Queue is an act of joining a line to be served and it is part of our everyday human involvement. The objectives of the study focused on using a mathematical model to determine the waiting time of two selected banks as...Queue is an act of joining a line to be served and it is part of our everyday human involvement. The objectives of the study focused on using a mathematical model to determine the waiting time of two selected banks as well as compare the average waiting time between the banks. The study uncovered the extent of usage of queuing models in achieving customer satisfaction as well as permitting to make better decisions relating to potential waiting times for customers. The study adopted a case study and observational research with the source of data being primary. Purposive sampling technique was used to select the two banks under study with the target population comprising of all the customers who intended to transact businesses with the banks within the period of 11 am to 12 pm. The sample sizes for the first, second and third day of the first bank are twenty-eight (28), seventeen (17) and twenty (20) respectively with three servers on each day whereas that for the first, second and third day of the second bank is twenty (20), nine (9) and seventeen (17) with two servers on each day. A multiple server (M/M/s) Model was adopted, and Tora Software was the statistical tool used for the analysis. Findings of the study revealed that the second bank had a higher utilization factor than the first bank. Also, the number of customers in the banking hall of the second bank was higher than that of the first bank during the entire period of observation. Finally, it takes customers of the first bank lesser minutes to complete their transaction than the second bank. In conclusion, the three days observations revealed different banking situations faced by customers in both banks which had effect on waiting time of customer service. The waiting time of customer service has effect on the number of customers in the queue and system, the probability associated with the emptiness of the system and the utilization factor. Based on the results, the study recommended, <i><span>inter</span></i> <i><span>alia</span></i><span>, </span><span>that the management of the second bank should adopt a three-server (M/M/3)</span><span> model.展开更多
Minimizing time cost in time-shared operating systems is considered basic and essential task,and it is the most significant goal for the researchers who interested in CPU scheduling algorithms.Waiting time,turnaround ...Minimizing time cost in time-shared operating systems is considered basic and essential task,and it is the most significant goal for the researchers who interested in CPU scheduling algorithms.Waiting time,turnaround time,and number of context switches are themost time cost criteria used to compare between CPU scheduling algorithms.CPU scheduling algorithms are divided into non-preemptive and preemptive.RoundRobin(RR)algorithm is the most famous as it is the basis for all the algorithms used in time-sharing.In this paper,the authors proposed a novel CPU scheduling algorithm based on RR.The proposed algorithm is called Adjustable Time Slice(ATS).It reduces the time cost by taking the advantage of the low overhead of RR algorithm.In addition,ATS favors short processes allowing them to run longer time than given to long processes.The specific characteristics of each process are;its CPU execution time,weight,time slice,and number of context switches.ATS clusters the processes in groups depending on these characteristics.The traditionalRRassigns fixed time slice for each process.On the other hand,dynamic variants of RR assign time slice for each process differs from other processes.The essential difference between ATS and the other methods is that it gives a set of processes a specific time based on their similarities within the same cluster.The authors compared between ATS with five popular scheduling algorithms on nine datasets of processes.The datasets used in the comparison vary in their features.The evaluation was measured in term of time cost and the experiments showed that the proposed algorithm reduces the time cost.展开更多
文摘Queue is an act of joining a line to be served and it is part of our everyday human involvement. The objectives of the study focused on using a mathematical model to determine the waiting time of two selected banks as well as compare the average waiting time between the banks. The study uncovered the extent of usage of queuing models in achieving customer satisfaction as well as permitting to make better decisions relating to potential waiting times for customers. The study adopted a case study and observational research with the source of data being primary. Purposive sampling technique was used to select the two banks under study with the target population comprising of all the customers who intended to transact businesses with the banks within the period of 11 am to 12 pm. The sample sizes for the first, second and third day of the first bank are twenty-eight (28), seventeen (17) and twenty (20) respectively with three servers on each day whereas that for the first, second and third day of the second bank is twenty (20), nine (9) and seventeen (17) with two servers on each day. A multiple server (M/M/s) Model was adopted, and Tora Software was the statistical tool used for the analysis. Findings of the study revealed that the second bank had a higher utilization factor than the first bank. Also, the number of customers in the banking hall of the second bank was higher than that of the first bank during the entire period of observation. Finally, it takes customers of the first bank lesser minutes to complete their transaction than the second bank. In conclusion, the three days observations revealed different banking situations faced by customers in both banks which had effect on waiting time of customer service. The waiting time of customer service has effect on the number of customers in the queue and system, the probability associated with the emptiness of the system and the utilization factor. Based on the results, the study recommended, <i><span>inter</span></i> <i><span>alia</span></i><span>, </span><span>that the management of the second bank should adopt a three-server (M/M/3)</span><span> model.
基金The authors extend their appreciation to Deanship of Scientific Research at King Khalid University for funding this work through the Research Groups Project under Grant Number RGP.1/95/42.
文摘Minimizing time cost in time-shared operating systems is considered basic and essential task,and it is the most significant goal for the researchers who interested in CPU scheduling algorithms.Waiting time,turnaround time,and number of context switches are themost time cost criteria used to compare between CPU scheduling algorithms.CPU scheduling algorithms are divided into non-preemptive and preemptive.RoundRobin(RR)algorithm is the most famous as it is the basis for all the algorithms used in time-sharing.In this paper,the authors proposed a novel CPU scheduling algorithm based on RR.The proposed algorithm is called Adjustable Time Slice(ATS).It reduces the time cost by taking the advantage of the low overhead of RR algorithm.In addition,ATS favors short processes allowing them to run longer time than given to long processes.The specific characteristics of each process are;its CPU execution time,weight,time slice,and number of context switches.ATS clusters the processes in groups depending on these characteristics.The traditionalRRassigns fixed time slice for each process.On the other hand,dynamic variants of RR assign time slice for each process differs from other processes.The essential difference between ATS and the other methods is that it gives a set of processes a specific time based on their similarities within the same cluster.The authors compared between ATS with five popular scheduling algorithms on nine datasets of processes.The datasets used in the comparison vary in their features.The evaluation was measured in term of time cost and the experiments showed that the proposed algorithm reduces the time cost.
