Scheduled Block Time Research Articles

Socioeconomic factors influence surgical wait times for non-emergent gynecologic surgical procedures: a retrospective analysis

BackgroundIn various disciplines, an association between surgical wait times and patient outcomes has been identified. This study is among the first to investigate whether practice setting influences wait times for elective surgeries in benign gynecology.MethodsThis retrospective study of patients at three New York hospitals from 10/2019–2/2020 compared surgical wait times among patients seen in federally-qualified health centers (FQHC’s) and private practice settings. Emergent surgeries, oncology cases, abortions, urogynecology procedures, and cases concurrently booked with another specialty were excluded. Surgical wait time was defined as the time (days) from the decision to operate to the day of the procedure. A multivariable mixed model was used to model surgical wait time by setting of care, adjusting for age, BMI, race, ethnicity, insurance, need for medical clearance, and scheduled block time. A univariable analysis was then utilized to assess surgical wait times by clinical setting for each insurance type.ResultsFive hundred forty patients were identified with a median age of 45.6 years (range 16–87). Average surgical wait time was 27 days (range 1–288 days). In multivariable analysis, longer surgical wait times were associated with being seen preoperatively in a FQHC compared to the private practice setting (102% longer, 59.5 days vs. 22 days, p < 0.0001), and with needing medical clearance (56.4% longer, 45 days vs. 22 days. p = 0.0001).ConclusionsThese results suggest that in benign gynecology, surgical wait times are significantly influenced by the practice setting in which a patient gets care, with notable delays in care among patients who are seen in a federally qualified health center preoperatively.

Schedule-level optimization of flight block times for improved airline schedule planning: A data-driven approach

The development of flight block time schedules for future airline operations is a critical aspect of the schedule development process. It involves determining the appropriate gate-to-gate time for each flight to optimize on-time performance and ensure efficient utilization of flight resources. This article presents a modeling framework that addresses the airlines' scheduled block time (SBT) design problem. The framework employs a novel data-driven optimization model that incorporates reliability measures at both the flight and schedule levels. This approach enables airline schedule planners to assess the relationship between the allocated SBT for each flight and the overall schedule reliability, and make informed decisions regarding the trade-off between schedule reliability and operational costs. Experiments show that the proposed methodology outperforms real-world SBT plans implemented by two major U.S. airlines, demonstrating its superiority. The results of this comparison indicate that the developed framework could assist airlines in improving reliability while deploying the same block time budget or in eliminating unnecessary slack in their schedules while achieving their targeted reliability, resulting in significant cost savings.

The limitation of machine-learning based models in predicting airline flight block time

This study presents three different machine learning (ML) models to estimate the flight block time for commercial airlines. The models rely only on explanatory variables that airlines would know when they were planning their schedules several months prior to the actual flight. Historical Actual Block Time (ABT) data is collected for one-way flights in seven airport pairs in the domestic U.S. market for 2019. The variability of the ABT and its components for these airport pairs is presented. The main features that affect this variability are investigated. The results confirm that seasonality, aircraft type, departure/arrival hour, and airport congestion are significant variables in partially explaining ABT variations. Overall, the results show that the considered ML models have limited capability in predicting the ABT. The prediction accuracy of these ML models is compared against a benchmarking scenario, where the median value of the historical ABT is used as an estimate for the block time. It is found that the median-based approach provides better performance compared to the ML models. A sensitivity analysis is performed to evaluate the risk of flight delay against different levels of block time padding. The possible trade-off between the block time padding and the expected flight delays is presented. Finally, the study evaluates and compares different airlines' block time padding strategies in the different airport pairs. Results show significant discrepancies among airlines concerning setting the scheduled block time (SBT) in the same airport pair. However, it is difficult to confirm whether airlines are purposely adopting padding strategies, or they lack the ability to optimize their block time padding against expected on-time performance.

The turnaround tactic and on-time performance: Implications for airlines' efficiency

Schedule padding is a frequent strategy applied by transport providers, often used to improve their on-time performance. Normally, time efficiency is evaluated in terms of arrival delays. However, in the air transport industry, evidence shows that flights tend to depart late, although arriving on time. This study investigates the carriers' turnaround time strategy by comparing the scheduled and actual daily movements of all flights from or to Europe in 2019. Results show that turnaround variations are more frequent in larger airports, during peak hours, and when an aircraft performs a higher number of flights. Low-cost carriers usually set a significantly shorter turnaround time than traditional carriers, using a larger part of the scheduled block time for ground operations. This strategy greatly influences on-time performance measures and other efficiency indicators traditionally based on scheduled activities, with important implications for both airlines and passengers.

