Expected Wait Time Research Articles

Wait time to stochastic self-focusing

The first passage time extended to stochastic nonlinear beam dynamics emerges as a natural time scale and a meaningful estimator for the expected wait time to the first self-focusing event within a large sample of stochastic laser pulses. We show that the ratio P/Pcr of the laser peak power P to the critical power of self-focusing Pcr, which plays a central role in deterministic self-focusing, keeps its status as a key governing parameter in stochastic self-focusing. However, in contrast to its deterministic counterpart, the Pcr/P ratio of a stochastic laser beam is no longer an indicator of whether self-focusing will occur, but is, rather, a predictor of when the self-focusing is expected, in the sense of the first passage time, given the statistics of the laser field.

Discount or Speed?: An Analysis of Reward Offer in Exchange for No-Rush Shipping

This research investigates how a no-rush shipping reward program, which offers a discount or a reward to a customer who chooses no-rush shipping for her online purchased items, can be utilized by an online seller to reduce shipping and expediting costs. In particular, we consider a setting in which an online seller has to ship two items to a customer. Each item can be shipped either on time or late, depending on its uncertain lead time. If an item is delivered late, the seller will incur a loss in customer goodwill unless the customer opts in to a no-rush shipping reward program and agrees to take a reward in exchange for potentially receiving an item late. To avoid shipping an item late, the seller also has an option to expedite an item at an extra cost. The seller’s optimal decisions regarding whether to offer a no-rush shipping reward program on any items and whether to ship each item separately or together at a normal or expedited speed are characterized. Our findings reveal that the number of items that the seller should offer the no-rush shipping reward program decreases as the required reward to entice customer participation increases. If the no-rush shipping reward program is offered on both items, then consolidating them into a single shipment always emerges as the optimal strategy. However, in cases where the program applies to none or only one item, it would be optimal for the seller to consolidate the shipment if and only if the shipping cost is sufficiently large. Through numerical demonstration, we underscore the noteworthy cost savings attainable via the no-rush shipping reward program, especially when the expected wait time to receive an item is long, the expediting cost is large, but the normal shipping cost is small. This study suggests that implementing a no-rush shipping reward program empowers online sellers with enhanced shipping management flexibility, which in turn effectively reduces operating and shipping costs as well as the environmental impact, while also meeting customers’ expectation.

Reinforcement Learning for Dynamic Pricing of Shared-Use Autonomous Mobility Systems Considering Heterogeneous Users: Model Development and Scenario Testing

A key aspect of the success of shared-use autonomous mobility systems will be the ability to price rides in real time. As these services become more prevalent, it becomes of high importance to detect shifts in behavior to quickly optimize the system and ensure system efficiency and economic viability. Therefore, (1) pricing algorithms should be able to price rides according to complex underlying demand functions with heterogeneous customers and (2) the algorithm should be able detect nonstationary behavior (e.g., changing customers’ willingness to pay) from its previously learnt decisions and alter its pricing mechanism accordingly. We formulate a dynamic pricing and learning problem as a Markov decision process and subsequently solve it through a reinforcement learning (RL) algorithm, with heterogeneous customers accepting the trip characteristics (price, expected wait time) probabilistically. Insights from a fixed fleet operation of an autonomous private ridesourcing system in Chicago are presented. Given our formulation of the demand model, the algorithm learns in 25 days, increasing revenue by 90% and decreasing customer wait times by 90% compared to day 5. After gathering insights from the RL algorithm and applying optimal static pricing (i.e., a constant specific surge multiplier), we find that RL can achieve near 90% optimality in revenue. The RL algorithm, nevertheless, proves to be robust. Two scenarios are tested where a sudden shock occurs or customers slowly change their willingness to pay, illustrating that RL can quickly adapt its parameters to the situation.

