scholarly journals Analysis of Landing Airplane Queue Systems at Juanda International Airport Surabaya

CAUCHY ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 49-63
Author(s):  
Yuniar Farida ◽  
Fadilah Akbar ◽  
Moh. Hafiyusholeh ◽  
Moh. Hartono
Keyword(s):  
Waiting Time ◽  
Construction Project ◽  
Queuing System ◽  
Service Performance ◽  
Queuing Model ◽  
International Airport ◽  
Flight Frequency

Juanda International Airport is currently preparing to realize the construction of terminal 3. This construction project impression that Juanda Airport is experiencing an overload, including in the airplane queue. This study aims to analyze the current queuing system at the Juanda International Airport apron, whether effective, quite effective, or less effective in serving the number of existing flights with two terminals. An analysis of the queuing system was conducted in several scenarios. They are in normal/regular condition, a scenario if there is an increase in flight frequency, and a scenario if there is a reduction in aprons’ number because of certain exceptional situations. To analyze the airplane’s landing queue at Juanda airport apron, the queuing model (M/M/51) : (FCFS/∞/∞) is used. From this model, the results show that in normal conditions, the estimated waiting time for each airplane in the system is 0.18 hours with a queue of 2 up to 3 planes/hour, categorized as effective. In one apron reduction scenario, each airplane’s estimated waiting time in the system is 0.7 hours, with a queue of 6 up to 7 planes categorized as less effective. In the scenario of additional flights, only 9 other flights are allowed every day to keep the service performance still quite effective. By obtaining this results analysis, the decision of PT. Angkasa Pura 1 (Persero) to build terminal 3 is suitable to reduce queuing time and improve Juanda International Airport services to be more effective.

Development of a Heuristics Model for Simplifying a Multi-Channel Queuing System

2011 ◽  
Vol 367 ◽  
pp. 647-652
Author(s):  
B. Kareem ◽  
A. A. Aderoba
Keyword(s):  
Waiting Time ◽  
Channel Model ◽  
Single Channel ◽  
Queuing System ◽  
Queuing Model ◽  
Average Waiting Time ◽  
Queuing Models ◽  
Significant Difference ◽  
Number Of Customers ◽  
Proportionality Principle

Queuing model has been discussed widely in literature. The structures of queuing systems are broadly divided into three namely; single, multi-channel, and mixed. Equations for solving these queuing problems vary in complexity. The most complex of them is the multi-channel queuing problem. A heuristically simplified equation based on relative comparison, using proportionality principle, of the measured effectiveness from the single and multi-channel models seems promising in solving this complex problem. In this study, six different queuing models were used from which five of them are single-channel systems while the balance is multi-channel. Equations for solving these models were identified based on their properties. Queuing models’ performance parameters were measured using relative proportionality principle from which complexity of multi-channel system was transformed to a simple linear relation of the form = . This showed that the performance obtained from single channel model has a linear relationship with corresponding to multi-channel, and is a factor which varies with the structure of queuing system. The model was tested with practical data collected on the arrival and departure of customers from a cocoa processing factory. The performances obtained based on average number of customers on line , average number of customers in the system , average waiting time in line and average waiting time in the system, under certain conditions showed no significant difference between using heuristics and analytical models.

scholarly journals Determination of Optimal Number of Servers at Network Queuing Nodes to Reduce Waiting Time in a Tertiary Institution Clinic in Bida, Nigeria

2020 ◽  
Vol 24 (9) ◽  
pp. 1631-1639
Author(s):  
I. Muhammad ◽  
L. Adamu
Keyword(s):  
Waiting Time ◽  
Current System ◽  
Optimal Number ◽  
Queuing System ◽  
Tertiary Institution ◽  
Queuing Model ◽  
Direct Observations ◽  
Expected Waiting Time ◽  
System Nodes

In this paper, a network queuing model that determines optimal numbers of servers at the nodes of the school clinic network queuing system to  reduce waiting time of the patients has been presented. The relevant data was collected for a period four weeks, through direct observations and interviews. The number of arrivals and departures were also obtained. The total expected waiting time of the patient in the current system before modification was 50minutes with total number of 10 servers in all the nodes, while the total new expected waiting time of patient in the system after modification was reduced to 19 minutes with total number of 17 servers in all the nodes. The study has determined optimal number of servers at the nodes of the school clinic network system. Results from this study is an important information to the management of the school clinic for proper planning and better service delivery. Keywords: Network Queuing System, Nodes, Servers, School Clinic.

