A model-based evolutionary algorithm for home health care scheduling

Clapper, Yoram; Berkhout, Joost; Bekker, René; Moeke, Dennis

doi:10.1016/j.cor.2022.106081

Cited by 17 publications

(2 citation statements)

References 42 publications

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“…For instance, Decerle et al [19] and Grenouilleau et al [20] developed memetic algorithms, which involve genetic algorithms followed by a local search step, to address the problem. On the other hand, [21] introduced a modelbased evolutionary algorithm. Following a similar approach to the work by Di Gaspero and Urli [16], Grenouilleau et al [20] considered a multi-day horizon and aimed to balance caregivers' workload, including minimizing overtime.…”

Section: Related Workmentioning

confidence: 99%

Multi-Neighborhood Simulated Annealing for the Home Healthcare Routing and Scheduling Problem

Ceschia,

Gaspero,

Rosati

et al. 2024

Preprint

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Purpose: Over time, the focus on supportive and geriatric care has shifted from being predominantly provided in institutional settings like nursing or rest homes to be delivered within the homes of the patients. Trained caregivers now provide home healthcare services by visiting patients in their own homes and carrying out specific services based on each patient's individual needs before moving on to the next patient. Planning such a service involves considering the routing aspect and ensuring synchronization between services and designated time windows for patients. Methods: To solve the problem, we propose a local search approach that combines different neighborhood operators guided by the Simulated Annealing metaheuristic. Additionally, we introduce a realistic and diverse dataset and a robust and flexible file format based on JSON. This dataset and format have the potential to facilitate future comparisons and analyses. Results: Our study shows that by appropriately tuning our algorithm in a statistically rigorous manner, it outperforms existing methods on all benchmarks.

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Section: Related Workmentioning

confidence: 99%

Multi-Neighborhood Simulated Annealing for the Home Healthcare Routing and Scheduling Problem

Ceschia,

Gaspero,

Rosati

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…1) Initialization: Beginning with the initialization process, a population of possible solutions, each representing a potential regression model, should be established. In most cases, these solutions are shown as vectors of variables that describe the model structure [197].…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Artificial intelligence applications in solar energy

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.

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Home Healthcare Staffing, Routing, and Scheduling Problem With Multiple Shifts and Emergency Considerations

Algendi,

Urrutia,

Hvattum

et al. 2024

Networks

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Effective planning of human resources is critical in designing an efficient home healthcare system. In this study, we present a novel home healthcare staffing, routing, and scheduling problem inspired by a real‐world application. The proposed problem addresses a set of patients, with varying daily visit requirements, being served by a set of caregivers with different qualification levels over a multi‐day multi‐shift planning horizon. The problem aims to minimize the number of extra shifts for caregivers, maximize the allocation of caregivers to emergencies, and minimize the sum of route durations over the planning horizon. These objectives are optimized hierarchically while considering a set of restrictions, including time windows, skill matching, synchronization, care continuity, and labor regulations. To tackle the problem, we introduce a mixed‐integer linear programming model. The model is then extended and two sets of valid inequalities are incorporated to enhance its tightness. Computational experiments are conducted on a set of 20 instances. The results highlight the efficiency of the proposed extension in increasing both the number of instances that can be solved to optimality and the number of instances for which a feasible solution is found.

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A model-based evolutionary algorithm for home health care scheduling

Cited by 17 publications

References 42 publications

Multi-Neighborhood Simulated Annealing for the Home Healthcare Routing and Scheduling Problem

Multi-Neighborhood Simulated Annealing for the Home Healthcare Routing and Scheduling Problem

Artificial intelligence applications in solar energy

Home Healthcare Staffing, Routing, and Scheduling Problem With Multiple Shifts and Emergency Considerations

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