Sooner or Later? Promising Delivery Speed in Online Retail

Abstract. Problem definition: Online retailers have to provide customers with an estimate of how fast an order can be delivered before they decide to make the purchase. Retailers can strategically adjust this delivery speed promise online without changing offline infrastructure, and doing so may fundamentally impact business outcomes. It can influence consumers’ purchasing decisions and postpurchase experiences, often in the opposite direction. On one hand, an aggressive (i.e., faster) delivery estimate could ensure that more customers meet their deadlines and thus, may increase their purchases ex ante. On the other hand, an aggressive estimate tends to overpromise, potentially leading to a longer than expected wait time, which can lower customer satisfaction and increase product returns ex post. In this research, we estimate the causal effect of retailers’ delivery speed promise on customer behaviors and business performance. Methodology/results: Collaborating with Collage.com , an online retailer that sells customized photo products across the United States, we exogenously varied the disclosed delivery speed estimates online while keeping the physical delivery speed unchanged. Using the difference-in-differences identification strategy, we find that a faster promise increases sales and profits, but it also increases product returns and reduces customer retention. In addition, we propose a data-driven model that uses the estimated parameters to optimize delivery promises to maximize customer lifetime value. Managerial implications: Our findings provide managerial insights and a data-driven policy that retailers can leverage to optimize and customize their delivery promises. Funding: T. Sun acknowledges research support from CKGSB Research Institute. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0174 .

Predicting Time to and Average Quality of Future Offers for Kidney Transplant Candidates Declining a Current Deceased Donor Kidney Offer: A Retrospective Cohort Study.

At the time a kidney offer is made by an organ donation organization (ODO), transplant physicians must inform candidates on the pros and cons of accepting or declining the offer. Although physicians have a general idea of expected wait time to kidney transplantation by blood group in their ODO, there are no tools that provide quantitative estimates based on the allocation score used and donor/candidate characteristics. This limits the shared decision-making process at the time of kidney offer as (1) the consequences of declining an offer in terms of wait-time prolongation cannot be provided and (2) the quality of the current offer cannot be compared with that of offers that could be made to the specific candidate in the future. This is especially relevant to older transplant candidates as many ODOs use some form of utility matching in their allocation score. We aimed to develop a novel method to provide personalized estimates of wait time to next offer and quality of future offers for kidney transplant candidates if they refused a current deceased donor offer from an ODO. A retrospective cohort study. Administrative data from Transplant Quebec. All patients who were actively registered on the kidney transplant wait list at any point between March 29, 2012 and December 13, 2017. The time to next offer was defined as the number of days between the time of the current offer and the next offer if the current one were declined. The quality of the offers was measured with the 10-variable Kidney Donor Risk Index (KDRI) equation. Candidate-specific kidney offer arrival was modeled with a marked Poisson process. To derive the lambda parameter for the marked Poisson process for each candidate, the arrival of donors was examined in the 2 years prior to the time of the current offer. The Transplant Quebec allocation score was calculated for each ABO-compatible offer with the characteristics that the candidate presented at the time of the current offer. Offers where the candidate's score was lower than the scores of actual recipients of the second kidneys transplanted were filtered out from the candidate-specific kidney offer arrival. The KDRIs of offers that remained were averaged to provide an estimate of the quality of future offers, to be compared with that of the current offer. During the study period, there were 848 unique donors and 1696 transplant candidates actively registered. The models provide the following information: average time to next offer, time to which there is a 95% probability of receiving a next offer, average KDRI of future offers. The C-index of the model was 0.72. When compared with providing average group estimates of wait time and KDRI of future offers, the model reduced the root-mean-square error in the predicted time to next offer from 137 to 84 days and that of predicted KDRI of future offers from 0.64 to 0.55. The precision of the model's predictions was higher when observed times to next offer were 5 months or less. The models assume that patients declining an offer remain wait-listed until the next one. The model only updates wait time every year after the time of an offer and not in a continuous fashion. By providing personalized quantitative estimates of time to and quality of future offers, our new approach can inform the shared decision-making process between transplant candidates and physicians when a kidney offer from a deceased donor is made by an ODO.

Improving Access to Renal Transplantation for Highly Sensitized Patients.