scholarly journals Deskripsi Sistem Antrian pada Klinik Dokter Spesialis Penyakit Dalam

2012 ◽  
Vol 12 (1) ◽  
pp. 72
Author(s):  
Deiby T Salaki
Keyword(s):  
Direct Observation ◽  
Waiting Time ◽  
Arrival Rate ◽  
Queuing System ◽  
Queuing Model ◽  
Average Waiting Time ◽  
Number Of Patients

DESKRIPSI SISTEM ANTRIAN PADA KLINIK DOKTER SPESIALIS PENYAKIT DALAM ABSTRAK Penelitian ini dilakukan untuk mengetahui deskripsi sistem antrian pada klinik dokter internist. Pengumpulan data dilakukan secara langsung pada klinik dokter internist JHA selama 12 hari, selama 2 jam waktu pengamatan tiap harinya pada periode sibuk.. Model antrian yang digunakan adalah model (M/M/1) : (FIFO/~/~), tingkat kedatangan bersebaran poisson, waktu pelayanan bersebaran eksponensial, dengan jumlah pelayanan adalah seorang dokter, disiplin antrian yang digunakan adalah pasien yang pertama datang yang pertama dilayani, jumlah pelayanan dalam sistem dan ukuran populasi pada sumber masukan adalah tak berhingga.  Sistem antrian pada klinik ini memiliki kecepatan kedatangan pelayanan anamnesa rata-rata  menit 1 orang pasien datang, kecepatan kedatangan pelayanan pemeriksaan fisik rata-rata  menit 1 orang pasien datang, rata-rata waktu pelayanan anamnesa untuk  seorang pasien  menit, rata-rata waktu pelayanan pemeriksaan fisik untuk  seorang pasien  menit, peluang kesibukan  pelayanan anamnesa sebesar , peluang kesibukan  pelayanan pemeriksaan fisik sebesar , dan peluang pelayanan anamnesa menganggur sebesar , peluang pelayanan pemeriksaan fisik menganggur sebesar . Rata-rata banyaknya pengantri untuk anamnesa adalah  pasien sedangkan untuk pemeriksaan fisik  pasien, rata-rata banyaknya pengantri dalam sistem adalah  pasien, waktu rata-rata seorang pasien dalam klinik adalah  menit, waktu rata-rata seseorang pasien untuk antri adalah  menit. Kata kunci: Sistem Antrian, Klinik Penyakit Dalam  DESCRIPTION OF QUEUING SYSTEM AT THE INTERNIST CLINIC ABSTRACT This research determines the description of queuing system at the internist Clinic. Data collected by direct observation during 12 days and in 2 hours. Queuing model that used is model of (M/M/1): (FIFO /~/~). Based on the research, the clinic has 3.256 minutes per patient in average arrival rate for anamnesys, the average arrival rate for diaagnosys is 3.255 minutes per patient, average service speed for anamnesys is 2.675 minutes per patient, average service speed for diagnosys is 12.635 minutes, the probability of busy periods for anamnesys is 0.864, the probability of busy periods for diagnosys is 0.832 and probability of all free services or no patient in the anamnesys equal to 0.136, probability of all free services or no patient in the anamnesys equal to 0.168. The average number of patients in anamnesys queue is 5 patients, the average number of patients in diagnosys queue is 4 patients, the average number of patients in the system is 10 patients, the average waiting time in the system is 47.078 minutes and the average queuing time is 31.660 minutes. Keywords: Queuing system, internist clinic

scholarly journals SISTEM ANTRIAN REKAM MEDIS PASIEN DI RUMAH SAKIT MENGGUNAKAN MODEL MULTI CHANNEL DENGAN POLA POISSON

2020 ◽  
Vol 5 (2) ◽  
pp. 121-131
Author(s):  
Yeyi Gusla Nengsih
Keyword(s):  
Distribution Pattern ◽  
Waiting Time ◽  
Service Time ◽  
Medical Records ◽  
Single Phase ◽  
Service Performance ◽  
Time Data ◽  
Queuing Model ◽  
Busy Time ◽  
System L

Queue is an occurrence where someone has to wait their turn to get service. Queuing processes in hospitals when processing outpatient registration medical records at the hospital often occur especially during peak hours. To overcome this problem, a solution is needed to improve service performance at the hospital. The queuing model used is the Multi Channel-single phase queuing model which has one or more services flowing by a single queue. The variables to be observed are time between arrivals, service time data and number of services assuming a Poisson distribution pattern. The results of this study will show the operator's busy time is 83.33%, the number of queues in a certain period (Lq) is 13 patients, the number of registrants in the system (L) is 14 patients, the waiting time in the queue (Wq) is 24 minutes, and waiting time in the system (W) is 30.06 minutes.

