Journal of Reliability and Statistical Studies

Optimizing Resource Allocation in M/M/1/N Queues with Feedback, Discouraged Arrivals, and Reneging for Enhanced Service Delivery

2024-04-11T13:36:04+02:00

This article presents a novel computational approach for analyzing M/M/1/N queues with feedback, discouraged arrivals, and reneging, under the first-come, first-served (FCFS) discipline. We calculate explicit transient state probabilities and represent results using symmetric tridiagonal matrix eigenvalues. Through numerical simulations, we validate our method, providing practical insights for optimizing resource allocation. Our study contributes to both theory and application, advancing queueing theory and aiding decision-makers in improving service quality and resource management.

A Review Based on Various Applications to Find a Consistent Pairwise Comparison Matrix

2024-05-03T12:55:08+02:00

Multi-criteria decision-making (MCDM) is a crucial process that provides a systematic approach to resolving numerous challenging problems encountered in everyday life. An effective method for addressing such MCDM challenges is the Analytic Hierarchy Process (AHP). Within AHP, the resolution of these problems relies on the Pairwise Comparison Matrix (PCM), a pivotal component of the decision-making framework. A fundamental aspect of AHP lies in ensuring the consistency of the comparison matrix to validate the logical perspective of the respondents. An inconsistent matrix undermines its utility as a reference for decision-making, underscoring the significance of achieving consistency in the PCM as a pivotal stage in the decision-making process. In this discourse, we delve into various methodologies aimed at deriving a refined and consistent PCM capable of replacing the original inconsistent version. To facilitate comprehension, we categorize the references based on proposed approaches and specific focal points.

Modeling Software Release Time and Software Patch Release Time Based on Testing Effort and Warranty

2024-05-08T07:36:50+02:00

In this world of software technology, our dependency on software’s is increasing continuously. As a result, software industries are working hard to develop highly reliable software and to meet the expectation of customers. Generally, software companies release software early in market to take gain market share, but rigorous software testing is required for early release software to ensure reliability of software and meet the customer’s expectations. This requires a huge amount of resources, and it increases financial burden on the company, consequently, decreases the overall profit of company. Further, late release due to prolong testing of a software may improves reliability but results into a loss of market opportunity cost or may not be fulfil the customer’s aspirations. As a result, to stay competitive, companies release software early and release patches later to fix the bugs, improve the functionality of software, and to update the software. Software industries are improving the performance or usability of software by releasing patches which may increase the consumption of testing effort and consequently increase in cost. On the other hand, software firms also provide warranty on their products. To address the above said issues, we have developed a testing effort-based software reliability growth model, which incorporates warranty policy and estimates the optimal software release and patch time with the objective to minimise the total testing cost. Further, we have used Genetic Algorithm (GA) to estimate optimum software release and patch time. A numerical illustration has been presented on a real time data set to validate the proposed model.

Incorporating Honey Badger Algorithm in Estimating Gamma Distribution With Application to Stock Price Modelling

2024-06-24T13:59:31+02:00

This study evaluates the performance of various estimation methods in stock price analysis across diverse parameters, focusing on the Honey Badger Algorithm (HBA). The purpose is to determine the most accurate and reliable method for parameter estimation. Methodologically, we analyze data spanning eight years from publicly traded Malaysian property companies, employing financial metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Our findings highlight HBA’s consistent precision in parameter estimation, with values closely aligning with initial parameters across different stock sizes. For example, HBA-Gamma model achieves an MAE of 0.0592 and an RMSE of 0.8458 for 13 stocks, demonstrating its proficiency in capturing stock price distributions in dynamic markets. In contrast, the Artificial Immune System (AIS) provides reasonable estimates but with higher variability. The Regression Method exhibits mixed outcomes, displaying accuracy in some cases but notable variability and reduced precision, especially with larger datasets. The Moment Method, while adequate, shows slightly higher variance compared to both HBA and AIS. Further analysis using Log Likelihood values confirms HBA’s superior fit to the data, consistently surpassing AIS, Regression Method, and Moment Method in likelihood maximization across various stock numbers. Specifically, HBA exhibits lower MAE and RMSE values of 0.1034 and 0.06723, respectively, for 26 stocks, further validating its effectiveness in parameter estimation and stock price prediction. These findings underscore the importance of integrated approaches that account for market nuances rather than relying solely on individual model forecasts. The results affirm HBA’s potential for informed investment decision-making, emphasizing its robust performance and enhanced predictive capabilities compared to alternative methodologies. However, further research is needed to assess the generalizability of these findings to other markets and contexts.

District-level Study of Uttar Pradesh Based on the MCDM Approach

2024-07-11T11:35:17+02:00

Development and population are two crucial and complex areas of study for the researchers. They depend on many variables such as demography, economic status, nutritional status of the child and women, etc. This research aims to determine the best districts by evaluating them against eight specific criteria that reflect the demographic composition of women and children in Uttar Pradesh (UP).