Transplantation is accepted to be the best form of treatment for end-stage renal disease, and it is also clear that the longer the patients wait on dialysis, the worse their overall graft and patient outcomes are, posttransplantation. The presence of increasing levels of anti-HLA antibodies progressively increases waiting times, especially highly sensitized patients (HSP) with panel reactivity (PRA) >85%. Currently, in the EuroTransplant (ET) allocation programs, the presence of antibody, even at a low level as detected by single antigen beads, generates a virtual PRA (vPRA). This results in unacceptable antigen matches so that offers are not made to these patients, increasing their expected wait time. ET allocates organs in three different systems. The Acceptable match program is successful in transplanting sensitized patients, but only a tiny percentage of potential recipients are eligible. The standard ET kidney allocation system integrates patients’ PRA into a mismatch probability (MMP), which generates additional points with the aim of assisting HSP to be transplanted. However, despite this, and partly because of the low donor rates in Germany, the median waiting time for patients with a vPRA of >95% is 13.2 y (and >10 y for patients with vPRA <95%). The ET senior programme that allocates kidneys on the basis of blood group, waiting time, and region (to minimize ischemic time) also disadvantages HSP. In this article, Zecher et al1 have analyzed the impact of vPRA levels of the various ET allocation systems in Germany and the factors disadvantaging HSP, including the effectiveness of the MMP to compensate for sensitization. Many of their findings are not surprising. For HSP, the higher their vPRA, the longer their median wait times are, except organs offered in the acceptable match system. This is only open to about 2% of patients, and the shorter waiting times reflect that the German patients in this program are in a pool with all ET countries, most of whom have much shorter waiting times. The MMP was designed to compensate for sensitization (as well as blood group and HLA type), and although incorporation of vPRA in 2020 did improve the correlation between vPRA and the score, the effect was not marked, and a high score could be generated with low or even zero vPRA. Furthermore, the maximum MMP score is only 100 out of a total of 800 points, thus limiting its efficacy to significantly reduce waiting times for HSP. The analysis of their cohort of patients on the waiting list, therefore, demonstrated that the MMP compensation was very inadequate. They also found that there was a high organ rejection rate in transplant centers for immunological reasons, suggesting inadequate or incorrect unacceptable antigen mismatches data provided to ET. Finally, the authors found large (> 2-fold) differences in the probability of transplantation between the 7 regions within Germany. This may partly be explained by variation in donation rate but is likely to represent different approaches to listing unacceptable antigen mismatches, including single antigen beads data. Clearly, HSP are and will always be disadvantaged by their sensitization, but in the interest of equity, their waiting times should be reduced as much as possible to improve long-term outcomes. Zecher et al1 highlight several important issues resulting in excessive waiting times, especially for HSP. There should be considerable scope to modify reporting of acceptable and unacceptable antigens to ET, and modification of the allocation algorithms could significantly improve waiting times for these severely disadvantaged patients. Comparison of individual center practice is also likely to identify factors that might reduce center and regional variation. German (and all ET) data could be used to model alterations to the allocation algorithms to improve waiting times for HSP and for better equity to other groups of patients, such as ethnic minorities. Lessons may be learned from offering algorithms in other countries. For instance, one of the key objectives for the 2019 UK Kidney Offering Scheme was to avoid prolonged waiting times that are predictable, which is particularly relevant to HSP. In the development of the scheme, different methods of prioritization were considered. These included priority tiers and different points weightings based on factors including sensitization, matchability, and waiting time, and the methods were evaluated by analyzing simulated data across different potential schemes. The 2019 UK scheme is expected to significantly reduce variation in waiting time in sensitized patients, and this reduction is also expected to be seen across other factors, including ethnicity. Although this paper highlights inequities in the ET system and Germany in particular, it highlights the need for all organ allocation algorithms to be reviewed and alternatives extensively modeled to generate algorithms to improve overall equity.

The Penney’s Game with Group Action

Consider equipping an alphabet $\mathcal{A}$ with a group action that partitions the set of words into equivalence classes which we call patterns. We answer standard questions for the Penney's game on patterns and show non-transitivity for the game on patterns as the length of the pattern tends to infinity. We also analyze bounds on the pattern-based Conway leading number and expected wait time, and further explore the game under the cyclic and symmetric group actions.