Studying the appointment scheduling window considering patient no-show behavior in one public and one private outpatient clinics

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohsen Abdoli ◽  
Mostafa Zandieh ◽  
Sajjad Shokouhyar
Keyword(s):  
Waiting Time ◽  
Queuing System ◽  
System Capacity ◽  
Outpatient Clinics ◽  
Effective Parameters ◽  
Queuing Model ◽  
Public And Private ◽  
Content Type ◽  
Total Cost ◽  
Patient Waiting Time

Purpose This study is carried out in one public and one private health-care centers based on different probabilities of patient’s no-show rate. The present study aims to determine the optimal queuing system capacity so that the expected total cost is minimized. Design/methodology/approach In this study an M/M/1/K queuing model is used for analytical properties of optimal queuing system capacity and appointment window so that total costs of these cases could be minimized. MATLAB software version R2014a is used to code the model. Findings In this paper, the optimal queuing system capacity is determined based on the changes in effective parameters, followed by a sensitivity analysis. Total cost in public center includes the costs of patient waiting time and rejection. However, the total cost in private center includes costs of physician idle time plus costs of public center. At the end, the results for public and private centers are compared to reach a final assessment. Originality/value Today, determining the optimal queuing system capacity is one of the most central concerns of outpatient clinics. The large capacity of the queuing system leads to an increase in the patient’s waiting-time cost, and on the other hand, a small queuing system will increase the cost of patient’s rejection. The approach suggested in this paper attempts to deal with this mentioned concern.

Hubungan Budaya Pengamanan dan Kinerja Bandar Udara dengan Kepuasan Penumpang Angkutan Udara di Bandar Udara Internasional Soekarno - Hatta

WARTA ARDHIA ◽  
2013 ◽  
Vol 39 (2) ◽  
pp. 128-145
Author(s):  
Siti Masrifah
Keyword(s):  
Significant Relationship ◽  
Positive Relationship ◽  
Service Performance ◽  
Air Transport ◽  
Cultural Relations ◽  
Security Culture ◽  
International Airport ◽  
Passenger Satisfaction ◽  
Strong Positive Relationship ◽  
And Performance

This study examined the association of security culture (X1) and the performance of airport services (X2) with air transport passenger satisfaction (Y), either individually or jointly.This study with a sample of 100 respondents passenger air transport, the distribution of questionnaires to terminal 2F domestic at Soekarno Hatta International Airport Cengkareng.Calculation results show a positive and significant relationship shown in securing cultural relations (X1) to the satisfaction of passenger air transport (Y). Airport service performance (X2) to the satisfaction of passenger air transport (Y). The relation security culture and performance of airport service to the satisfaction of the air transport of passengers together.The final results of this study have a strong positive relationship between security culture in air transport passenger satisfaction. Strong positive relationship between performance of airport services in air transport passenger satisfaction. And a strong positive relationship between culture security and performance of airport services to the satisfaction of the air transport of passengers together.Penelitian ini untuk mengetahui hubungan budaya pengamanan (X1) dan kinerja pelayanan bandar udara (X2) dengan kepuasan penumpang angkutan udara (Y), baik secara sendiri-sendiri maupun secara bersama-sama. Penelitian ini dengan jumlah sampel sebanyak 100 responden penumpang angkutan udara, dengan melakukan sebaran kuesioner di terminal 2F domestik Bandar Udara International Soekarno Hatta Cengkareng. Hasil perhitungan menunjukkan hubungan yang positif dan signifikan pada budaya pengamanan (X1) dengan kepuasan penumpang angkutan, hubungan positif dan signifikan antara kinerja pelayanan bandar udara (X2) dengan kepuasan penumpang angkutan udara (Y), serta menghasilkan hubungan positif dan signifikan antara budaya pengamanan (X1) dan kinerja pelayanan Bandar udara (X2) dengan kepuasan penumpang angkutan udara (Y) secara bersama-sama. Hasil akhir penelitian ini mempunyai hubungan positif dan kuat antara budaya pengamanan dengan kepuasan penumpang angkutan udara. Hubungan positif dan kuat antara kinerja pelayanan Bandar udara dengan kepuasan penumpang angkutan udara. Hubungan positif dan kuat antara budaya pengamanan dan kinerja pelayanan Bandar udara dengan kepuasan penumpang angkutan udara secara bersama-sama. Implikasi bagi penyelenggara bandar udara dalam tercapainya kepuasan penumpang angkutan udara dengan memberikan rasa aman, lancar, tertib, dan selamat dalam suatu penerbangan, serta jasa pelayanan bandar udara dengan kebersihan terminal dan ketersediaan fasilitas yang cukup dan baik.