Influence of Socioeconomic Factors on Surgical Wait Times for Elective Surgeries in Benign Gynecology [A81

INTRODUCTION: In various disciplines, an association between patient outcomes and surgical wait times has been identified. This study provides the first investigation into whether practice setting influences wait times for elective surgeries in benign gynecology. METHODS: This retrospective study of patients at three Manhattan hospitals from October 2019 to February 2020 compared surgical wait times among patients seen in federally qualified health centers (FQHC) and private practice settings. Emergent surgeries, oncology cases, abortions, urogynecology procedures, and cases concurrently booked with another specialty were excluded. Surgical wait time was defined as the time (days) from the decision to operate to the day of procedure. A multivariable mixed model was used to model the log of surgical wait time by setting of care, adjusting for age, body mass index (BMI), race/ethnicity, insurance, need for medical clearance, and scheduled block time. RESULTS: A total of 540 patients were identified with a median age of 45.6 years (range 16-87 years). Average surgical wait time was 27 days (range 1-288 days). In multivariate analysis, longer surgical wait times were associated with the FQHC setting relative to private practice (70.9% longer, P=.006), and with needing medical clearance (36.8% longer, P=.02). Insurance type and race/ethnicity did not significantly impact surgical wait times in multivariate analysis. In univariate analysis, patients with public insurance had longer wait times compared with patients with commercial insurance (25% longer, P=.03). CONCLUSION: These results suggest that in benign gynecology, surgical wait times are significantly influenced by practice setting. Further research should investigate the reasons behind these preoperative delays with the goal of correcting inequities inherent in the medical system.

Examination on the setting of scheduled block time in China's aviation framework dependent on quantile regression model

Examination on the setting of scheduled block time in China's aviation framework dependent on quantile regression model

An Improved Whale Algorithm for Setting Standard Scheduled Block Time Based on the Airline Fairness

Standard scheduled flight block time (SBT) setting is of great concern for Civil Aviation Administration of China (CAAC) and airlines in China. However, the standard scheduled flight block times are set in the form of on-site meetings in practice and current literature has not provided any efficient mathematical models to calculate the flight block times fairly among the airlines. The objective of this paper is to develop and solve a mathematical model for standard SBT setting with consideration of both fairness and reliability. We use whale optimization algorithm (WOA) and an improved version of the whale optimization algorithm (IWOA) to solve the SBT setting problem. A novel nonlinear update equation of convergence factor for random iterations is used in place of the original linear one in the proposed IWOA algorithm. Experimental results show that the suggested approach is effective, and IWOA performs better than WOA in the concerned problem, whose solutions are better compared to the flight block times released by CAAC. In particular, it is interesting to find that MSE, RMSE, MAE, MAPE and Theil of the reliability in 60%–70% range are always the smallest and the average fairness of airlines is better than that of 60%–75% range. The model and solving approach presented in this article have great potential to be applied by CAAC to determine the standard SBTs strategically.

How Fast You Can Actually Fly: A Comparative Investigation of Flight Airborne Time in China and the U.S.

Actual airborne time (AAT) is the time between wheels-off and wheels-on of a flight. Understanding the behavior of AAT is increasingly important given the ever growing demand for air travel and flight delays becoming more rampant. As no research on AAT exists, this paper performs the first empirical analysis of AAT behavior, comparatively for the U.S. and China. The focus is on how AAT is affected by scheduled block time (SBT), origin-destination (OD) distance, and the possible pressure to reduce AAT from other parts of flight operations. Multiple econometric models are developed. The estimation results show that in both countries AAT is highly correlated with SBT and OD distance. Flights in the U.S. are faster than in China. On the other hand, facing ground delay prior to takeoff, a flight has limited capability to speed up. The pressure from short turnaround time after landing to reduce AAT is immaterial. Sensitivity analysis of AAT to flight length and aircraft utilization is further conducted. Given the more abundant airspace, flexible routing networks, and efficient ATFM procedures, a counterfactual that the AAT behavior in the U.S. were adopted in China is examined. We find that by doing so significant efficiency gains could be achieved in the Chinese air traffic system. On average, 11.8 minutes of AAT per flight would be saved, coming from both reduction in SBT and reduction in AAT relative to the new SBT. Systemwide fuel saving would amount to over 300 million gallons with direct airline operating cost saving of nearly $1.3 billion nationwide in 2016.