Sooner or Later? Promising Delivery Speed in Online Retail

Online retailers have to provide customers with an estimate of how fast an order can be delivered before they purchase it. Retailers can strategically adjust this delivery speed promise online without changing offline infrastructure, and it may fundamentally impact business outcomes. It can influence consumers' purchasing decisions and post-purchase experiences, often in the opposite direction. On one hand, an aggressive (i.e., faster) delivery estimate could ensure that more customers meet their deadlines and thus may increase their purchases ex ante. On the other hand, an aggressive estimate tends to overpromise customers, risking a longer than expected wait time, which can lower customer satisfaction and increase product returns ex post. In this research, we study the causal effect of retailers' delivery speed promise on customer behaviors and business performance. Collaborating with Collage.com, an online retailer that sells customized photo products across the US, we exogenously varied the disclosed delivery speed estimates online while keeping the physical delivery speed unchanged. Using a difference-in-differences identification and a dataset with 212,340 transactions in 7,090 cities, we find that a one-day faster promise increases sales by 0.73%, profits by 2.0%, and value per order by 3.5%; a one-day slower promise reduces sales by 0.51%, profits by 2.7%, and value per order by 3.1%. However, the aggressive disclosure increases product returns and deteriorates customer retention. In addition, we study heterogeneous responses across new and existing customers. Our findings provide managerial insights that retailers could leverage to customize their delivery promises.

Impact of Behavioral Factors on Performance of Multi‐Server Queueing Systems

Recent studies have shown that the processing speed of employees in service‐based queueing systems is impacted by various behavioral factors. However, there is limited analytical work to investigate how these behavioral factors affect the overall performance of different queueing system designs. In this study, we focus on the response of human servers to the design and congestion level of the queueing system in which they operate. Specifically, we incorporate two behavioral factors into multi‐server analytical queueing models: (1) server speedup due to increase of workload, and (2) server slowdown due to social loafing when multiple workers share the workload. We evaluate how these factors affect the performance of both the multi‐server single‐queue ( SQ) and multi‐server parallel‐queue ( PQ) system and the relative superiority of each system with respect to the number of customers in queue and the expected wait time in queue. We show that the impact of workload‐dependent speedup can be decomposed into a direct effect and indirect effect on system performance. The direct effect leads to a reduced queue size due to increased expected service rate, while the indirect effect decreases queue size due to the “smoothing” effect. We quantify the performance impacts associated with both behavioral factors, illustrate the conditions where each effect dominates, and derive threshold values for these behavioral effects beyond which PQ systems outperform SQ systems. We also consider strategic routing and its impact on the performance of PQ systems. Our analytical contributions and numerical analyses offer important managerial guidance regarding the choice of the queueing system design and provide a theoretical foundation for future research in behavioral queueing.

Abstract 5: Optimizing Emergency Room Throughput for Cardiac Telemetry Patients: A Queuing Theory Approach

Background: The capacity of telemetry monitoring in hospitalized patients is constrained due to limited equipment and personnel. Electing for telemetry monitoring may pose a bottleneck for patients admitted through the Emergency Department (ED). This can result in increased length of stays in the ED, a parameter that is correlated with decreased quality of care due to crowding and delay in treatment. Queuing theory is the mathematical study of waiting times. We opted to use queuing theory to analyze system performance before and after an expansion of telemetry capacity. Objective: To retrospectively analyze the impact of telemetry capacity on ED wait times using enterprise data warehouse (EDW) data and queuing theory. Methods: Unused telemetry boxes were used as a proxy metric for available cardiac telemetry monitoring. The number of telemetry boxes in use at a large urban academic medical center was determined by daily manual count. The number of telemetry admissions, average length of telemetry use, and length of stay in the ED were obtained from EDW data. Using queuing theory, system parameters including expected wait time and percent of patients who will wait were calculated for scenarios to simulate before and after the expansion of telemetry capacity. These modeled system behaviors were compared to observed data from before and after the intervention to understand the utility of the mathematical model. Results: Over the study period of 365 days, a total of 13,515 inpatient non-critical care hospital encounters had orders for telemetry monitoring. Prior to the increase in telemetry capacity, there were 144 telemetry boxes, which were utilized at 96.7% of capacity. Using these parameters, the model predicts a 5.8-hour average wait time and a 42.1% chance that a patient will have to wait for a telemetry box. Increasing telemetry capacity modestly by 10 boxes (144 to154 boxes) results in a dramatic system improvement, reducing utilization to 88.6%, average wait time for a telemetry bed to 34 minutes, and percent of patients waiting for a telemetry slot to 9.4%. These predicted results compared well to the observed data. After the intervention, there was a decrease in utilization to 86.9%, and a statistically significant reduction in time waiting in the ED for an inpatient bed by 1.24 (p&lt;0.05) hours per telemetry patient, representing a total savings of ED boarding time of 11.2 bed-days per week. Conclusions: Queuing theory allows for modeling of healthcare system behavior to predict needed clinical capacity for achieving system goals. Our simplified model demonstrates that a modest increase in capacity can drive significant system improvements. Utilizing queuing theory and understanding the behavior of the healthcare system can improve the predicted impact of interventions on cost-effectiveness and quality of care.

