scholarly journals Analysis On The Effect Of Express Checkouts In Retail Stores

2017 ◽  
Vol 33 (4) ◽  
pp. 765 ◽  
Author(s):  
Jin Kyung Kwak
Keyword(s):  
Queue Length ◽  
Computational Study ◽  
Wait Time ◽  
Arrival Rate ◽  
Service Operations ◽  
Time Variability ◽  
Retail Stores ◽  
Average Wait Time ◽  
Retail Service

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.

On Queue-Length Information when Customers Travel to a Queue

2020 ◽  
Author(s):  
Refael Hassin ◽  
Ricky Roet-Green
Keyword(s):  
Social Welfare ◽  
Queue Length ◽  
Real Life ◽  
Wait Time ◽  
Arrival Rate ◽  
Problem Definition ◽  
Model Complexity ◽  
Equilibrium Strategy ◽  
Online Information ◽  
Queue Lengths

Problem definition: We consider a service system in which customers must travel to the queue to be served. In our base model, customers observe the queue length and then decide whether to travel. We also consider alternative information models and investigate how the availability of queue-length information affects customer-equilibrium strategies, throughput, and social welfare. Academic/practical relevance: A common assumption in queueing models is that once a customer decides to join the queue, joining is instantaneous. This assumption does not fit real-life settings, where customers possess online information about the current wait time at the service, but while traveling to the service, its queue length may change. Motivated by this realistic setting, we study how queue-length information prior to traveling affects customers’ decision to travel. Methodology: We prove that a symmetric equilibrium exists in our base model. We perform the calculation numerically as a result of the model complexity, which is due to the fact that the arrival rate to the traveling queue depends on the current state of the service queue, and vice versa. The alternative models are tractable, and we present their analytical solution. Results: When customers can observe the service-queue length prior to traveling, their probability of traveling is monotonically nonincreasing with the observed queue length. We find that customers may adopt a generalized mixed-threshold equilibrium strategy: Travel when observing short queue lengths, avoid traveling when observing long queue lengths, and mix between traveling and not traveling when observing intermediate queue lengths, with a decreasing probability of traveling. Managerial implications: Our results imply that when system congestion is high, the provider can increase throughput by disclosing the queue-length information, whereas at low congestion, the provider benefits from concealing the information. With respect to social welfare, queue-length information prior to departure is beneficial when congestion is at intermediate to high levels and yields the same social welfare otherwise.

Doubling Personal Rapid Transit Capacity with Ridesharing

2005 ◽  
Vol 1930 (1) ◽  
pp. 107-112
Author(s):  
Robert E. Johnson
Keyword(s):  
Wait Time ◽  
Arrival Rate ◽  
Rapid Transit ◽  
Security Measures ◽  
Double Peak ◽  
Peak Period ◽  
Average Wait Time ◽  
Personal Rapid Transit ◽  
Transit Capacity ◽  
Simulation Results

A new personal rapid transit (PRT) operating policy is specified; it can often double peak period capacity with moderate wait times. When a passenger selects a destination for a vehicle, the destination is displayed for all to see. Any waiting passenger going to the same destination can also board. However, vehicles are not delayed just to gather additional riders. Fares are charged per person, rather than per vehicle. If the passenger queue length happens to equal the number of destinations and all destinations are equally likely, it is shown that on average one extra passenger will board each vehicle and that vehicle occupancy and line capacity will approximately double. The average wait time in this case will be approximately N/(2R), where N is the number of destinations and R is the vehicle arrival rate at the origin station. A station layout that facilitates this ridesharing policy is presented, and security measures are described for strangers traveling together. Simulation results give occupancy values up to 18% higher than expected from a simple model. During peak periods with directional flows, passengers facing a long wait can choose to travel to a destination near their own with no passenger queue and from there immediately board a vehicle to continue to their original destination. This makes ridesharing feasible in relatively large PRT systems.

