scholarly journals Analysis of the organization of operation of engineering systems of residential buildings based on a queuing model with interruptions and close-downs

2020 ◽  
Vol 8 (4) ◽  
pp. 77-82
Author(s):  
Georgij Afanasiev
Keyword(s):  
Queuing Theory ◽  
Residential Buildings ◽  
Probabilistic Methods ◽  
Main Element ◽  
Single Server ◽  
Queuing Model ◽  
Inspection Work ◽  
Rational Organization ◽  
The One ◽  
Technical Operation

In resent years has been increasing interest for application of probabilistic methods, in particular queueing theory for estimation of activities of managing companies of residential buildings. Maintenance of residential buildings is a set of measures that ensure the highest reliability of all elements and systems of a building. The main element of the technical operation of residential buildings is a system of scheduled prophylactic inspections and repairs. However even with its rational organization, there is always a positive probability of failure of building elements, which depends not only on the aging factors of the structure. The goal of the managing company is, on the one hand, to prevent the formation of a too long queue of emergency calls, and on the other, to complete all planned prophylactic maintenance work. As a mathematical model we consider a single-server queue with vacations and close-down periods. The service team can start a scheduled preventive repair only when all request for sudden mailfunctions are satisfied. This period we call vacation. There is a close-down period before vacation. This time period is required for preparation and organization of the prophylactic and inspection work. Based on the methods of the queuing theory, the system’s characteristics that determine the quality of its work, as well as the boundaries of the change of parameters at which the system copes with the work from the standpoint of a particular criterion are defined.

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 COMMUTER LINE TICKET PURCHASE QUEUING SYSTEM IN BOGOR STATION

2017 ◽  
Vol 2 (02) ◽  
pp. 35
Author(s):  
Resista Vikaliana
Keyword(s):  
Queuing Theory ◽  
Service Use ◽  
Single Channel ◽  
Performance Measure ◽  
Queuing System ◽  
Queuing Model ◽  
The One ◽  
Multi Phase ◽  
Ticket Purchase ◽  
First In First Out

Queue is a situation that happen to people, goods, and components that need to wait to get a service. The good quality of service will satisfy the customers and decrease the queue line. Queue often happens in a station especially in weekdays. A long queue line happens in the station is one of the problems that need to be solved. Instead of manual ticket purchasing that served by the operator, today PT KAI Commuter Line also serve ticket purchasing using THB machine. The purpose of this study is to compare the performance of queuing model that happen in Bogor station locket and to determine if the queuing model is efficient by comparing the service standard, between the manual and the one that used THB machine. The method used in this research was descriptive method by using queuing theory calculation. The model of locket queuing using THB machine in Bogor Station is Multi Channel-Multi Phase, in ticket purchasing using THB machine. Besides, in the operator locket service, the queuing model is Single Channel-Single Phase. Both s ticket purchasing service use First In First Out (FIFO) disciple. The maximum amount of the queue line and the source of customers’ arrival are infinite. Based on the value of system performance can be concluded that queuing system and the service given already great and effective (based on the performance measure and probability or passengers’’ chances), passengers who are waiting to buy tickets, either manual or using machine less than 1, or assumed 1 person. From the observation, the use of THB machine decrease the queue line, but need to be socialized because passengers does not know how to use THB machine to buy ticket independently.Keywords: queue, queuing model, commuter line ticket purchasing, Bogor station

scholarly journals Transient Solution of a Single Server Queuing Model with Correlated Reneging Using Runge-Kutta Method

2020 ◽  
Vol 5 (5) ◽  
pp. 886-896
Author(s):  
Rakesh Kumar ◽  
Bhavneet Singh Soodan
Keyword(s):  
Queuing Theory ◽  
Real Life ◽  
System Size ◽  
Single Server ◽  
Kutta Method ◽  
Transient Solution ◽  
Queuing Model ◽  
Runge Kutta Method ◽  
Runge Kutta ◽  
Expected Waiting Time