Impact of Behavioral Factors on Performance of Multi-Server Queueing Systems

Recent studies have shown that the processing speed of employees in service-based queueing systems is impacted by various behavioral factors. Limited analytical work, however, has been done to investigate how these behavioral factors affect the overall performance of different queueing system designs. In this paper, we focus on the response of human servers to the design and congestion level of the queueing system in which they operate. Specifically, we incorporate two behavioral factors into multi-server analytical queueing models: (1) server speedup due to increase of workload, and (2) server slowdown due to social loafing when multiple workers share the workload. We evaluate how these factors affect the performance of both the multi-server single-queue (SQ) and multi-server parallel-queue (PQ) system and the relative superiority of each system with respect to the number of customers in queue and the expected wait time in queue. We show that the impact of workload-dependent speedup on the queue size can be decomposed into a direct impact that reduces the queue size due to an increase in the expected service rate, and an indirect impact that further reduces the queue size due to smoothing. We quantify the performance impacts associated with both behavioral factors and clearly illustrate the conditions where each effect dominates and derive threshold values for these behavioral effects beyond which PQ systems outperform SQ systems. Finally, we consider strategic routing and its impact on the performance of the PQ system. Our analytical contributions and numerical analyses offer generalized managerial guidance regarding the choice of the queueing system design and provide a theoretical foundation for future research in behavioral queueing.

Can a supervised algorithmic assessment of men for prostate cancer improve the quality of care? A retrospective evaluation of a prostate assessment pathway in Saskatchewan.

The Saskatoon Prostate Assessment Pathway (SPAP) was developed in 2013 in part to decrease the wait times between physician referral and biopsy for patients with suspected prostate cancer. Using an algorithm carefully designed to optimize appropriate prostate biopsy rates, physicians can directly refer patients for biopsy through the SPAP without seeing a urologist. All other patients are referred to the Saskatoon Urology Associates (SUA). The present study evaluates the performance of the algorithm. 971 patients seen at the SUA and 302 patients seen through the SPAP were identified. Information on age, biopsy status and outcome, risk stratification, and time between referral and biopsy was collected. Biopsy wait time data was analyzed using gamma distribution. Association between referral method and biopsy rate, and between referral method and risk stratification, was analyzed using Z-test. The expected wait time from referral to biopsy for patients seen through SUA was 2.63 times longer than those seen through SPAP (34 vs. 91 days). The biopsy rate of patients seen in the SPAP was significantly higher than those by SUA (88% vs. 69%, 95% confidence interval [CI] 0.14-0.26; p<0.00001). There was no significant difference in positive biopsy rates for patients seen through the SPAP vs. SUA (81% vs. 74%, 95% CI -0.011,0.14; p=0.095), for detection of low-risk cancer, (12% vs. 10%, 95% CI -0.034,0.080; p=0.44), or for clinically relevant cancer, i.e., intermediate- and high-risk cancer, for SPAP vs. SUA (56.54% vs. 56.68%, 95% CI -0.091,0.089; p=0.49). The algorithm used in the SPAP is effective in decreasing wait time to prostate biopsy and has the same cancer/pre-cancer detection rate, but at the cost of a higher biopsy rate. Both referral mechanisms result in few low-risk cancer detection biopsies, finding primarily cases of high- or intermediate-risk cancer.

