCSP, to approach this task. In order to be used in this problem, ACO requires modifications in the problem representation and additional mechanisms to deal with constraints. We propose a dynamic neighborhood graph and an improved method that constructs the solution that improves its exploration capability over deterministic or greedy heuristic methods. This technique has been applied to a very large real world instance of HCSP for which results are available for comparison. IACS-HCSP is able to improve the previous results on this specific instance in terms of cost. At the same time it can be used to help decision making when there is a choice between competing objectives, because it finds a full range of feasible solutions with different equilibria between time and size of the required labor force.

Flexible home care scheduling

Home care services are in high demand given how they are steadily becoming the primary source of care for the elderly. Powerful decision support tools are indispensable for effectively managing available staff in the context of ever-increasing demand for care and limited caregiver availability. This paper advances home care literature by introducing flexible task durations, thereby enabling tasks to be completed faster and ultimately more care to be scheduled. This new concept, which originates from practice, introduces an additional decision to be made when creating a schedule, thereby greatly increasing the scheduling complexity. Consequently, this paper introduces a new optimization-based decision support model which allows for scheduling with flexible task duration, as well as other types of flexibility. A computational study quantifies the impact of: (i) scheduling with a finer task granularity thereby enabling accurate prioritization of high and low priority care, (ii) flexibility in task duration enabling tasks to be completed faster and more care to be scheduled, and (iii) increasing the number of different locations visited by a caregiver thereby enabling a trade-off between the number of serviced clients and caregiver workload. A new publicly available real-world data set is used, obtained directly from home care organizations operating in Flanders. Analysis of the computational results demonstrates that significant improvements in operational efficiency may be realized with minimal effort required by organizations. Furthermore, the proposed algorithm’s performance is confirmed by comparison against the bounds obtained by solving an integer programming formulation of the problem. Finally, a management policy scheme is proposed which, when gradually implemented in a home care organization, results in a more efficient and therefore cost-effective deployment of its workforce.

A bi-objective home care scheduling problem: Analyzing the trade-off between costs and client inconvenience

Organizations providing home care services are inclined to optimize their activities in order to meet the constantly increasing demand for home care. In this context, home care providers are confronted with multiple, often conflicting, objectives such as minimizing their operating costs while maximizing the service level offered to their clients by taking into account their preferences. This paper is the first to shed some light on the trade-off relationship between these two objectives by modeling the home care routing and scheduling problem as a bi-objective problem. The proposed model accounts for qualifications, working regulations and overtime costs of the nurses, travel costs depending on the mode of transportation, hard time windows, and client preferences on visit times and nurses. A distinguishing characteristic of the problem is that the scheduling problem for a single route is a bi-objective problem in itself, thereby complicating the problem considerably. A metaheuristic algorithm, embedding a large neighborhood search heuristic in a multi-directional local search framework, is proposed to solve the problem. Computational experiments on a set of benchmark instances based on real-life data are presented. A comparison with exact solutions on small instances shows that the algorithm performs well. An analysis of the results reveals that service providers face a considerable trade-off between costs and client convenience. However, starting from a minimum cost solution, the average service level offered to the clients may already be improved drastically with limited additional costs.

An investigation on the generality level of selection hyper-heuristics under different empirical conditions

The present study concentrates on the generality of selection hyper-heuristics across various problem domains with a focus on different heuristic sets in addition to distinct experimental limits. While most hyper-heuristic research employs the term generality in describing the potential for solving various problems, the performance changes across different domains are rarely reported. Furthermore, a hyper-heuristic's performance study purely on the topic of heuristic sets is uncommon. Similarly, experimental limits are generally ignored when comparing hyper-heuristics. In order to demonstrate the effect of these generality related elements, nine heuristic sets with different improvement capabilities and sizes were generated for each of three target problem domains. These three problem domains are home care scheduling, nurse rostering and patient admission scheduling. Fourteen hyper-heuristics with varying intensification/diversification characteristics were analysed under various settings. Empirical results indicate that the performance of selection hyper-heuristics changes significantly under different experimental conditions.