Home health-care network design: Location and configuration of home health-care centers

Rodriguez-Verjan, Carlos; Augusto, Vincent; Xie, Xiaolan

doi:10.1016/j.orhc.2017.09.001

Cited by 20 publications

(6 citation statements)

References 23 publications

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“…The authors combined constraint programming with different metaheuristics to treat realistic constraints with flexibility. Rodriguez-Verjan et al [21] proposed two MILP models considering strategic and tactical decisions for an HHC network design problem. The first model determines a strategic location plan for HHC centers while the second model covers a tactical management plan for each HHC center.…”

Section: Literature Reviewmentioning

confidence: 99%

Solving a More Flexible Home Health Care Scheduling and Routing Problem with Joint Patient and Nursing Staff Selection

Nasir

Dang

2018

Sustainability

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Development of an efficient and effective home health care (HHC) service system is a quite recent and challenging task for the HHC firms. This paper aims to develop an HHC service system in the perspective of long-term economic sustainability as well as operational efficiency. A more flexible mixed-integer linear programming (MILP) model is formulated by incorporating the dynamic arrival and departure of patients along with the selection of new patients and nursing staff. An integrated model is proposed that jointly addresses: (i) patient selection; (ii) nurse hiring; (iii) nurse to patient assignment; and (iv) scheduling and routing decisions in a daily HHC planning problem. The proposed model extends the HHC problem from conventional scheduling and routing issues to demand and capacity management aspects. It enables an HHC firm to solve the daily scheduling and routing problem considering existing patients and nursing staff in combination with the simultaneous selection of new patients and nurses, and optimizing the existing routes by including new patients and nurses. The model considers planning issues related to compatibility, time restrictions, contract durations, idle time and workload balance. Two heuristic methods are proposed to solve the model by exploiting the variable neighborhood search (VNS) approach. Results obtained from the heuristic methods are compared with a CPLEX based solution. Numerical experiments performed on different data sets, show the efficiency and effectiveness of the solution methods to handle the considered problem.

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Section: Literature Reviewmentioning

confidence: 99%

Solving a More Flexible Home Health Care Scheduling and Routing Problem with Joint Patient and Nursing Staff Selection

Nasir

Dang

2018

Sustainability

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“…In traditional settings, the medical is provided in medical facilities and the patient has to move from home to hospital and/or remain there to be monitored constantly. Boosted by an insufficient capacity of the medical system and by the recent technological advances of portable medical equipment, the idea of moving medical staff from health-care facilities to those persons who need assistance has raised a growing interest (Cotta et al , 2001; Rodriguez-Verjan et al , 2018). These services are known as home health care delivery (HHD).…”

Section: Introductionmentioning

confidence: 99%

A network flow-based model for operations planning in home health care delivery

Castaño

Velasco

2020

IJLM

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PurposeTo solve the problem, a mathematical model is proposed; it relies on a directed acyclic graph (DAG), in which arcs are used to indicate whether a pair of appointments can be assigned to the same route or not (and so to the same care worker). The proposed model aims at minimizing the personnel required to meet daily demand and balancing workloads among the workers while considering the varying traffic patterns derived from traffic congestion.Design/methodology/approachThis paper aims at providing solution approaches for addressing the problem of assigning care workers to deliver home health-care (HHC) services, demanding different skills each. First, a capacity planning problem is considered, where it is necessary to define the number of workers required to satisfy patients' requests and then, patients are assigned to the care workers along with the sequence followed to visit them, thus solving a scheduling problem. The benefits obtained by permitting patients to propose multiple time slots where they can be served are also explored.FindingsThe results indicate that the problem can be efficiently solved for medium-sized instances, that is, up to 100 daily patient requests. It is also indicated that asking patients to propose several moments when they can receive services helps to minimize the need for care workers through more efficient route allocations without affecting significantly the balance of the workloads.Originality/valueThis article provides a new framework for modeling and solving a HHC routing problem with multiskilled personnel. The proposed model can be used to identify efficient daily plans and can handle realistic characteristics such as time-dependent travel times or be extended to other real-life applications such as maintenance scheduling problems.

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“…. Each of these instances is characterized by the number of service types (L ∈ {4, 6} as inMankowska et al (2014);Rodriguez et al (2015);Rodriguez-Verjan et al (2018); Zheng et al (2021)), caregivers types (K ∈ {4, 6, 8}), days in the planning horizon T ∈ {30, 90, 180}. We summarize these instances in TableH.1 in Appendix H.We set the caregivers' standard work hours h k to 480 minutes (i.e., 8 hours, see, e.g.,Zhan & Wan (2018)).…”

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confidence: 99%

“…We summarize these instances in TableH.1 in Appendix H.We set the caregivers' standard work hours h k to 480 minutes (i.e., 8 hours, see, e.g.,Zhan & Wan (2018)). We generate the monthly fixed cost for employing one full-time caregivers, c k , from U [3000, 6000](Rodriguez-Verjan et al, 2018). We generate the quarterly and semiannual cost, c k , from U [9000, 18000] and U [18000, 36000], respectively.…”

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confidence: 99%

Integrated Home Care Staffing and Capacity Planning: Stochastic Optimization Approaches

Wang¹,

Shehadeh²,

Xie³

et al. 2022

Preprint

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We propose stochastic optimization methodologies for a staffing and capacity planning problem arising from home care practice. Specifically, we consider the perspective of a home care agency that must decide the number of caregivers to hire (staffing) and the allocation of hired caregivers to different types of services (capacity planning) in each day within a specified planning horizon. The objective is to minimize the total cost associated with staffing (i.e., employment), capacity allocation, over-staffing, and under-staffing. We propose two-stage stochastic programming (SP) and distributionally robust optimization (DRO) approaches to model and solve this problem considering two types of decision-makers, namely an everything in advance decision-maker (EA) and a flexible adjustment decision-maker (FA). In the EA models, we determine the staffing and capacity allocation decisions in the first stage before observing the demand. In the FA models, we decide the staffing decisions in the first stage. Then, we determine the capacity allocation decisions based on demand realizations in the second stage.We derive equivalent mixed-integer linear programming (MILP) reformulations of the proposed nonlinear DRO model for the EA decision-maker that can be implemented and efficiently solved using off-the-shelf optimization software. We propose a computationally efficient column-andconstraint generation algorithm with valid inequalities to solve the proposed DRO model for the FA decision-maker. Finally, we conduct extensive numerical experiments comparing the operational and computational performance of the proposed approaches and discuss insights and implications for home care staffing and capacity planning.

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Home health-care network design: Location and configuration of home health-care centers

Cited by 20 publications

References 23 publications

Solving a More Flexible Home Health Care Scheduling and Routing Problem with Joint Patient and Nursing Staff Selection

Solving a More Flexible Home Health Care Scheduling and Routing Problem with Joint Patient and Nursing Staff Selection

A network flow-based model for operations planning in home health care delivery

Integrated Home Care Staffing and Capacity Planning: Stochastic Optimization Approaches

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