Net utility maximization based scheduling model

AbstractThis paper investigates a financial market where stock returns depend on an unobservable Gaussian mean reverting drift process. Information on the drift is obtained from returns and randomly arriving discrete-time expert opinions. Drift estimates are based on Kalman filter techniques. We study the asymptotic behavior of the filter for high-frequency experts with variances that grow linearly with the arrival intensity. The derived limit theorems state that the information provided by discrete-time expert opinions is asymptotically the same as that from observing a certain diffusion process. These diffusion approximations are extremely helpful for deriving simplified approximate solutions of utility maximization problems.

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A novel macro and micro scheduling model for regulated safety inspections based on Probability Integral Transform and modified Bin Packing algorithm

IEEE Access ◽

10.1109/access.2021.3083214 ◽

2021 ◽

pp. 1-1

Author(s):

Mohamed A. Zytoon

Keyword(s):

Bin Packing ◽

Integral Transform ◽

Scheduling Model ◽

Packing Algorithm ◽

Probability Integral Transform ◽

Probability Integral ◽

Safety Inspections

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Operating Rooms efficiency: leveraging data to improve management of elective surgery

European Journal of Public Health ◽

10.1093/eurpub/ckaa166.528 ◽

2020 ◽

Vol 30 (Supplement_5) ◽

Author(s):

C Quercioli ◽

G A Carta ◽

G Cevenini ◽

G Messina ◽

N Nante ◽

...

Keyword(s):

Historical Data ◽

Elective Surgery ◽

District General Hospital ◽

Operating Rooms ◽

Accurate Estimation ◽

Surgical Time ◽

Accurate Analysis ◽

Management Efficiency ◽

Scheduling Model ◽

The Mean

Abstract Background Careful scheduling of elective surgery Operating Rooms (ORs) is crucial for their efficient use, to avoid low/over utilization and staff overtime. Accurate estimation of procedures duration is essential to improve ORs scheduling. Therefore analysis of historical data about surgical times is fundamental to ORs management. We analyzed the effect, in a real setting, of an ORs scheduling model based on estimated optimum surgical time in improving ORs efficiency and decreasing the risk of overtime. Methods We studied all the 2014-2019 elective surgery sessions (3,758 sessions, 12,449 interventions) of a district general hospital in Siena's Province, Italy. The hospital had3 ORs open 5 days/week 08:00-14:00. Surgery specialties were general surgery, orthopedics, gynecology and urology. Based on a pilot study conducted in 2016, which estimated a 5 times greater risk of having an OR overtime for sessions with a surgical time (incision-suture)>200 minutes, from 2017 all the ORs were scheduled using a maximum surgical time of 200 minutes calculated summing the mean surgical times for intervention and surgeon (obtained from 2014-2016 data). We carried out multivariate logistic regression to calculate the probability of ORs overtime (of 15 and 30 minutes) for the periods 2014-2016 and 2017-2019adjusting for raw ORs utilization. Results The 2017-2019 risk of an OR overtime of 15 minutes decreased by 25% compared to the 2014-2016 period (OR = 0.75, 95%CI=0.618-0.902, p = 0.003); the risk of a OR overtime of 30 minutes decreased by 33% (OR = 0.67, 95%CI= 0.543-0.831, p < 0.001). Mean raw OR utilization increase from 62% to 66% (p < 0.001). Mean number of interventions per surgery sessions increased from 3.1 to 3.5 (p < 0.001). Conclusions This study has shown that an analysis of historical data and an estimate of the optimal surgical time per surgical session could be helpful to avoid both a low and excessive use of the ORs and therefore to increase the efficiency of the ORs. Key messages An accurate analysis of surgical procedures duration is crucial to optimize operating room utilization. A data-based approach can improve OR management efficiency without extra resources.

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