Evaluating Virtual Care Experiences for Patients with Covid-19 Chief Complaints: Best-Practices Recommendations (Preprint)

2020 ◽  
Author(s):  
Saif Khairat ◽  
Malvika Pillai ◽  
Barbara Edson ◽  
Robert Gianforcaro
Keyword(s):  
Patient Experience ◽  
Wait Time ◽  
Secondary Outcome ◽  
Major Drawback ◽  
Cross Sectional ◽  
Average Wait Time ◽  
Adherence To Medication ◽  
Virtual Care ◽  
Chief Complaints ◽  
Virtual Visits

BACKGROUND Importance: Positive patient experiences are associated with illness recovery and adherence to medication. The shift toward virtual visits creates a need to understand the opportunities and challenges in providing a patient experience that is at least as positive as in-person visits. OBJECTIVE To evaluate the virtual care experience for patients with Covid-19 as their chief complaints. METHODS We conducted a cross-sectional study of the first cohort of patients with Covid-19 concerns in a virtual clinic. We collected data on all virtual visits between March 20-29, 2020. Outcomes: The main endpoints of this study were patient diagnosis, prescriptions received, referrals, wait time and duration, and satisfaction. The secondary outcome was the reported choice of alternative care options. RESULTS Of the 358 total virtual visits, 42 patients marked “Covid-19 Concern” as their chief complaint. Of those patients, 23 (54.8%) female patients, the average age of patients was 33.9 years, and 41 (97.7%) patients were seeking care for themselves and one (3.3%) visit was for a dependent. For all virtual visits, the average wait time (SD) was 157.2 (181.7) minutes and the average wait time (SD) for Covid-19 Concern visits was 177.4 (186.5) minutes. Covid-19 Concern phone visits had an average wait time (SD) of 180.1 (187.2), compared to 63.4 (34.4) minutes for Covid-19 Concern video visits. Thirteen (65%) patients rated their provider as “Excellent” with similar proportions among phone (64.3%) and video (66.7%). CONCLUSIONS This study evaluated the virtual experiences of patients with Covid-19 concerns. There were different experiences for patients depending on their choice of communication. Long wait times were a major drawback in the patient experience. We have learned from evaluating the experience of our first cohort of Covid-19 Concern patients.

scholarly journals Concurrency Control in Database Systems

2019 ◽  
Vol 1 (2) ◽  
pp. 26-40
Author(s):  
Dardina Tasmere ◽  
Md. Nazmus Salehin
Keyword(s):  
Concurrency Control ◽  
Phase Locking ◽  
Wait Time ◽  
Arrival Rate ◽  
Database Systems ◽  
Control Algorithms ◽  
Control Mechanisms ◽  
Time Stamp ◽  
System Behavior ◽  
Two Phase

Concurrency control mechanisms including the wait, time-stamp and rollback mechanisms have been briefly discussed. The concepts of validation in optimistic approach are summarized in a detailed view. Various algorithms have been discussed regarding the degree of concurrency and classes of serializability. Practical questions relating arrival rate of transactions have been presented. Performance evaluation of concurrency control algorithms including degree of concurrency and system behavior have been briefly conceptualized. At last, ideas like multidimensional timestamps, relaxation of two-phase locking, system defined prewrites, flexible transactions and adaptability for increasing concurrency have been summarized.

scholarly journals P012: Why did you leave? Contacting left without being seen patients to understand their emergency department experience

CJEM ◽  
2018 ◽  
Vol 20 (S1) ◽  
pp. S61-S61
Author(s):  
B. Brar ◽  
J. Stempien ◽  
D. Goodridge
Keyword(s):  
Health Care ◽  
Wait Time ◽  
Wait Times ◽  
Average Wait Time ◽  
Left Without Being Seen ◽  
Care Processes ◽  
Time Period ◽  
Initial Interaction ◽  
Improving Health Care

