A Theatre Attendance Model

2017 ◽  
Vol 6 (3) ◽  
pp. 23-38
Author(s):  
Michele Bisceglia
Keyword(s):  
Utility Function ◽  
Waiting Time ◽  
Arrival Time ◽  
Inductive Reasoning ◽  
Free Entry ◽  
Previous Round ◽  
Agents Heterogeneity

In this manuscript, the author proposes a model that constitutes a generalization of the El Farol Bar problem. In this model, in each period, each one of the n agents decides the arrival time at a theatre with free entry in which there are k (k<n) seats. Each individual wants to minimize the waiting time (before the beginning of the show) but prefers to assist to the show comfortably seated. The author introduces a utility function that takes into account these aspects, in which also agents' heterogeneity, in terms of different patience or comfort preferences, is considered. The author examines some possible approaches to this problem, and provides a new inductive reasoning modeling for a simplified version of this Theatre Attendance model, according to which each agent decides the arrival time at the theatre in a certain period by looking at the outcome of the previous round.

scholarly journals Simulasi Penentuan Optimasi Arah Taksi Kosong Dengan Gps Dan Metode Monte Carlo

2010 ◽  
Vol 22 (2) ◽  
pp. 214-228
Author(s):  
Sabungan Halomoan Hutapea ◽  
Rosita Sinaga
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Model ◽  
Waiting Time ◽  
Arrival Time ◽  
Taxi Driver ◽  
Coordinate Position ◽  
Position Data ◽  
The Right ◽  
Monte Carlo Simulation Model

The development of metropolitan cihJ as Jakarta for years is high, thus the demand Jo transportationservices. Tcui is one of mode transportation which is mostly used by middle to high class people. Theproblem is, sometimes many people are wasting time for waiting empty taxi, mean while manyempty taxi, are searching for the passenger.The objective of this simulation model is to direct the taxi, movement to get passenger with the help ofi~b "based software. So then, there is information to direct the taxi, mavement to choose the right Wat}to get passenger with high possibilihJ rate of passenger.The taxi, driver is equipped by GPS that will find out the coordinate position data of geographical. Toinput the data such as position of start-finish and arrival time of passenger it can be done by cellμlarphone then data will be transmit to ~bsite.Using simulation approaching, a Monte Carlo simulation model can be build that will optimize taxi,driver to find passenger. This modeling will be able to estimate the possibilihj of passenger arrival anddistribution of taxi, cars in one area. The comparison of simulation without modeling resuled intincreament of some aspects, such as increment of delivetJ services increase to 81.48%, searching timeand waiting time become 4.12% and 6.45% respectively.Keyword: taxi, movement, waiting time, travel time, Monte-Carlo Simulation

scholarly journals Teknik Penjadwalan Prosesor FIFO, SJF Non Preempetive, Round Robin

2019 ◽  
Vol 1 ◽  
pp. 864
Author(s):  
Ikhsan Parinduri ◽  
Siti Nurhabibah Hutagalung
Keyword(s):  
Waiting Time ◽  
Arrival Time ◽  
Round Robin ◽  
Main Menu ◽  
Start Time ◽  
Processor Scheduling ◽  
Time Value ◽  
Calculation Process ◽  
Burst Time ◽  
Made In

Processor scheduling is divided into several methods including FIFO, Non Preempetive SJF, Round Robin. Implementation can know the performance of the processor which consists of process, waiting time, arrival time and completion stage. In this case the processor scheduling is made in NetBeans IDE.7.0.1 programming with input on the main menu and calculation process display menu with AWT (Average Waiting Time) value with units of ms, table display process: process, burst time and gaint chart : process, waiting time, start time and end time.

scholarly journals Design of Optimal Scheduler for Process Scheduling

2020 ◽  
Vol 9 (3) ◽  
pp. 3759-3761
Keyword(s):  
Operating Systems ◽  
Waiting Time ◽  
Arrival Time ◽  
Turnaround Time ◽  
Process Scheduling ◽  
Cpu Scheduling ◽  
Cpu Utilization ◽  
Optimal Scheduler

CPU Scheduling takes plays an important role in multiprogramming systems. There are several programs present in memory. It is the responsibility of operating systems to select the process and assign it to CPU. There are various algorithms available for CPU Scheduling. The algorithm’s performance depends on various factors like arrival time, priority etc. This paper helps to select the best algorithm by comparing various algorithms under the same condition and analyzed them based on various factors like waiting time, turnaround time, CPU utilization, Throughput.

The Study of Train Operation Plan of Night Train on Beijing-Guangzhou High-Speed Railway under Segmented Rectangular Maintenance Time Window

2014 ◽  
Vol 505-506 ◽  
pp. 619-623
Author(s):  
Hao Jia ◽  
Bao Ming Han ◽  
Qi Zhang
Keyword(s):  
Waiting Time ◽  
High Speed ◽  
Arrival Time ◽  
Time Window ◽  
High Speed Railway ◽  
Maintenance Time ◽  
Train Operation

Considering the demand of night train on Beijing-Guangzhou high-speed railway, two modes of segmented rectangular maintenance time window are discussed. Methods to make train operation plans under these two modes are proposed, aiming at shortening the train waiting time and making the departure and arrival time more reasonable. Comparing the new designed train operation plans with the original one under the vertical rectangular maintenance time window, it shows that segmented rectangular maintenance time window is better in night train operation.

