Mingda Liu
Continuity-skill-restricted Scheduling and Routing Problem: Formulation, Optimization and Implications
Liu, Mingda; Zhao, Yanlu; Xie, Xiaolei
Abstract
As the aging population grows, the demand for long-term continuously attended home healthcare (AHH) services has increased significantly in recent years. AHH services are beneficial since they not only alleviate the pressure on hospital resources, but also provide more convenient care for patients. However, how to reasonably assign patients to doctors and arrange their visiting sequences is still a challenging task due to various complex factors such as heterogeneous doctors, skill-matching requirements, continuity of care, and uncertain travel and service times. Motivated by a practical problem faced by an AHH service provider, we investigate a deterministic continuity-skill-restricted scheduling and routing problem (CSRP) and its stochastic variant (SCSRP) to address these operational challenges. The problem is formulated as a heterogeneous site-dependent and consistent vehicle routing problem with time windows. Yet there is not a compact model and a practically implementable exact algorithm in the literature to solve such a complicated problem. To fill this gap, we propose a branch-price-and-cut algorithm to solve the CSRP and a discrete-approximation-method adaption for the SCSRP. Extensive numerical experiments and a real case study verify the effectiveness and efficiency of the proposed algorithms and provide managerial insights for AHH service providers to achieve better performance.
Citation
Liu, M., Zhao, Y., & Xie, X. (2024). Continuity-skill-restricted Scheduling and Routing Problem: Formulation, Optimization and Implications. IISE Transactions, 56(2), 201-220. https://doi.org/10.1080/24725854.2023.2215843
Journal Article Type | Article |
---|---|
Acceptance Date | May 11, 2023 |
Online Publication Date | Jun 28, 2023 |
Publication Date | 2024 |
Deposit Date | May 16, 2023 |
Publicly Available Date | Jul 27, 2023 |
Journal | IISE Transactions |
Print ISSN | 2472-5854 |
Electronic ISSN | 2472-5862 |
Publisher | Taylor and Francis Group |
Peer Reviewed | Peer Reviewed |
Volume | 56 |
Issue | 2 |
Pages | 201-220 |
DOI | https://doi.org/10.1080/24725854.2023.2215843 |
Public URL | https://durham-repository.worktribe.com/output/1173887 |
Publisher URL | https://www.tandfonline.com/journals/uiie21 |
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Copyright Statement
Copyright © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
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