Scheduled block time setting and on-time performance of U.S. and Chinese airlines—A comparative analysis

Scheduled block time (SBT) is the duration between scheduled departure time and scheduled arrival time of a given flight. SBTs have a significant impact on airline operations and performance, and airlines consider various factors in setting them. Recent studies have proposed several models for understanding U.S. airlines’ SBT-setting behavior and have examined the contribution of SBT-setting to airline on-time performance (OTP). How airlines in different countries and regions set their SBTs and to what degree differences in SBT-setting behavior explain observed differences in OTP is still unknown. Here we develop econometric models of SBTs based on historical distributions of actual block time to reveal the differences in SBT-setting behavior between major airlines in China and the U.S. We find that Chinese airlines focus on the left tail of the distributions while the U.S. airlines consider the left tail, middle, and inner right tail. They also have different ways of accounting for taxi-out time in setting SBTs, with the U.S. focusing on historical time distributions while in China taxi-out time is based on airport category. We then perform counterfactual analysis by using one nation’s model to predict the other nation’s SBTs and the resulting difference in OTP. Results indicate that the OTP of Chinese airlines would match those of the U.S. if they followed U.S. practices in setting SBTs, while U.S. airlines would have slightly worse OTP than Chinese carriers if they set SBTs according to Chinese methods. Our findings highlight the difference in SBT-setting behavior of major airlines in China and U.S. and its contribution to OTP. The model and methods presented here may also be applied to study and compare SBT setting and its impact on OTP in other parts of the world.

Schedule creep – In search of an uncongested baseline block time by examining scheduled flight block times worldwide 1986–2016

Based on a stratified random sampling of airlines’ schedules for 200 heavily travelled directional nonstop airport pairs, this paper examines systematically how scheduled block times in non-stop flights have changed from 1986 to 2016. Three econometric analyses, by way of a 10th percentile quantile regression, 15th percentile quantile regression and ordinary least-squares regression, show that after accounting for the effects of air traffic growth, airport-specific congestion, flight delays, number of seat per flight, aircraft type, flight heading, airport slot policy, other airport-specific anomalies, airline-specific policies and changes in crude oil price, scheduled block times have been growing at a pace between 0.21 and 0.33 min per year depending on the regression model, or a total of between 6.2 and 9.8 min per flight from 1986 to 2016. Over-flying crowded parts of Europe contributes to an increase of 4.1 min of block time in 2016 compared with 1986, while over-flying crowded parts of China contributes to a corresponding increase of 8.9 min of block time. Slot-based practices at one end of an airport pair reduce the scheduled block times between 1.3 and 2.0 min, and this reduction can vary slightly over time in depending on the regression model. Regional influences add to the changes in scheduled block times. Those airport pairs within north-eastern U.S. have scheduled block times between 3.6 and 4.0 min longer than their counterparts in the rest of North America, which in turn grow at 0.10 min per year in the 10th percentile regression. Airline-specific policies also add to further changes to the scheduled block times, with some starting with longer times in 1986 and reducing theirs over the years while others starting with shorter times in 1986 and increasing theirs over the years. Hub-specific adjustments to scheduled block times by individual airlines are also observed. Airlines with increasing frequency share at the departure and arrival airports, or in the non-stop airport pair itself, are shown to reduce the scheduled block times of that route. The overall increase from the projected baseline block times in 1986 to the actual scheduled block times in 2016 is 19.2 min per flight from the sampled nonstop city-pairs, consistent with a previous study on buffers in flight schedules within the U.S. Overall, un-adjusted scheduled block times are not a reliable benchmark for determining true flight delays, but using a percentile statistics from past flight records to determine a minimum feasible block time is a reasonable estimate even if aircraft types are not explicitly accounted for.

