Column-generation-based heuristic approaches to stochastic surgery scheduling with downstream capacity constraints

Zhang, Jian; Dridi, Mahjoub; Moudni, Abdellah El

doi:10.1016/j.ijpe.2020.107764

Cited by 29 publications

(17 citation statements)

References 46 publications

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“…Fügener et al published a tutorial with an example from a surgical suite [20]. Some contemporary studies treat each patient as having a length of stay equal to the mean for their procedure category (i.e., assuming perfect predictive ability) [21][22][23][24][25]. Others use discrete empirical probability distributions, statistically analogous to the Wilcoxon-Mann-Whitney approach [20,[26][27][28][29].…”

Section: Discussionmentioning

confidence: 99%

Proportions of Surgical Patients Discharged Home the Same or the Next Day Are Sufficient Data to Assess Cases’ Contributions to Hospital Occupancy

Dexter¹,

Epstein²,

Shi³

2021

Cureus

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Introduction When the hospital census is high, perioperative medical directors or operating room (OR) managers may need to consider postponing some surgical cases scheduled to be performed within the next three workdays. This scenario has arisen at hospitals in regions with large increases in admissions due to coronavirus disease 2019 (COVID-19). We compare summary measures for hospital length of stay (LOS) to guide the OR manager having to decide which cases may need to be postponed to ensure a sufficient reserve of available inpatient beds. Methods We studied the 1,201,815 ambulatory and 649,962 inpatient elective cases with a major therapeutic procedure performed during 2018 at all 412 non-federal hospitals in Florida. The data were sorted by the hospital, and then by procedure category. Statistical comparisons of LOS were made pairwise among all procedure categories with at least 100 cases at (the) each hospital, using the chi-square test (LOS ≤ 1 day versus LOS > 1 day), Student's t-test with unequal variances, and the Wilcoxon-Mann-Whitney test. The comparisons among the three tests then were repeated having sorted the data by procedure category and making statistical comparisons among all hospitals with at least 100 cases for the procedure category. Results Whether using a criterion for statistical significance of P < 0.05 or P < 0.01, and whether compared with Student's t-test with unequal variances or Wilcoxon-Mann-Whitney test, the chi-square test had greater odds (i.e., greater statistical power) to detect differences in LOS (all four with P < 0.0001 and all 95% lower confidence limits for odds ratios ≥ 3.00). The findings were consistent when the data, first sorted by procedure category and then by probability distributions of LOS, were compared between hospitals (all P < 0.0001 and the 95% lower confidence limits for odds ratio ≥ 3.72). Conclusions For purposes of comparing procedure categories pairwise at the same hospital, there was no loss of information by summarizing the probability distributions using single numbers, the percentages of cases among patients staying longer than overnight. This finding substantially simplifies the mathematics for constructing dashboards or summaries of OR information system data to help the perioperative OR manager or medical director decide which cases may need to be postponed, when the hospital census is high, to provide a sufficient reserve of inpatient hospital beds.

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Section: Discussionmentioning

confidence: 99%

Proportions of Surgical Patients Discharged Home the Same or the Next Day Are Sufficient Data to Assess Cases’ Contributions to Hospital Occupancy

Dexter¹,

Epstein²,

Shi³

2021

Cureus

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“…While ORs are considered to be the most important surgical resources and their utilizations are optimized in almost all the relevant works, recently, more and more researchers take the supporting facilities of surgical activities into consideration and address multi-resource patient admission control and surgery scheduling problems (e.g., Min & Yih, 2010b;Gocgun & Ghate, 2012;Huh et al, 2013;Astaraky & Patrick, 2015;Truong, 2015;Jebali & Diabat, 2015Samudra et al, 2016;Neyshabouri & Berg, 2017;J. Zhang et al, , 2020.…”

Section: Patient Admission Controlmentioning

confidence: 99%

An approximate dynamic programming approach to the admission control of elective patients

Zhang,

Dridi,

Moudni

2020

Preprint

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In this paper, we propose an approximate dynamic programming algorithm to solve a Markov decision process (MDP) formulation for the admission control of elective patients. To manage elective patients from multiple specialties equitably and efficiently, we establish a waiting list of patients and assign each patient a time-dependent dynamic priority. Then taking the random arrivals of patients into account, sequential decisions are made on a weekly basis. At the end of each week, we select the patients to be treated in the following week from the waiting list. By minimizing the cost function of MDP over an infinite horizon, we seek to achieve the best trade-off between the patients' waiting time and the over-utilization of operating rooms and downstream resources. Considering the curses of dimensionality resulting from the large scale of realistically sized problems, we first analyze the structural properties of the MDP model and propose an algorithm that facilitates the search for greedy actions. We then develop a novel approximate dynamic programming algorithm based on recursive least-squares temporal difference learning as the solution technique. Experimental results reveal that the proposed algorithms consume much less computation time in comparison with that required by conventional dynamic programming methods. Additionally, the algorithms are shown to be capable of computing high-quality near-optimal policies for realistically sized problems.

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“…They formulated the decision issues into nonlinear programs and deployed various reformulation-linearization techniques to form three mathematical models. Zhang, Dridi, and El Moudni [22] also considered the capacity constraints of the downstream intensive care unit. Uncertainty of surgical and recovery durations render a stochastic programming model, which was then transformed into a deterministic one and handled by a column generation approach.…”

Section: Introductionmentioning

confidence: 99%

Scheduling of Anaesthesia Operations in Operating Rooms

Hsu

Hwang

et al. 2021

Healthcare

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This paper considers scheduling of surgical operations across multiple operating rooms subject to the limited availability of anaesthetists. The objective is to construct a feasible operations schedule that has the minimum makespan, i.e., the completion time of all operations. We abstract the problem into a theoretical server scheduling problem and formulate it in a mathematical form by proposing an integer programming model. Due to the intractability of its computing time, we circumvent the exact approaches and develop two approximation methods. Then, the steepest descent search is adopted for improving the solutions. Computational study suggests that the proposed methods can produce quality solutions in a few seconds.

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Column-generation-based heuristic approaches to stochastic surgery scheduling with downstream capacity constraints

Cited by 29 publications

References 46 publications

Proportions of Surgical Patients Discharged Home the Same or the Next Day Are Sufficient Data to Assess Cases’ Contributions to Hospital Occupancy

Proportions of Surgical Patients Discharged Home the Same or the Next Day Are Sufficient Data to Assess Cases’ Contributions to Hospital Occupancy

An approximate dynamic programming approach to the admission control of elective patients

Scheduling of Anaesthesia Operations in Operating Rooms

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