On exponential bounds for the waiting-time distribution function in GI/G/1

1976 ◽  
Vol 13 (2) ◽  
pp. 411-417 ◽  
Author(s):  
R. Bergmann ◽  
D. Stoyan
Keyword(s):  
Distribution Function ◽  
Waiting Time ◽  
Time Distribution ◽  
Arrival Time ◽  
Distribution Functions ◽  
Integral Inequalities ◽  
Arrival Time Distribution ◽  
Waiting Time Distribution ◽  
Exponential Bounds ◽  
Stationary Waiting Time

Exponential bounds for the stationary waiting-time distribution of the type ae–θt are considered. These bounds are obtained by the use of Kingman's method of ‘integral inequalities’. Approximations of Θ and a are given which are useful especially if the service and/or inter-arrival time distribution functions are NBUE or NWUE.

scholarly journals Patient waiting time and satisfaction in GP clinic at a tertiary hospital in Thailand

2018 ◽  
Vol 192 ◽  
pp. 01034
Author(s):  
Apichart Boonma ◽  
Kanchana Sethanan ◽  
Sukangkana Talangkun ◽  
Teerawat Laonapakul
Keyword(s):  
Standard Deviation ◽  
Waiting Time ◽  
Qualitative Data ◽  
Arrival Time ◽  
Tertiary Hospital ◽  
Satisfaction Survey ◽  
Patient Satisfaction Survey ◽  
Patient Waiting Time ◽  
General Practice Clinic ◽  
Patient Waiting

This study aims to investigate patient waiting time in General Practice Clinic and identify the main cause of the problem through patient satisfaction survey. 9,232 Patients were randomly observed and quantitative data such as arrival time and waiting time were recorded. Other 1,200 patients were randomly selected for satisfaction survey which is considered as qualitative data. The result indicated that average waiting time of patients was 175.87 minutes with standard deviation of 68.66 minutes. Waiting time at GP clinic appeared to be the longest with an average of 92.9 minutes and standard deviation of 43.4 minutes. Also, based on the obtained data, patients remained positive until their waiting time exceeded 2 hours.

scholarly journals Rearing, bird type and pre-slaughter transport conditions I. Effect on dead on arrival

2018 ◽  
Vol 16 (2) ◽  
pp. e0503 ◽  
Author(s):  
Morris Villarroel ◽  
Fernando Pomares ◽  
Miguel A. Ibáñez ◽  
Almudena Lage ◽  
Paula Martínez-Guijarro ◽  
...  
Keyword(s):  
Wind Speed ◽  
Waiting Time ◽  
Arrival Time ◽  
Daily Maximum ◽  
Transport Distance ◽  
Broiler Production ◽  
High Incidence ◽  
Commercial Broiler ◽  
One Year ◽  
Bird Weight

The transport of broilers to slaughter normally results in a small percentage of dead on arrival (DoA) but little is known about the effects of flock thinning or bird weight. A multivariable linear model was used to analyse the incidence of DoA over one year in 1,856 flocks of Ross broilers (9,188 shipments). Each flock was categorized according to broiler type (yellow-skinned females and males, white-skinned females and males and roaster females) and thinning (birds transported after thinning, remaining birds after thinning and non-thinned flocks), in addition to transport distance, waiting time, maximum and minimum daily temperatures, precipitation and maximum wind speed. The overall percentage of DoA was 0.187%. The effect of the daily maximum outside temperature on DoA was quadratic with minimum DoA at 21.5ºC. Arrival time to the slaughterhouse and waiting time increased DoA by 0.0044% and 0.0021% for every 60 min increase, respectively. DoA were higher in males (which were heavier than females), and in the flocks that were previously thinned. An interaction between thinning and bird type was found, so that DoA were higher in previously thinned flocks of male broilers and roaster females. Despite the high incidence of thinning and larger bird weight, the percentage of DoA was comparable to previous studies. This research provides one of the largest detailed analyses of DoA in commercial broiler production in the Iberian Peninsula. The models described allow to quantify how increases in temperature, transport distance, waiting time, bird weight and the practise of thinning can all increase broiler mortality.

On the virtual waiting time in an M/G/1 retrial queue

1991 ◽  
Vol 28 (02) ◽  
pp. 446-460 ◽  
Author(s):  
G. Falin ◽  
C. Fricker
Keyword(s):  
Waiting Time ◽  
Arrival Time ◽  
Retrial Queue ◽  
Single Server ◽  
Random Delay ◽  
Single Server Queue ◽  
Virtual Waiting Time ◽  
Server Queue ◽  
First Time ◽  
Do So

This paper deals with the stationary distribution of the virtual waiting time, i.e. the time between the arrival and the beginning of service of a customer in a single-server queue that operates as follows. If the server is busy at an arrival time, the customer is rejected. This customer attempts service again after some random delay and continues to do so until the first time at which the server is idle. At this time, the customer is served and leaves the system after service completion. Interarrival times and delays are assumed to be two independent sequences of i.i.d. exponentially distributed random variables. Service times are also i.i.d., generally distributed, and independent of the previous sequences.

On the virtual waiting time in an M/G/1 retrial queue

1991 ◽  
Vol 28 (2) ◽  
pp. 446-460 ◽  
Author(s):  
G. Falin ◽  
C. Fricker
Keyword(s):  
Waiting Time ◽  
Arrival Time ◽  
Retrial Queue ◽  
Single Server ◽  
Random Delay ◽  
Single Server Queue ◽  
Virtual Waiting Time ◽  
Server Queue ◽  
First Time ◽  
Do So

This paper deals with the stationary distribution of the virtual waiting time, i.e. the time between the arrival and the beginning of service of a customer in a single-server queue that operates as follows. If the server is busy at an arrival time, the customer is rejected. This customer attempts service again after some random delay and continues to do so until the first time at which the server is idle. At this time, the customer is served and leaves the system after service completion. Interarrival times and delays are assumed to be two independent sequences of i.i.d. exponentially distributed random variables. Service times are also i.i.d., generally distributed, and independent of the previous sequences.

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