scholarly journals Applications of Queuing Theory in Quantitative Business Analysis

2020 ◽  
pp. 253-257
Keyword(s):  
Quantitative Analysis ◽  
Waiting Time ◽  
Queuing Theory ◽  
Single Channel ◽  
Analysis Approach ◽  
Single Server ◽  
Expected Number ◽  
Queuing Model ◽  
Infinite Population ◽  
Expected Waiting Time

Queuing Theory provides the system of applications in many sectors in life cycle. Queuing Structure and basic components determination is computed in queuing model simulation process. Distributions in Queuing Model can be extracted in quantitative analysis approach. Differences in Queuing Model Queue discipline, Single and Multiple service station with finite and infinite population is described in Quantitative analysis process. Basic expansions of probability density function, Expected waiting time in queue, Expected length of Queue, Expected size of system, probability of server being busy, and probability of system being empty conditions can be evaluated in this quantitative analysis approach. Probability of waiting ‘t’ minutes or more in queue and Expected number of customer served per busy period, Expected waiting time in System are also computed during the Analysis method. Single channel model with infinite population is used as most common case of queuing problems which involves the single channel or single server waiting line. Single Server model with finite population in test statistics provides the Relationships used in various applications like Expected time a customer spends in the system, Expected waiting time of a customer in the queue, Probability that there are n customers in the system objective case, Expected number of customers in the system

scholarly journals Analysis of queue change of visitors and performace system in the Department of Population and Civil Regristation of Semarang City

2020 ◽  
Vol 202 ◽  
pp. 15005
Author(s):  
Sugito ◽  
Alan Prahutama ◽  
Dwi Ispriyanti ◽  
Mustafid
Keyword(s):  
Customer Satisfaction ◽  
Waiting Time ◽  
Public Service ◽  
Service Time ◽  
Service Sector ◽  
General Distribution ◽  
Queuing Systems ◽  
Queuing Model ◽  
The Public ◽  
Second Period

The Population and Civil Registry Office in Semarang city is one of the public service units. In the public service sector, visitor / customer satisfaction is very important. It can be identified by the length of the queue, the longer visitors queue this results in visitor dissatisfaction with the service. Queue analysis is one of the methods in statistics to determine the distribution of queuing systems that occur within a system. In this study, a queuing analysis as divided into two periods. The first period lasts from 2-13 March 2015, while the second period lasts November 16th to December 20th 2019. The variables used are the number of visitors and the service time at each counter in intervals of 30 minutes. The results obtained are changes in the distribution and queuing model that is at counter 5/6 and counter 10. The queuing model obtained at the second perideo for the number of visitors and the time of service with a General distribution. The average number of visitors who come in 30 minute intervals in the second period is more than the first period, this indicates an increase in visitors. The opportunity for service units is still small, the waiting time in the queue is getting smaller. This shows that the performance of the queuing system at the Semarang Population and Civil Registry Office is getting better.

scholarly journals Small Queuing Restaurant Sustainable Revenue Management

2020 ◽  
Vol 12 (8) ◽  
pp. 3477
Author(s):  
Kwangji Kim ◽  
Mi-Jung Kim ◽  
Jae-Kyoon Jun
Keyword(s):  
South Korea ◽  
Revenue Management ◽  
Waiting Time ◽  
Equilibrium Points ◽  
Management Strategies ◽  
Queuing System ◽  
Utilization Rate ◽  
Trade Off ◽  
Average Waiting Time ◽  
Price Increases

When competitive small restaurants have queues in peak periods, they lack strategies to cope. However, few studies have examined small restaurants’ revenue management strategies at peak times. This research examines how such small restaurants in South Korea can improve their profitability by adapting their price increases, table mix, and the equilibrium points of the utilization rates, and reports the following findings based on the analysis of two studies. In Study 1, improving profitability by increasing prices should carefully consider the magnitude and timing. In Study 2, when implementing the table mix strategy, seat occupancy and profit also increase, and we further find the equilibrium points of the utilization rates. Under a queuing system, the utilization rate and average waiting time are also identified as having a trade-off relationship. The results provide insights into how managers of small restaurants with queues can develop efficient revenue management strategies to manage peak hours.

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