In this paper, the concept of correlated reneging is introduced in queuing theory. The reneging considered so far is dependent on system size, but there are many real life situations where customers may renege due to exogenous factors other than the state of the system. Further, the reneging of customer may induce the other customers to renege at two successive time points. Such reneging is called correlated reneging. An M/M/1/K queuing model with correlated reneging is studied. Runge-Kutta method of fourth order is presented to obtain the transient solution of the model. Some performance measures like expected system size and expected waiting time in the system are studied.

scholarly journals Single Server Queuing Model to Determine the Patient Flow Process in a Township Hospital

2019 ◽  
Vol 8 (12S) ◽  
pp. 488-489
Keyword(s):  
Queuing Theory ◽  
Tamil Nadu ◽  
Patient Flow ◽  
Health Centre ◽  
Single Server ◽  
Queuing Model ◽  
Flow Process ◽  
Health Care Centre ◽  
Township Hospital ◽  
The Relationship

This research aims to analyse the use of queuing theory in the two branches of a local township health care centre located in a small township in Tamil Nadu, India. A scenario from the out-patient departments of the aforesaid centre shows the relationship between the different variables operating the system. The focus of this research is also to provide some insights for improving the efficiency of the medical centre through the queuing model. The research concludes that the Queuing system at branch A of the health centre is 93% efficient and at Branch B it is 73%efficient.

Evaluation of subway passengers transfer hub congestion based on queuing model and TOPSIS method

2021 ◽  
pp. 1-22
Author(s):  
Xiaokun Wang ◽  
Dong Ni
Keyword(s):  
Queuing Theory ◽  
Evaluation Model ◽  
Flow Characteristics ◽  
Distribution Area ◽  
Flow Density ◽  
Topsis Method ◽  
Queuing Model ◽  
Service Facility ◽  
Passenger Flow ◽  
Topsis Technique

To scientifically and reasonably evaluate and pre-warn the congestion degree of subway transfer hub, and effectively know the risk of subway passengers before the congestion time coming. We analyzed the passenger flow characteristics of various service facilities in the hub. The congested area of the subway passenger flow interchange hub is divided into queuing area and distribution area. The queuing area congestion evaluation model selects M/M/C and M/G/C based on queuing theory. The queuing model and the congestion evaluation model of the distribution area select the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. Queue length and waiting time are selected as the evaluation indicators of congestion in the queuing area, and passenger flow, passenger flow density and walking speed are selected as the evaluation indicators of congestion in the distribution area. And then, K-means cluster analysis method is used to analyze the sample data, and based on the selected evaluation indicators and the evaluation model establishes the queuing model of the queuing area and the TOPSIS model of the collection and distribution area. The standard value of the congestion level of various service facilities and the congestion level value of each service facility obtained from the evaluation are used as input to comprehensively evaluate the overall congestion degree of the subway interchange hub. Finally we take the Xi’an Road subway interchange hub in Dalian as empirical research, the data needed for congestion evaluation was obtained through field observations and questionnaires, and the congestion degree of the queue area and the distribution area at different times of the workday was evaluated, and the congestion of each service facility was evaluated. The grade value is used as input, and the TOPSIS method is used to evaluate the degree of congestion in the subway interchange hub, which is consistent with the results of passenger congestion in the questionnaire, which verifies the feasibility of the evaluation model and method.

Modeling Bus Capacity for Bus Stops Using Queuing Theory and Diffusion Approximation

2021 ◽  
pp. 036119812110302
Author(s):  
Chao Wang ◽  
Weijie Chen ◽  
Yueru Xu ◽  
Zhirui Ye
Keyword(s):  
Approximation Method ◽  
Service Time ◽  
Diffusion Approximation ◽  
Coefficient Of Variation ◽  
Queuing Theory ◽  
Queuing Model ◽  
Bus Stop ◽  
Proposed Model ◽  
Bus Stops ◽  
Bus Service