Introduction: As experienced in Emergency Departments (EDs) across Canada, Saskatoon EDs have a percentage of patients that leave before being assessed by a physician. This Left Without Being Seen (LWBS) group is well documented and we follow the numbers closely as a marker of quality, what happens after they leave is not well documented. In Saskatoon EDs, if a CTAS 3 patient that has not been assessed by a physician decides to leave the physician working in the ED is notified. The ED physician will: try to talk to the patient and convince them to stay, can assess the patient immediately if required, or discuss other appropriate care options for the patient. In spite of this plan patients with a CTAS score of 3 or higher (more acute) still leave Saskatoon EDs without ever being seen by a physician. Our desire was to follow up with the LWBS patients and try to understand why they left the ED. Methods: Daily records from one of the three EDs in Saskatoon documenting patients with a CTAS of 3 or more acute who left before being seen by a physician were reviewed over an eight-month period. A nurse used a standardized questionnaire to call patients within a few days of their ED visit to ask why they left. If the patients declined to take part in the quality initiative the interaction ended, but if they agreed a series of questions was asked. These included: how long they waited, reasons why they left, if they went somewhere else for care and suggestions for improvement. Descriptive statistics were obtained and analyzed to answer the above questions. Results: We identified 322 LWBS patients in an eight-month time period as CTAS 3 or more acute. We were able to contact 41.6% of patients. The average wait time was 2 hours and 18 minutes. The shortest wait time was 11 minutes, whereas the longest wait time was 8 hours and 39 minutes. It was found that 49.1% of patients went to another health care option (Medi-Clinic or another ED in Saskatoon) within 24hrs of leaving the ED. Long wait times were cited as the number one reason for leaving. Lack of better communication from triage staff regarding wait time expectations was cited as the top response for perceived roadblocks to care. Reducing wait times was cited as the number one improvement needed to increase the likelihood of staying. Conclusion: The Saskatoon ED LWBS patient population reports long wait times as the main reason for leaving. In order to improve the LWBS rates, improving communication and expectations regarding perceived wait times is necessary. The patient perception of the ED experience is largely intertwined with wait times, their initial interaction with triage staff, and how easily they navigate our very busy departments. Therefore, it is vital that we integrate the patient voice in future initiatives geared towards improving health care processes.

Triage in a public outpatient sexual health clinic

Sexual Health ◽  
2006 ◽  
Vol 3 (2) ◽  
pp. 87 ◽  
Author(s):  
Vickie Knight ◽  
Anna McNulty
Keyword(s):  
Sexual Health ◽  
Waiting Times ◽  
Health Centre ◽  
Wait Time ◽  
Health Clinic ◽  
Average Wait Time ◽  
Survey Tool ◽  
Time Flow ◽  
Sexually Transmissible Infections ◽  
Nurse Triage

Background:The increasing prevalence of sexually transmissible infections in Australia, coupled with a NSW Health Department requirement to target services to those most in need, has led many services to investigate patient triage as a way of better using scarce resources.2 In October 1997, a Triage Nurse position was trialled that aimed to facilitate the optimal flow of patients through the Sydney Sexual Health Centre (SSHC) clinic in an efficient and patient-focussed manner. A pre and post implementation time-flow study was conducted to analyse the effect. A staff survey was also completed to ascertain staff acceptance of the Nurse Triage system. Methods:A time-flow data survey tool was developed and placed in the medical record of every person attending the SSHC in one month in 1997 and again in 1999. The staff survey was an 11-item likert scale questionnaire administered to all centre staff. Data were analysed and average visit and waiting times were generated. Results:When comparing 1997 with 1999 data, the main results of note were that the length of consultation had been stable or decreased, the average wait time had remained stable or decreased and the wait time in the medical and nursing unbooked clinic had decreased. The average wait time for the unbooked clinic had decreased from ~24 minutes in 1997 to ~12 minutes in 1999. Conclusions:Since the introduction of Nurse Triage, the average overall waiting times for those who attend without an appointment has halved and the wait to see a doctor and a nurse has decreased. The majority of staff felt that the triage process had improved patient flow.