Spatial welfare effects of shared taxi operating policies for first mile airport access

Abstract With increasing availability of alternative mobility options for first/last mile, it is necessary to better understand how shared taxis are impacting airport access demand and consumer surplus. However, no study has been conducted to evaluate the welfare effects of the range of shared taxi matching and fare allocation policies for airport access. Using several data sources primarily from Port Authority of NY and NJ and The Taxi and Limousine Commission, a mode choice model is estimated for access to John F. Kennedy International Airport in New York City. The baseline model and data show that passengers have a value of time of $101 per hour, consistent with Harvey’s study from 1986. Airport taxi travelers are also elastic to cost in a similar manner to public transit. The model is used to evaluate two policies: a first (we call this wait-share policy) where taxis can offer shared rides for two passengers from the same zip code, incorporating an endogenous expected wait time variable; and a second (we call this space-share policy) where taxis match randomly arriving passengers from any zip codes in the city. These two policies reflect extreme ends of a spectrum of policies between waiting and detouring. Findings suggest that having a shared taxi option benefit passengers in NYC going to JFK airport by at least 10% increase in consumer surplus. However, the increase in taxi ridership comes at a cost to transit ridership. Furthermore, the population in NYC that benefits most is highly dependent on the type of shared taxi policy. A wait-share policy benefits passengers from the dense parts of Manhattan most, while a space-share policy distributes the benefits more to other boroughs. These insights can help policymakers set regulations in providing first/last mile ride-sharing taxi options in different cities around the world.

Analysis On The Effect Of Express Checkouts In Retail Stores

In this study, we investigate the effect of having express checkout lanes in retail stores. Express checkout lanes are being used to reduce expected wait time of small-buying customers, but their operational effect has not been analyzed so far. By comparing the wait time and the queue length of the two scenarios (universal checkout lanes only and separated checkout lanes with express counters) via simulation, we have found that the average wait time of the separated checkout lanes may not be shorter than that of universal checkout lanes. This may be due to that the effect of pooling servers decreases as the number of servers being pooled at each checkout set decreases. The queue length of express checkout lanes may be shorter than that of universal checkout lanes, but in some cases, the average queue length of separated checkout lanes is longer than that of universal checkout lanes, probably due to the effect of pooling servers. By conducting a computational study, we have observed that the effect of pooling servers decreases with customer arrival rate, decreases with regular checkout duration, and slightly increases with regular checkout time variability. These results give us an insight on when the express checkout counters can be effective in retail service operations.

Variations in Cervical Cytology Testing Among Women of Different Ethnicities [29G

INTRODUCTION: Current information about ethnic differences in the progression and regression of intraepithelial lesions on cervical cytology is undefined, with existing studies looking solely at ethnic differences in compliance with follow-up and treatment. The purpose of this study was to investigate variation in progression and regression of cervical cytology screening results among an ethnically diverse population. METHODS: A retrospective review of was conducted for women receiving cervical cytology evaluation from January 1, 2006 through September 30, 2015. Differences in intraepithelial lesion diagnoses on cervical cytology for patients were reviewed. We fit Markov multi-state models to estimate self-reported race/ethnicity-specific expected wait times and hazard rates for each possible regression and progression, and compared a model with race to an intercept-only model using a chi-square likelihood ratio test. RESULTS: The sample consisted of 7635 patients, with mean baseline age 34±12, and was 31% Hispanic, 26% White, 17% Black, and 9% Asian, Middle Eastern or other. We estimated a total of 24 hazard ratios and 95% confidence intervals comparing rates of transition to and from ASCUS, ASCH, LGSIL, and HGSIL, for the race/ethnicity categories of Black, Hispanic, and a category for Asian, Middle Eastern, or other, compared to White. The model including race fit significantly better than the intercept-only model (p &lt; 0.001). Visual inspection of expected wait time curves indicated that Black and Hispanic women had a slower progression as well as a slower regression. CONCLUSION: Preliminary analysis suggests that the progression/regression of precancerous lesions on cervical cytology may behave differently among women of different ethnicities.

Minimum Cost Matching for Autonomous Carsharing

Carsharing programs provide an alternative to private vehicle ownership. Combining car-sharing programs with autonomous vehicles would improve user access to vehicles thereby removing one of the main challenges to widescale adoption of these programs. While the ability to easily move cars to meet demand would be significant for carsharing programs, if implemented incorrectly it could lead to worse system performance. In this paper, we seek to improve the performance of a fleet of shared autonomous vehicles through improved matching of vehicles to passengers requesting rides. We consider carsharing with autonomous vehicles as an assignment problem and examine four different methods for matching cars to users in a dynamic setting. We show how applying a recent algorithm (Scalable Collision-avoiding Role Assignment with Minimal-makespan or SCRAM) for minimizing the maximal edge in a perfect matching can result in a more efficient, reliable, and fair carsharing system. Our results highlight some of the problems with greedy or decentralized approaches. Introducing a centralized system creates the possibility for users to strategically mis-report their locations and improve their expected wait time so we provide a proof demonstrating that cancellation fees can be applied to eliminate the incentive to mis-report location.