For bus service quality and line capacity, one critical influencing factor is bus stop capacity. This paper proposes a bus capacity estimation method incorporating diffusion approximation and queuing theory for individual bus stops. A concurrent queuing system between public transportation vehicles and passengers can be used to describe the scenario of a bus stop. For most of the queuing systems, the explicit distributions of basic characteristics (e.g., waiting time, queue length, and busy period) are difficult to obtain. Therefore, the diffusion approximation method was introduced to deal with this theoretical gap in this study. In this method, a continuous diffusion process was applied to estimate the discrete queuing process. The proposed model was validated using relevant data from seven bus stops. As a comparison, two common methods— Highway Capacity Manual (HCM) formula and M/M/S queuing model (i.e., Poisson arrivals, exponential distribution for bus service time, and S number of berths)—were used to estimate the capacity of the bus stop. The mean absolute percentage error (MAPE) of the diffusion approximation method is 7.12%, while the MAPEs of the HCM method and M/M/S queuing model are 16.53% and 10.23%, respectively. Therefore, the proposed model is more accurate and reliable than the others. In addition, the influences of traffic intensity, bus arrival rate, coefficient of variation of bus arrival headway, service time, coefficient of variation of service time, and the number of bus berths on the capacity of bus stops are explored by sensitivity analyses.

A spatial queuing model for the location decision of emergency medical vehicles for pandemic outbreaks: the case of Za'atari refugee camp

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Felix Blank
Keyword(s):  
Queuing Theory ◽  
Emergency Service ◽  
Performance Metrics ◽  
Service System ◽  
Refugee Camps ◽  
Location Decision ◽  
Stress Tests ◽  
Queuing Model ◽  
Content Type ◽  
Emergency Medical

PurposeRefugee camps can be severely struck by pandemics, like potential COVID-19 outbreaks, due to high population densities and often only base-level medical infrastructure. Fast responding medical systems can help to avoid spikes in infections and death rates as they allow the prompt isolation and treatment of patients. At the same time, the normal demand for emergency medical services has to be dealt with as well. The overall goal of this study is the design of an emergency service system that is appropriate for both types of demand.Design/methodology/approachA spatial hypercube queuing model (HQM) is developed that uses queuing-theory methods to determine locations for emergency medical vehicles (also called servers). Therefore, a general optimization approach is applied, and subsequently, virus outbreaks at various locations of the study areas are simulated to analyze and evaluate the solution proposed. The derived performance metrics offer insights into the behavior of the proposed emergency service system during pandemic outbreaks. The Za'atari refugee camp in Jordan is used as a case study.FindingsThe derived locations of the emergency medical system (EMS) can handle all non-virus-related emergency demands. If additional demand due to virus outbreaks is considered, the system becomes largely congested. The HQM shows that the actual congestion is highly dependent on the overall amount of outbreaks and the corresponding case numbers per outbreak. Multiple outbreaks are much harder to handle even if their cumulative average case number is lower than for one singular outbreak. Additional servers can mitigate the described effects and lead to enhanced resilience in the case of virus outbreaks and better values in all considered performance metrics.Research limitations/implicationsSome parameters that were assumed for simplification purposes as well as the overall model should be verified in future studies with the relevant designers of EMSs in refugee camps. Moreover, from a practitioners perspective, the application of the model requires, at least some, training and knowledge in the overall field of optimization and queuing theory.Practical implicationsThe model can be applied to different data sets, e.g. refugee camps or temporary shelters. The optimization model, as well as the subsequent simulation, can be used collectively or independently. It can support decision-makers in the general location decision as well as for the simulation of stress-tests, like virus outbreaks in the camp area.Originality/valueThe study addresses the research gap in an optimization-based design of emergency service systems for refugee camps. The queuing theory-based approach allows the calculation of precise (expected) performance metrics for both the optimization process and the subsequent analysis of the system. Applied to pandemic outbreaks, it allows for the simulation of the behavior of the system during stress-tests and adds a further tool for designing resilient emergency service systems.

