The implementor/adversary algorithm for the cyclic and robust scheduling problem in health-care

Holte, Matias; Mannino, Carlo

doi:10.1016/j.ejor.2012.10.029

Cited by 42 publications

(15 citation statements)

References 19 publications

(53 reference statements)

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“…( 2017 ). An implementor/adversary algorithm is used also in Holte and Mannino ( 2013 ) for a cyclic robust scheduling problem arising in health care. Moreover, a real-time scheduling model is proposed in Du et al.…”

Section: Literature Review: Uncertainty and Consistency In Home Carementioning

confidence: 99%

Addressing consistency and demand uncertainty in the Home Care planning problem

Cappanera

Scutellà

2021

Flex Serv Manuf J

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Optimizing Home Care Services is receiving a great attention in Operations Research. We address arrival time consistency, person-oriented consistency and demand uncertainty in Home Care, while jointly optimizing assignment, scheduling and routing decisions over a multiple-day time horizon. Consistent time schedules are very much appreciated by patients who, in this setting, are very sensitive to changes in their daily routines. Also person-oriented consistency positively impacts on service quality, guaranteeing that almost the same set of caregivers take care of a patient in the planning horizon. Demand uncertainty plays a pivotal role, too, since both the set of patients under treatment and their care plan can change over time. To the best of our knowledge, this is the first paper dealing with all these aspects in Home Care via a robust approach. We present a mathematical model to the problem, and a pattern-based algorithmic framework to solve it. The framework is derived from the model via decomposition, i.e. suitably fixing the scheduling decisions through the concept of pattern. We propose alternative policies to generate patterns, taking into account consistency and demand uncertainty; when embedding them in the general framework, alternative pattern based algorithms originate. The results of a rich computational experience show that introducing consistency and demand uncertainty in pattern generation policies is crucial to efficiently compute very good quality solutions, in terms of robustness and balancing of the caregiver workload. In addition, a comparison with a simpler model, where no kind of consistency is imposed, shows the importance of considering consistency in pursuing a valuable patient-centered perspective, with a positive effect also on the efficiency of the solution approach.

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Section: Literature Review: Uncertainty and Consistency In Home Carementioning

confidence: 99%

Addressing consistency and demand uncertainty in the Home Care planning problem

Cappanera

Scutellà

2021

Flex Serv Manuf J

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“…Traditionally, the efficiency and the efficacy of robust approaches are investigated via the so-called "Price of Robustness". That is, by estimating the price that we are willing to pay to guarantee robustness, using robust solutions instead of the "cheaper" nominal ones [19], which may become unfeasible when some uncertain events realize. In the Home Care context under study, instead, the situation is radically different.…”

Section: Computational Experimentsmentioning

confidence: 99%

Demand uncertainty in robust Home Care optimization

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Scutellà

Nervi

et al. 2018

Omega

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We study the Home Care Problem under uncertainty. Home Care refers to medical, paramedical and social services that may be delivered to patient homes. The term includes several aspects involved in the planning of home care services, such as caregiver-to-patient assignment, scheduling of patient requests, and caregiver routing. In Home Care, cancellation of requests and additional demand for known or new patients are very frequent. Thus, managing demand uncertainty is of paramount importance in limiting service disruptions that might occur when such events realize. We address uncertainty of patient demand over a multiple-day time horizon, when assignment, scheduling and routing decisions are taken jointly, both from a methodological and a computational perspective. In fact, we propose a non-standard cardinality-constrained robust approach, analyse its properties, and report the results of a wide experimentation on real-life instances. The obtained results show that, for instances of moderate size, the approach is able to efficiently determine robust solutions of good quality in terms of balancing among caregivers and number of uncertain requests that can be managed. Also, the robustness of the solutions with respect to possible realizations of uncertain requests, evaluated on a small subset of instances, appears to be significant. Furthermore, preliminary experiments on a decomposition method, obtained from the robust one by fixing the scheduling decisions, show a drastic gain in computational efficiency, with the determination of robust solutions of still good quality. Therefore, the approach appears to be very promising to cope with robustness even on Home Care instances of larger size

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“…In addition, some researchers have considered the uncertainty of the parameters in MSS problem. This includes uncertainty on Operating Time (OT) and Length Of Stay (LOS) in post-surgery units [12,15,[17][18][19]. Furthermore, various performance measures have been used to evaluate the performance of MSS solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

Master Surgical Scheduling Problem with Multiple Criteria and Robust Estimation

Shafaei

Mozdgir

2018

Scientia Iranica

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In this research, the Master Surgical Scheduling (MSS) problem at the tactical level of hospital planning and scheduling is studied. Before constructing the MSS, a strategic level problem, i.e., Case Mix Planning Problem (CMPP), shall be solved to allocate the capacity of Operating Room (OR) to each surgical specialty. In order to make an e ective coordination between CMPP and MSS, the results obtained from solving the CMPP are used as an input for the respective MSS. In the MSS, frequently performed elective surgeries are planned in a cyclic manner for a pre-de ned planning period. As a part of the planning process, it is required to adjust downstream limited resources, such as Intensive Care Unit (ICU) and ward beds, with patient ow. In this study, a mathematical model is developed to construct an MSS. The proposed model is based on a lexicographic goal programming approach, which is aimed at minimizing the OR spare time while considering the results of the CMPP. In this paper, the data required to solve MSS are collected from a medium-sized Iranian hospital. Hence, a robust estimation method is applied to reduce the e ect of outliers on the decision-making process. The results show the performance of the proposed method against the solution put in practice in the hospital.

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The implementor/adversary algorithm for the cyclic and robust scheduling problem in health-care

Cited by 42 publications

References 19 publications

Addressing consistency and demand uncertainty in the Home Care planning problem

Addressing consistency and demand uncertainty in the Home Care planning problem

Demand uncertainty in robust Home Care optimization

Master Surgical Scheduling Problem with Multiple Criteria and Robust Estimation

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