Assessing the impact of tactical airport surface operations on airline schedule block time setting

With the growth of air traffic, airport surfaces are congested and air traffic operations are disrupted by the formation of bottlenecks on the surface. Hence, improving the efficiency and predictability of airport surface operations is not only a key goal of NASA’s initiatives in Integrated Arrival/Departure/Surface (IADS) operations, but also has been recognized as a critical aspect of the FAA NextGEN implementation plan. While a number of tactical initiatives have been shown to be effective in improving airport surface operations from a service provider’s perspective, their impacts on airlines’ scheduled block time (SBT) setting, which has been found to have direct impact on airlines’ on-time performance and operating cost, have received little attention. In this paper, we assess this impact using an econometric model of airline SBT combined with a before/after analysis of the implementation of surface congestion management (SCM) at John F. Kennedy International Airport (JFK) in 2010. Since airlines do not consider gate delay in setting SBT, we find that reduction in taxi-out time variability resulting from SCM leads to more predictable taxi-out times and thus decreases in SBT. The JFK SCM implementation is used as a case study to validate model prediction performance. The observed SBT decrease between 2009 and 2011 at JFK is 4.8 min and our model predicts a 4.2 min decrease. In addition, Charlotte Douglas International Airport (CLT) is used as an example to demonstrate how different surface operations improvements scenarios can be evaluated in terms of SBT reduction.

Behavioral analysis of airline scheduled block time adjustment

Scheduled block time (SBT) is the time between gate departure and gate arrival assumed by airlines for use in published timetables and operations planning. SBT setting has critical impacts on airlines’ operating cost and on-time performance. Air carriers regularly update their SBTs to respond to changing operating conditions and evolving business strategies. Most existing studies have focused on investigating the impact of SBT on on-time performance or predicting SBT based on historical performance and market characteristics. However, the dynamics of adjusting SBT, which may allow deeper understanding about the trade-offs airlines make between SBT and on-time performance, have been rarely studied. In this paper, we assume that SBT adjustment choices reveal preferences. Based on airlines’ practice in setting SBT, hypothetical SBT scenarios and their corresponding on-time performance profiles are re-constructed to mimic the situations faced by airline schedulers. This enables us to infer how airlines trade-off between SBT, on-time arrivals, and earliness. By using correlated mixed logit models, we find that our five study airlines are willing to increase SBT from 0.38 to 0.54min to increase on-time performance by 1%. We also find that both on-time performance and early arrival are valued by airlines, but the former is considerably more valuable. The estimated models can also be used to predict airlines’ SBT adjustments in response to changes in operational performance.

Block time reliability and scheduled block time setting

While in ground transportation the concept of reliability has been extensively studied, there is little literature in air transportation. Scheduled block time (SBT) setting is a crucial part in airlines’ scheduling. Interviews with an airline and relevant work in ground transportation have shown that SBT and the historical block time distribution, reflecting block time reliability, have a close relationship. This paper investigates how the change in actual block time distribution will affect SBT and system performance. Firstly this relationship is studied with empirical data and multiple regression models. The distribution of the historical block time for a flight is depicted by the difference between every 10th percentiles. We found that gate delay plays a minor role in setting SBT and that SBTs have decreasing sensitivity to historical flight times toward the right tail of the distribution. To specifically link SBT setting with the flight’s on-time performance, a SBT adjustment model is further developed. Poor on-time performance leads to increased SBT in the next year. With the behavior model results showing that both the median block time and the “inner right tail” of the distribution affect SBT setting, an impact study is conducted to validate these impacts with historical data. The impact of historical block time distribution on SBT is validated with real data in year 2006–2008 and 2009–2011. Furthermore, by studying the flight performance difference based on different changes in SBT, we conclude that ignoring the impact on SBT changes when considering potential benefits of improved block time distribution could lead to inaccurate results.

The Impact of Airline Flight Schedules on Flight Delays

Airline flight delays have come under increased scrutiny lately in the popular press, with the Federal Aviation Administration data revealing that airline on-time performance was at its worst level in 13 years in 2007. Flight delays have been attributed to several causes such as weather conditions, airport congestion, airspace congestion, use of smaller aircraft by airlines, etc. In this paper, we examine the impact of the scheduled block time allocated for a flight, a factor controlled by airlines, on on-time arrival performance. We analyze empirical flight data published by the Bureau of Transportation Statistics to estimate the scheduled on-time arrival probability of each commercial domestic flight flown in the United States in 2007 by a major carrier. The structural estimation approach from econometrics is then used to impute the overage to underage cost ratio of the newsvendor model for each flight. Our results show that airlines systematically “underemphasize” flight delays, i.e., the flight delay costs implied by the newsvendor model are less than the implied costs of early arrivals for a large fraction of flights. Our results indicate that revenue drivers (e.g., average fare) and competitive measures (e.g., market share) have a significant impact on the scheduled on-time arrival probability. We also show that the scheduled on-time arrival probability is not positively affected by the total number of passengers on the aircraft rotation who could be affected by a flight delay, or the number of incoming and outgoing connecting passengers on a flight. Operational characteristics such as the hub and spoke network structure also have a significant impact on the scheduled on-time arrival probability. Finally, full-service airlines put a higher weight on the cost of late arrivals than do low-cost carriers, and flying on the lowest fare flight on a route results in a drop in the scheduled on-time arrival probability.