The identification of the criteria of the variables is determined by various factors such as education, security & threat, gender equality, and health dimensions within the districts of UP, India. To achieve this we attempted to implement the multiple criteria decision-making (MCDM) methods comprehensibly. This study has presented an impartial assessment of the performance of 75 districts in UP. The methodology included a technique for order preference by similarity to ideal solution (TOPSIS) and multi-objective optimization based on ratio analysis (MOORA). Data on demographic and educational parameters were collected from the most recent published report of the national family health survey (NFHS-5) and various online portals & platforms of the government of UP. Also, we made an attempt to validate the techniques using a non-parametric statistical test known as Wilcoxon sign rank test. TOPSIS and MOORA were identified as two most popular MCDM techniques for demography research. Interestingly, we found districts namely, (Agra, Kanpur Nagar, Moradabad), Lucknow and Shrawasti as outliers with respect to variables area(A3)
, CAW(A5) and TFR(A6) respectively that need to be dealt with careful attention and effective measures has to be taken. The study provides useful information on the demographic characteristics of districts in UP and possibly provide the basis to our policymakers for designing the targeted interventions to improve the social and economic indicators of the State.

A Study on One-Parameter Entropy-Transformed Exponential Distribution and Its Application

2024-02-29T06:26:38+01:00

This study presents the Entropy-Transformed Exponential Distribution (EnTrED), in an attempt to enhancing the flexibility and applicability of the traditional exponential distribution. The study explores the statistical properties of the EnTrED, including mode, quantile function, reliability, moments, and hazard function. The parameters of the distribution were estimated using maximum likelihood estimation, and the stability of these estimates was thoroughly evaluated through extensive Monte Carlo simulation. The simulation results demonstrated that the maximum likelihood estimates of the model parameters were well-behaved. Additionally, Empirical assessments against alternative distributions underscores the robustness of the EnTrED as a superior model for analyzing life data.

Analysis of EAC Using Multiple Regression and Conditional Process: A Statistical Approach

2024-04-13T19:56:14+02:00

In the modern world of increased competition, evolving technologies and volatile environment every institution must poise themselves to innovate and change, not only to prosper but merely to survive. The situation is more acute in institutions of higher education in India where things are changing with unprecedented speed. The survival of the institutions will depend upon the adaptability of their employees to embrace the change. However, employees’ acceptance to change cannot be developed overnight and also in isolation. It requires intensity and persistency of efforts in the right direction from the management. In the present paper, the researchers have identified the role of emotional intelligence and locus of control in enhancing employees’ acceptance to change in HEIs, Uttarakhand using multiple regression and conditional process analysis. The data for the present study were collected using stratified sampling and structured questionnaires from 432 employees from various HEIs in Uttarakhand. The finding revealed that EI enhances the EAC in HEI. Further, it also revealed that Job Satisfaction acts as a partial mediator in the relation between EI and EAC.

Enhancing Entrepreneurial Orientation among Women: A Multi Regression Approach

2024-06-17T20:31:06+02:00

The survival, growth and prosperity of any region depend upon its economy. While the government undertakes every step it can take to boost the economy of a region, it cannot uplift the economy alone. The entrepreneurs play a pivotal role in assisting the government in completing this Herculean task. The contribution of entrepreneurs is even more significant in small states like Uttarakhand, which is still in its nascent state after being carved out in 2000 as the 27th state of India. The hilly terrains, remoteness from the center, and extreme climate all contribute to making its economy more contingent upon the entrepreneurial ventures of the indigenous people. While there is a surge of entrepreneurial activities in urban areas close to the plains, there is a dearth of such endeavors in the hilly areas. The situation is graver in the rural areas of the hilly region, especially for women as most of them lack awareness, resources and support and are primarily engaged in agricultural and husbandry activities. A paradigm change is required for the inclusion of such women for the holistic and heuristic development of the economy. This demands a shift from an agrarian to an entrepreneurial civilization. However, entrepreneurial orientation cannot be developed overnight and in isolation. It requires the instillation of personality characteristics. In the present paper, the researchers have identified three personality variables: – self-esteem, self-efficacy, and locus of control as the antecedents of entrepreneurial orientation and, using multiple regression, have explored the contribution of these personality variables to entrepreneurial orientation among women in the hilly region of Uttarakhand. The data for the present study were collected using stratified sampling and structured questionnaires from 200 women in the age group of 18 years to 40 years. The finding revealed that the personality variables accounted for 66.5 % of the variation in entrepreneurial orientation. Further, it also revealed that locus of control (LOC) contributed most to the development of entrepreneurial orientation (EO), followed by self-efficacy and self-esteem.

Design of Control Charts Using Repetitive Sampling: A Comparative Study of Conditional Expected Delay

2024-07-02T09:00:06+02:00

Repetitive sampling is a valuable technique in statistical quality control, especially when industrial engineers face uncertainty with initial sample information. This study aims to develop a Conditional Expected Delay (CED) metric, focusing on scenarios without false alarms prior to a process shift, by using repetitive sampling for control charts. Additionally, we will evaluate the performance of control charts with repetitive sampling against traditional EWMA control charts in terms of CED, considering various smoothing constants and shift values. Our results demonstrate that control charts using repetitive sampling consistently outperform EWMA control charts. Therefore, based on our comprehensive analysis, we conclude that control charts with repetitive sampling are more efficient and effective than EWMA control charts.