Can Server Behavior and Service System Design Outweigh the Benefits of Pooling?

It is widely accepted that multi-server single-queue (SQ) systems outperform multi-server parallel-queue (PQ) systems due to the pooling effect. However, humans are not machines, and the assumption that servers perform identically regardless of the queueing system, is not necessarily true in practice. In this paper, we investigate the impact of two human server behaviors - social loafing and workload-dependent speedup - and one physical factor - walking time - on the performance of the SQ and PQ systems. We develop theoretical models and comparatively analyze them with respect to three queueing performance measures: expected wait time, pre-service time, and total system time. We propose functional forms for social loafing and speedup effects and evaluate the performance of the models numerically. We find that social loafing worsens the performance of the SQ system. Server speedup improves performance of the PQ system. Moreover, this improvement is greater than the direct impact of a comparable average increase in the server's speed. We derive thresholds beyond which PQ outperform SQ systems for all three performance measures. Our findings provide managers with guidance in the design of their queueing operations and serve as a foundation for future research on service systems with behavioral elements.

Partially Flexible Operating Rooms for Elective and Emergency Surgeries

ABSTRACTIn hospitals, the management of operating rooms faces a trade‐off between the need to be responsive to emergency surgeries and to conduct scheduled elective surgeries efficiently. Operating rooms can be configured as flexible and handle both electives and emergencies, or as dedicated to focus on either electives or emergencies. With flexible rooms, the prioritization of emergencies over scheduled electives can lead to schedule disruptions. Focused rooms can lead to imbalances between capacity and surgery workload. Whereas hospital administrators typically handle this trade‐off by employing either flexible rooms (complete flexibility) or dedicated rooms (complete focus), we investigate whether a combination of flexible and dedicated rooms (partial flexibility) could be a preferable alternative. The ensuing question is what is the right combination of flexible and dedicated rooms? A versatile simulation model is developed to evaluate different resource allocation policies under various environmental parameters and performance metrics, including patient wait time, staff overtime, and operating room utilization. The main result is that partial flexibility configurations outperform both complete flexibility and complete focus policies by providing solutions with improved values of expected wait time for both emergency and elective patients.

Patient Satisfaction With Nursing Care in an Urban and Suburban Emergency Department

Patient satisfaction is an important outcome measurement in the emergency department (ED). When unavoidable, the negative effect of patient wait time may be lessened by communicating expected wait time, affective support, health information, decisional control, and competent providers. This controlled quasi-experimental design used a convenience sample. The patient questionnaire included demographics, expected and perceived wait time, receiving of comfort items, information and engaging activities and their perceived helpfulness for coping with waiting, and the Consumer Emergency Care Satisfaction Scale measure of patient satisfaction with nursing. Systematic offering of comfort items, clinical information, and engaging activities were statistically analyzed for impact on perceived wait times, helpfulness in waiting, and satisfaction with nursing care. Interventions were supported by the data as helpful for coping with waiting and were significantly related to nursing care satisfaction. Interventions were less helpful for suburban patients who were also less satisfied. Nurses can influence patient satisfaction in the ED through communication and caring behaviors.

Equilibria of Online Scheduling Algorithms

We describe a model for competitive online scheduling algorithms. Two servers, each with a single observable queue, compete for customers. Upon arrival, each customer strategically chooses the queue with minimal expected wait time. Each scheduler wishes to maximize its number of customers, and can strategically select which scheduling algorithm, such as First-Come-First-Served (FCFS), to use for its queue. This induces a game played by the servers and the customers. We consider a non-Bayesian setting, where servers and customers play to maximize worst-case payoffs. We show that there is a unique subgame perfect safety-level equilibrium and we describe the associated scheduling algorithm (which is not FCFS). The uniqueness result holds for both randomized and deterministic algorithms, with a different equilibrium algorithm in each case. When the goal of the servers is to minimize competitive ratio, we prove that it is an equilibrium for each server to apply FCFS: each server obtains the optimal competitive ratio of 2.