Minimizing wait time between chemotherapy and radiotherapy treatments.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 82-82
Author(s):  
James J. Sauerbaum ◽  
Gina DeMaio ◽  
Bradley Geiger ◽  
Regina Cunningham ◽  
Marianna Holmes ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Task Force ◽  
Wait Time ◽  
Support Staff ◽  
Cancer Center ◽  
Wait Times ◽  
Average Wait Time ◽  
Scheduling Method ◽  
Reduce Wait Time ◽  
E Mail

82 Background: Members of the scheduling teams at the Abramson Cancer Center observed prolonged delays between chemotherapy and radiation therapy treatments scheduled by staff from 2 independent departments leading to inconvenience for patients receiving concurrent chemo- and radiation therapy (CRpts). Methods: An analysis of baseline data over 6 weeks revealed that for 157 unique consecutive patients undergoing daily chemotherapy and radiation (a total of 353 encounters), the mean time between scheduled treatments was 122 minutes. For 39% of encounters the wait time was greater than 120 minutes. To improve the adjacency of chemotherapy and radiation appointments and to consistently reduce wait time between treatments to less than 120 minutes, we formed a Chemotherapy/Radiation Scheduling Task Force consisting of patient service representatives, practice managers, and physician and nurse advisors. We determined that CRpts should be scheduled using a “huddle” strategy whereby prospectively identified CRpts are simultaneously scheduled for both treatments in a coordinated manner. Identifying CRpts for coordinated scheduling was facilitated by the creation of a chemo-radiation scheduling inbox to which clinicians and support staff e-mail names of new CRpts in order to alert the scheduling team. Our two lead schedulers meet 2-3 times per week to coordinate patient schedules. A weekly scorecard of the wait times for CRpts patients is distributed via e-mail to the clinicians and support staff. Results: Over the past 6 months, we have used the huddle method for 80% of 986 consecutive CRpt encounters. Our average wait time for huddle-scheduled encounters has been reduced to 62.5 minutes with only 9% of encounters having wait times over 120 minutes. For non-huddle-scheduled encounters, the average wait time is 129 minutes with 57% having wait times over 120 minutes. Conclusions: Utilization of a huddle scheduling method has successfully reduced wait time for CRpts. Use of the huddle method continues to grow with staff training and awareness of the new process.

Examining Applicability of RSQS with Added Dimensions for Organized Apparel Retailers in India

Paradigm ◽  
2017 ◽  
Vol 21 (1) ◽  
pp. 52-74 ◽  
Author(s):  
Arif Hasan
Keyword(s):  
Product Quality ◽  
Structural Equation ◽  
Primary Data ◽  
Equation Modeling ◽  
Original Form ◽  
Retail Stores ◽  
Retail Sector ◽  
Quality Scale ◽  
Retail Service ◽  
Apparel Retailers

This study endeavours to validate and develop a scale for organized apparel retail stores, as there is a need to develop and validate a modified instrument. Primary data have been collected from selected organized apparel retailers. Statistical tools, exploratory factor analysis (EFA) and structural equation modeling (SEM), were applied. It revealed that Retail Service Quality Scale (RSQS) model in original form is deficient and less appropriate in the Indian context, modified scale (i.e., Apparel Store Service & Product Quality Scale [ASSPQS]) may exhibit strong model fit for apparel retail sector. The findings and recommendations will enable retail stores to gather insights into current levels of service and product quality and may facilitate them in the improvement of certain aspects in service and product quality of the stores.

Queues with time-dependent arrival rates. III — A mild rush hour

1968 ◽  
Vol 5 (3) ◽  
pp. 591-606 ◽  
Author(s):  
G. F. Newell
Keyword(s):  
Queueing Theory ◽  
Queue Length ◽  
Arrival Rate ◽  
Time Dependent ◽  
Service Rate ◽  
Diffusion Approximations ◽  
Dimensionless Form ◽  
Queue Lengths ◽  
The Mean ◽  
Mean Queue Length

The arrival rate of customers to a service facility is assumed to have the form λ(t) = λ(0) — βt2 for some constant β. Diffusion approximations show that for λ(0) sufficiently close to the service rate μ, the mean queue length at time 0 is proportional to β–1/5. A dimensionless form of the diffusion equation is evaluated numerically from which queue lengths can be evaluated as a function of time for all λ(0) and β. Particular attention is given to those situations in which neither deterministic queueing theory nor equilibrium stochastic queueing theory apply.

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