Estimating waiting and idle times for special queuing situations of tree harvesting machines

1981 ◽  
Vol 11 (1) ◽  
pp. 99-104 ◽  
Author(s):  
C. H. Meng
Keyword(s):  
Waiting Time ◽  
Historical Data ◽  
Idle Time ◽  
Processing Times ◽  
Mean Waiting Time ◽  
Analytical Formulae ◽  
Input Time ◽  
Time Required ◽  
Tree Harvesting ◽  
Normally Distributed

The purpose of this study is to develop analytical formulae for special queuing situations which occur during the operations of the felling and processing devices of a tree harvester, and the pickup and processing devices of a tree processor. Analytical formulae are used to estimate mean waiting time and mean idle time; in case 1 both "input" times and processing times are normally distributed; in case 2 "input" times are normally distributed and processing times are Poisson distributed. "Input" time is a term used for convenience to denote time required to fell a tree by a harvester or time required to pick up a tree by a processor. Methods of choosing distributions for representing "input" times and processing times are provided. In addition, there are two examples, using historical data, which demonstrate the applications of the analytical formulae.

493 Evaluation of Patient Waiting Times for Plain Radiographs Whilst Attending Outpatient Clinics in A Tertiary Orthopaedic Centre

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
Z Hayat ◽  
E Kinene ◽  
S Molloy
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Time Frame ◽  
Outpatient Clinics ◽  
Contributing Factors ◽  
Time Data ◽  
Work Related ◽  
Mean Waiting Time ◽  
Appointment Time ◽  
Related Stress

Abstract Introduction Reduction of waiting times is key to delivering high quality, efficient health care. Delays experienced by patients requiring radiographs in orthopaedic outpatient clinics are well recognised. Method To establish current patient and staff satisfaction, questionnaires were circulated over a two-week period. Waiting time data was retrospectively collected including appointment time, arrival time and the time at which radiographs were taken. Results 84% (n = 16) of radiographers believed patients would be dissatisfied. However, of the 296 patients questioned, 56% (n = 165) were satisfied. Most patients (89%) felt the waiting time should be under 30 minutes. Only 36% were seen in this time frame. There was moderate negative correlation (R=-0.5); higher waiting times led to increased dissatisfaction. Mean waiting time was 00:37 and the maximum 02:48. Key contributing factors included volume of patients, staff shortages (73.7%), equipment shortages (57.9%) and incorrectly filled request forms. Eight (42.1%) had felt unwell from work related stress. Conclusions A concerted effort is needed to improve staff and patient opinion. There is scope for change post COVID. Additional training and exploring ways to avoid overburdening the department would benefit. Numerous patients were open to different days or alternative sites. Funding requirements make updating equipment, expanding the department and recruiting more staff challenging.

HOW TO REPORT AND MONITOR THE PERFORMANCE OF WAITING LIST MANAGEMENT

2002 ◽  
Vol 18 (3) ◽  
pp. 611-618
Author(s):  
Markus Torkki ◽  
Miika Linna ◽  
Seppo Seitsalo ◽  
Pekka Paavolainen
Keyword(s):  
Hallux Valgus ◽  
Waiting Time ◽  
Varicose Vein ◽  
Elective Surgery ◽  
Waiting Times ◽  
Waiting List ◽  
Mean Waiting Time ◽  
Number Of Patients ◽  
Potential Problems ◽  
Queue Discipline

Objectives: Potential problems concerning waiting list management are often monitored using mean waiting times based on empirical samples. However, the appropriateness of mean waiting time as an indicator of access can be questioned if a waiting list is not managed well, e.g., if the queue discipline is violated. This study was performed to find out about the queue discipline in waiting lists for elective surgery to reveal potential discrepancies in waiting list management. Methods: There were 1,774 waiting list patients for hallux valgus or varicose vein surgery or sterilization. The waiting time distributions of patients receiving surgery and of patients still waiting for an operation are presented in column charts. The charts are compared with two model charts. One model chart presents a high queue discipline (first in—first out) and another a poor queue discipline (random) queue. Results: There were significant differences in waiting list management across hospitals and patient categories. Examples of a poor queue discipline were found in queues for hallux valgus and varicose vein operations. Conclusions: A routine waiting list reporting should be used to guarantee the quality of waiting list management and to pinpoint potential problems in access. It is important to monitor not only the number of patients in the waiting list but also the queue discipline and the balance between demand and supply of surgical services. The purpose for this type of reporting is to ensure that the priority setting made at health policy level also works in practise.