The M/G/1 queue with negative customers

1996 ◽  
Vol 28 (02) ◽  
pp. 540-566 ◽  
Author(s):  
Peter G. Harrison ◽  
Edwige Pitel
Keyword(s):  
Iterative Solution ◽  
Single Server ◽  
Negative Customers ◽  
Poisson Arrival ◽  
First Case ◽  
Full Study ◽  
Service Times ◽  
The Stability ◽  
The One ◽  
Iterative Solution Method

We derive expressions for the generating function of the equilibrium queue length probability distribution in a single server queue with general service times and independent Poisson arrival streams of both ordinary, positive customers and negative customers which eliminate a positive customer if present. For the case of first come first served queueing discipline for the positive customers, we compare the killing strategies in which either the last customer in the queue or the one in service is removed by a negative customer. We then consider preemptive-restart with resampling last come first served queueing discipline for the positive customers, combined with the elimination of the customer in service by a negative customer—the case of elimination of the last customer yields an analysis similar to first come first served discipline for positive customers. The results show different generating functions in contrast to the case where service times are exponentially distributed. This is also reflected in the stability conditions. Incidently, this leads to a full study of the preemptive-restart with resampling last come first served case without negative customers. Finally, approaches to solving the Fredholm integral equation of the first kind which arises, for instance, in the first case are considered as well as an alternative iterative solution method.

Multi-threshold control by a single-server queuing model with a service rate depending on the amount of harvested energy

2018 ◽  
Vol 127-128 ◽  
pp. 1-20 ◽  
Author(s):  
Chesoong Kim ◽  
Sergei Dudin ◽  
Alexander Dudin ◽  
Konstantin Samouylov
Keyword(s):  
Service Rate ◽  
Single Server ◽  
Queuing Model ◽  
Threshold Control

A. Perencanaan Optimal Jumlah Petugas Pada Gerbang Tol Kuningan 2 Dengan Metode Antrian

2019 ◽  
Vol 2 (1) ◽  
pp. 1-12
Author(s):  
Doddy - Lombardo ◽  
Edward Rosyidi
Keyword(s):  
Queuing Theory ◽  
Arrival Rate ◽  
Service Level ◽  
Average Value ◽  
Planning Optimization ◽  
Level Data ◽  
Employee Scheduling ◽  
The Government ◽  
The One ◽  
Service Standards

ABSTRACTION   PT Jasa Marga (Persero), Tbk is a company engaged in the development and movement of toll roads having a Current, Safe and Comfortable Quality Policy increasingly demanded to improve the quality of its services. The number of substations that have been repaired at the Kuningan Toll Gate 2 against 4 substations cannot receive currents that increase during rush hour. The queue exceeds the service standards set by the government for a maximum of 5 vehicles for each substation. In this study used the FIFO Queue Model and Distribution testing using the Promodel 7.0 Version of Student Software to find out the distribution of arrival rate and service level data. To test the average value is used the One-way ANOVA test which was previously carried out also the test of adequacy, uniformity and normality of the data. Data collection is taken when a long queue is carried out at the Toll Gate. After passing the test, the next data ? is equal to 2,004 vehicles / hour and ? is = 417 vehicles / hour with Service Time = 8.63 seconds / vehicle, if it is done with Queuing Theory. Results Processing data with queuing theory obtained N (optimal) = 6 and n (Number of vehicles in the system) = 5 vehicles, q (Number of vehicles in queue) = 4 vehicles, d (Time of vehicle in system) = 43.37 seconds, w (Time of vehicle in queue) = 34.74 seconds. The results of data preparation are further processed to obtain optimal Employee Scheduling using tables so that there will be 3 employees in shift 1, 9 in Shift 2 and 2 in shift 3. on weekdays and 3 people on shift 1, 3 on Shift 2 and 2 on shift 3 on holidays. Keywords: Queue Method, Toll Gate, Planning, Optimization                                                                                     

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