The impact of flight delays on passenger demand and societal welfare

US airline passengers increasingly have access to flight delay information from online sources. As a result, air passenger travel decisions can be expected to be influenced by delay information. In addition, delays affect airline operations, resulting in increased block times on routes and, in general, higher carrier costs and airfares. This paper examines the impact of flight delays on both passenger demand and airfares. Delays are calculated against scheduled block times as well as against more idealized feasible flight times. Based on econometric estimations, welfare impacts of flight delays are calculated. We find that flight delays on a route reduce passenger demand and raise airfares, producing significant decreases in both consumer and producer welfare. Since producer welfare effects are estimated to be three times as large as consumer welfare effects, we conclude that from an economic efficiency rationale, airlines should be required to pay for the bulk of flight delay remediation efforts.

Criteria for assessing operating room utilization in a free‐standing children’s hospital

The staffed hours of operation in any surgical facility are a valuable institutional resource. The realistic target for the utilization of this resource is dependent on many factors including scheduling, efficiency, and culture of the facility. There is no previously reported measure for the actual utilization of staffed regular operating room (OR) hours in an academic pediatric institution. The leadership of the perioperative services at Children's Hospital Boston (CHB) sought to define the utilization of surgical suite staffed block time hours at that institution and in addition determine whether changes in workflow could increase the measured utilization. Operating room efficiency in fiscal year 2009 was measured using two variables: utilization and turnover measured in hours for each month in fiscal year 2009, recorded in hours expressed as ratios (observed/expected) and as differences (observed - expected). A total of 27,851 cases from October 1, 2008, through September 30, 2009, were analyzed. All elective cases were scheduled electronically following institutional guidelines; urgent or emergent procedures were scheduled into vacant time slots on the day of the procedure. Time series analysis based on a generalized autoregressive moving average process was used to compare expected with observed utilization and to evaluate changes in utilization and turnover ratios. Efficiency as measured by capped utilization divided by total available time in the OR averaged 79%. Utilization ratios ranged from a low of 73% in February 2009 to a high of 87% in July 2009. An improvement in on-time first-case starts may have contributed to the increase in the utilization of staffed block time. Turnover time as defined by turnover ratio decreased significantly over time, indicating an improved efficiency in the OR starting in April 2009. Adhering to the specific guidelines that are followed at CHB, the mean utilization of scheduled block time was 79%. This was achieved by maximizing workflow in the surgical, anesthesia, and nursing disciplines to shorten turnover time, fill gaps in the elective schedule with emergency procedures, and provide staffing to accommodate cases that extend beyond the scheduled staffed time prior to the reporting period. Simulated models from other pediatric institutions suggest that the optimal utilization of designated time periods in a surgical facility may range from 85% to 90%.

Depeaking – economic optimization of air traffic systems

More than half of all flights in and out of Frankfurt International Airport are conducted by Lufthansa airlines, as it is one of their two major hubs. The so called hub and spokes systems allow airlines to bundle major passenger flows via connecting flights across the in- and outbound traffic. The reliability of these transfer connections, and specifically the arrival punctuality at the hub airports are crucial to the economics of the daily operation. Since Frankfurt International Airport has lately been one of the most congested airports in Europe, delays (in particular arrival delays) have increased quite significantly throughout the last years. To compensate for additional queueing time in the arrival processes, the scheduled block times inbound Frankfurt were continuously adjusted upwardly keeping the arrival punctuality (and thereby the connection reliability) close to stable. The two disadvantages of this approach have been the decrease in aircraft productivity and over-deliveries in peak hours, which in turn induce either additional block time delay or ground delay programs inbound Frankfurt. This paper introduces an iterative stochastic-simulation approach that models the vicious circle of lengthening block times and increased over-deliveries. In a second step it quantifies the operational and economical effects of depeaking the schedule and illustrates the overall growth potentials for the depeaked operation of the airport. The schedule was successfully implemented in Frankfurt in summer 2004.