A cardinality-constrained robust model for the assignment problem in Home Care services

Carello, Giuliana; Lanzarone, Ettore

doi:10.1016/j.ejor.2014.01.009

Cited by 121 publications

(69 citation statements)

References 24 publications

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“…For instance, one could wonder why we have assumed the negative binomial to model the holding times (see (4)). …”

Section: Discussionmentioning

confidence: 99%

“…Appropriate resource planning is thus fundamental for avoiding process inefficiencies, delays, and overloaded operators. In addition, many random events may affect the service delivery and mine the feasibility of plans; see [1,2,3,4]. As mentioned above, the most relevant randomness sources are patients' health conditions, which may determine a different demand for visits than the planned one, as reported in [5].…”

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confidence: 99%

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A Bayesian framework for describing and predicting the stochastic demand of home care patients

et al. 2014

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This is the author's final version of the contribution published as:Argiento, R.; Guglielmi, A.; Lanzarone, E.; Nawajah, I. planning decisions related to service delivery in the territory must be taken. With the goal of helping home care management to take robust decisions, in this paper we propose a Bayesian model for estimating and predicting both the demand for care and the history of health conditions for patients in the charge of a home care service. In particular, we jointly model the temporal evolution of patients' care profile and the weekly number of visits required to nurses, and use a Markov chain Monte Carlo algorithm to compute posterior inference and prediction. The model is applied to data of one of the largest Italian home care providers, obtaining small prediction errors.

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“…For instance, one could wonder why we have assumed the negative binomial to model the holding times (see (4)). …”

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

A Bayesian framework for describing and predicting the stochastic demand of home care patients

et al. 2014

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“…An example for a free of choice scheme is Germany. See Haase and Müller (2015) and Carello and Lanzarone (2014) for more details on the ensuing models and problems.…”

Section: Introductionmentioning

confidence: 99%

A multi‐period multi‐criteria districting problem applied to primary care scheme with gradual assignment

Yanık

Kalcsics

Nickel

et al. 2019

Int Trans Operational Res

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We present a modeling and optimization approach for the planning of primary healthcare services in order to efficiently direct patient admissions to general practitioners (GP) and to leverage the capacity of the healthcare system. We propose a multi‐period multi‐criteria districting model for the problem of designing GP districts in the presence of six criteria: workload balance, capacity, accessibility, compactness, income equity, and district similarity. We combine the last three criteria into a single objective function and formulate the problem as a mixed integer program with binary location decision variables and relaxed allocation decision variables for gradual assignments. To assess the usefulness of the model, we test it on real‐case scenarios in Istanbul, Turkey.

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“…The policy suggested by the authors permits planners to allocate each new patient with a minimum increment of the expected cost sustained by the health care provider. Nurse-to-patient assignment with respect to continuity of care was studied by Carello and Lanzarone [5]. The authors developed a cardinality-constrained robust approach to modify the deterministic assignment model with the capability to consider uncertainty in patient demands without the need of generating scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A large amount of work in the existing HHC service literature is about the assignment of patients to nurses, scheduling and route planning of nursing staff to minimize the total distance covered. Some authors have also discussed the districting based care strategy and continuity of care aspects [3][4][5], whereas other researchers focused on the quality of care and satisfaction level of patients and nurses. Much of the research is still limited to the development of scheduling and routing plans, where it is mandatory to assign all the available nurses to all the patients considered in the problem.…”

Section: Introductionmentioning

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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A cardinality-constrained robust model for the assignment problem in Home Care services

Cited by 121 publications

References 24 publications

A Bayesian framework for describing and predicting the stochastic demand of home care patients

A Bayesian framework for describing and predicting the stochastic demand of home care patients

A multi‐period multi‐criteria districting problem applied to primary care scheme with gradual assignment

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

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