The Study on Production Performances of Part Transporter in Flexible Manufacturing System by Using Simulation Method

2012 ◽  
Vol 576 ◽  
pp. 714-717
Author(s):  
Mohammad Iqbal ◽  
Muhammad Ridwan Andi Purnomo ◽  
Muhammad Ammar Bin Mohd Imra ◽  
Mohamed Konneh ◽  
A.N. Mustafizul Karim
Keyword(s):  
Waiting Time ◽  
Flexible Manufacturing ◽  
Material Handling ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Arrival Rate ◽  
Simulation Method ◽  
Simulation Software ◽  
Average Waiting Time ◽  
Processing Times

Material handling is one of major components in Flexible Manufacturing System (FMS). Any improvement of material handling capability is to affect the performance of the whole system. This paper discusses the simulation study on the effect of part arrival rate and dispatching rules to the average waiting time and production rate of the FMS. The facilities of the system were modeled into simulation environment by using Arena Simulation Software. The production parameters such as machine processing times, part transportation speed and type of products were put into the model to represent the behaviors of the real system. Two rules have been considered in the study, i. e. first come first served (FCFS), and shortest processing time (SPT). Average waiting time and productivity were taken into account as performance measures of the system. The result of the study showed that SPT rule gives shorter average waiting time and higher productivity. Based on this result, the SPT rules would be used to control part transporter in order to have a better performance of the FMS.

Modern Hypermarket Receiving Yard Utilization: The Implementation of a Simulation Model

2017 ◽  
Vol 2 (4) ◽  
pp. 33-39
Author(s):  
Mohammad Annas
Keyword(s):  
Simulation Model ◽  
Exponential Distribution ◽  
Poisson Distribution ◽  
Waiting Time ◽  
Arrival Rate ◽  
Jel Classification ◽  
Utilization Rate ◽  
Processing Times ◽  
Kolmogorov Smirnov ◽  
Using Data

Objective - This research is a direct observation of initial queuing, using data that is categorised into two clusters: the number of people queuing at busy hours, and processing times in the same circumstances. Methodology/Technique - The raw data was converted for use in the Poisson distribution test, as well as the Kolmogorov-Smirnov exponential distribution options. An arena simulation model was also applied to identify the vendor's waiting time and to analyse receiving yard utilization. The average waiting time according to the Poisson distribution, the average serving time per vendor by an exponential distribution, and the number of receiving yards, are all essential factors effecting the utilization of receiving yards. Findings - The study compares the length of queues, serving times, arrival rate, and time in the system using dual and single receiving yard systems. However, the utilization rate on a two receiving yards system is less than the rate on single receiving yard system. As the aim of this study is to identify the utilization rate of the receiving yard, a single receiving yard operation is more representative of modern hypermarkets, and more efficient in terms of resource efficiency. Novelty - This study depends fully on the homogeneous operating hours of the retailers' receiving yards, the type of vehicle used by vendors to unload merchandises, procedures on moving the products to the inspections phase, a generalization of the products delivered by the vendors and the size of the modern hypermarkets business itself. Type of Paper: Empirical. Keywords: Receiving Yard Utilization; Hypermarket Receiving Yard; Queuing Simulation. JEL Classification: M1, M10, M19.

Relations between queue-size and waiting-time distributions

1980 ◽  
Vol 17 (03) ◽  
pp. 822-830
Author(s):  
Masao Mori
Keyword(s):  
Waiting Time ◽  
Queue Size ◽  
Waiting Time Distributions ◽  
Mean Waiting Time

Two types of representations for relation between queue-size and waiting-time distributions are studied. By using these, an incomplete but conceptually nice generalization of Pollaczek–Khinchine formula for mean waiting time forM/G/cis obtained.

scholarly journals The influence of water intake on waiting time prior to uroflowmetry: a prospective, randomized, double-blind trial

2017 ◽  
Vol 26 (3) ◽  
pp. 212-7
Author(s):  
Nur Rasyid ◽  
Donny E. Putra ◽  
Widi Atmoko ◽  
Adianti Khadijah ◽  
Dyandra Parikesit ◽  
...  
Keyword(s):  
Water Intake ◽  
Waiting Time ◽  
Final Analysis ◽  
Study Groups ◽  
Eligibility Criteria ◽  
Double Blind ◽  
Mean Waiting Time ◽  
Influence Of Water ◽  
Superiority Trial ◽  
Male Patients

Background: In uroflowmetry examination, patients are usually instructed to intake a large volume of water and wait until the bladder is full. The association between the volume of water intake and the waiting time before uroflowmetry is unknown. The aim of this study is to investigate the relationship between the volume of water intake and the waiting time prior to uroflowmetry.Methods: This trial was designed as a randomized, researchers, caregivers and patients blinded, superiority trial with three parallel groups and primary endpoint of waiting time prior to the uroflowmetry study based on the volume of patients’ water intake. Randomization was performed by block randomization with a 1:1:1 allocation. Patients scheduled for uroflowmetry at the Urology Clinic of Cipto Mangunkusumo Hospital were enrolled from March 2013 until December 2013. The eligibility criteria were male patients with ages above 50 years and body mass index 18.5–24.9 kg/m2.Results: A total of 83 patients was randomly assigned into 3 study groups: 300 ml (28 patients), 400 ml (28 patients), and 500 ml (27 patients). All patients were included in final analysis. Mean waiting time were 85.1±59.8 min, 107.2±70.4 min, and 66±28.4 min for patients intake 300, 400, and 500 ml of water respectively (p=0.07). The final bladder volumes for three groups were statistically different (262.4±130.8 ml, 289.4±126.2 ml, 359.2±137 ml; p=0.02).Conclusion: The volume water intake of 300–500 ml did not affect waiting time before uroflowmetry. Increasing water intake at least 500 ml added the final bladder volume and shorter the waiting time.

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