Combining workload balance and patient priority maximisation in operating room planning through hierarchical multi-objective optimisation

Aringhieri, Roberto; Duma, Davide; Landa, Paolo; Mancini, Susanna

doi:10.1016/j.ejor.2021.07.033

Cited by 24 publications

(21 citation statements)

References 59 publications

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“…A surgery group is a cluster of patients grouped based on their similar characteristics [54]. We identified that the most common type of surgery group is the medical specialities associated with the surgeries used in 19 papers [14,25,[49][50][51][52][53][55][56][57][58][59][60][61][62][63][64]. In contrast, other researchers assigned surgeons, which enabled them to consider surgeons' availability and preferences [50,[65][66][67][68][69][70][71].…”

Section: Types Of Surgery Groupmentioning

confidence: 99%

“…MIP find excellent solution with high computational time, DPHs and ILS find high-quality solutions in short time. [56] Hierarchical multi-objective optimization model, multineighbourhood local search-based matheuristic and generated new data instances.…”

Section: Types Of Surgery Groupmentioning

confidence: 99%

“…Maximize scheduled patients' score [52,56,60] Minimize clinical deterioration [62] OT Utilization Minimize the difference between OT time allocated and demand/target value (match demand) [49,61,63]…”

Section: Types Objective Function Referencesmentioning

confidence: 99%

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Research Trends in the Optimization of the Master Surgery Scheduling Problem

et al. 2022

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The Master Surgery Scheduling Problem (MSSP) allocates operating theatre time to surgery groups such as medical specialities or surgeons, which is essential for daily operational planners. Many researchers have highlighted issues in the optimization of surgical scheduling problems. However, most recent reviews limited the issues at the operational level and excluded the problem characteristics such as surgery group type and schedule cyclicity. This study aims to review the state-of-the-art of MSSP and identify new trends in optimization strategies (problem characteristics and objective function), uncertainty scheduling factors (types and approaches), and solution and evaluation methods. These aspects are the key components in developing an effective MSSP optimization model. This paper reviews articles published between 2000 and 2021 that addressed MSSP, concentrating on papers between 2016 and 2021. We underlined the popularity of medical specialities as the surgery group, one-week horizon length, a cyclic schedule, multi-objective optimization and evaluation by benchmarking. The analysis shows that surgery duration is the most prominent uncertainty type, while strategies for handling this are stochastic programming, robust optimization, and fuzzy programming. We highlighted the role of heuristic approaches in addressing the MSSP's computational complexity. This review's trends, challenges, and potential solutions are essential for future researchers in developing optimization models for MSSP.

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Section: Types Of Surgery Groupmentioning

confidence: 99%

Section: Types Of Surgery Groupmentioning

confidence: 99%

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Research Trends in the Optimization of the Master Surgery Scheduling Problem

et al. 2022

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“…(2018b). For one of the approaching methods to minimize the access to admission control, readers may refer to bed leveling (Beliën and Demeulemeester, 2008; Yip et al., 2018; Aringhieri et al., 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Recent advances in patient admission modeling have also been made by Alagoz et al (2015), Yang et al (2015), and Landa et al (2018b). For one of the approaching methods to minimize the access to admission control, readers may refer to bed leveling (Beliën and Demeulemeester, 2008;Yip et al, 2018;Aringhieri et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal admission control under premature discharge decisions for operational effectiveness

Yang

Jiang

Tang

2022

Int Trans Operational Res

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The shortage of inpatient beds causes surgery cancellations, treatment delays, and inappropriate ward arrangements. That is why studying how to make admission decisions on the wards of crowded units under premature discharge decisions is essential. In this study, we set up an infinite-horizon total discounted cost Markov decision process model to minimize the total expected cost to the hospital. By analyzing the structural properties of the optimal value function, we find that there is an optimal admission control policy of the threshold type for low-acuity emergency patients (type-2). Numerical experiments are done to examine how optimal dynamic policy depends on key system parameters. Furthermore, we evaluate the performance of the optimal dynamic policy with a benchmark policy without premature discharge decisions. The results indicate that the optimal admission control policy helps hospitals in maintaining low costs and enables patients with high-acuity diseases to enter primary wards more quickly.

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