International Journal of Research in Industrial Engineering
Volume:12 Issue: 3, Summer 2023

Nowadays, industrial businesses are more aware of the value of machine maintenance, and more especially, the adoption of an effective maintenance strategy. Total Productive Maintenance (TPM), which involves everyday chores involving the entire workforce, increases equipment efficiency, prevents breakdowns, and promotes autonomous operator maintenance. TPM is a fantastic technique for maintaining buildings and machines. This article provides research and a review of TPM implementation in an RMG Industry to help enhance Overall Equipment Effectiveness (OEE). Data from the past have been studied, and the findings obtained in terms of motivated employees, improved OEE, and a decrease in the number of rejects/accidents on the production line are fairly positive. The methodology calls for gradually applying lean principles, Autonomous Maintenance (AM), 5S, and planned maintenance. After TPM deployment on the critical machine, improvements in availability, performance, and quality are seen boosting the overall efficacy of the equipment. A comparison of OEE before and after implementation demonstrates the effectiveness of TPM deployment throughout the industry.

This research paper presents a comprehensive SWOT analysis of ChatGPT in healthcare, examining its strengths, weaknesses, opportunities, and threats. The paper highlights the potential benefits of ChatGPT, such as improved patient engagement and support for medical education, as well as its limitations, including the risk of inaccurate data and inability to summarize non-text reports. The paper also identifies opportunities for ChatGPT, such as enabling personalized healthcare delivery and supporting remote patient monitoring. However, the paper also highlights potential threats, such as self-treatment among patients and the risk of an AI-driven infodemic. The significance of this research paper lies in its valuable insights into the ethical and safe use of ChatGPT in healthcare, providing healthcare professionals and policymakers with important considerations for its use. The SWOT analysis also serves as a framework for future research and development of ChatGPT and other large language models in healthcare. This research paper is a significant contribution to the ongoing discussion on the use of ChatGPT in healthcare and its potential impact on patient care and public health.

Investigating stock price trends and determining future stock prices have become focal points for researchers within the finance sector. However, predicting stock price trends is a complex task due to the multitude of influencing factors. Consequently, there has been a growing interest in developing more precise and heuristic models and methods for stock price prediction in recent years. This study aims to assess the effectiveness of technical indicators for stock price prediction, including closing price, lowest price, highest price, and the exponential moving average method. To thoroughly analyze the relationship between these technical indicators and stock prices over predefined time intervals, we employ an Artificial Neural Network (ANN). This ANN is optimized using a combination of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Harmony Search (HS) algorithms as meta-heuristic techniques for enhancing stock price prediction. The GA is employed for selecting the most suitable optimization indicators. In addition to indicator selection, PSO and HS are utilized to fine-tune the Neural Network (NN), minimizing network errors and optimizing weights and the number of hidden layers simultaneously. We employ eight estimation criteria for error assessment to evaluate the proposed model's performance and select the best model based on error criteria. An innovative aspect of this research involves testing market efficiency and identifying the most significant companies in Iran as the statistical population. The experimental results clearly indicate that a hybrid ANN-HS algorithm outperforms other algorithms regarding stock price prediction accuracy. Finally, we conduct run tests, a non-parametric test, to evaluate the Efficient Market Hypothesis (EMH) in its weak form.

In this paper, the modeling of a closed-loop supply chain problem is discussed concerning economic and environmental aspects. The considered supply chain simultaneously makes strategic and tactical decisions, such as locating potential facilities, optimal allocation of product flow, and determining the optimal level of discount. Since the presented model is an NP-Hard model, MOPSO and SPEA II algorithms have been used to solve the problem. For this purpose, a priority-based encoding is presented, and the Pareto front resulting from solving different problems is compared. The results show that the MOPSO algorithm has obtained the most significant number of Pareto solutions in the large size. In contrast, the SPEA algorithm has included more Pareto solutions in the small and medium sizes. This is despite the fact that in different sizes, the MOPSO algorithm has the lowest calculation time among all algorithms. Also, according to the results obtained from the TOPSIS method, it was observed that the MOPSO algorithm in small and medium sizes and the SPEA2 algorithm in larger sizes have better performance than other proposed algorithms.

Selecting appropriate locations for Municipal Solid Waste (MSW) management facilities, such as landfills, is an important issue in rapidly developing regions. Multiple alternatives and evaluation attributes need to be analyzed to finalize the locations of these facilities. The selection of a landfill site in an urban area is a critical issue due to the involvement of many parameters. The decisive parameters are environmental, economic, and social, some of them conflicting, making landfill site selection a tedious and complex process. Multi Attribute Decision Making (MADM) approaches are found to be very effective for ranking several potential locations and, hence, selecting the best among them based on the identified attributes. Therefore, this study presents a two-stage MADM model that also accounts for all possible combinations of locations. This study evaluates economic, environmental, social, and technical attributes based on realistic conditions. Based on the results, 15 attributes are first identified through a comprehensive literature review and with the help of municipal officials during field surveys. These attributes are categorized into four types, i.e., economic, technical, environmental, and social, based on their respective propensity.In the second step, a statistical analysis questionnaire was distributed among the study population, and Cronbach's alpha was explained for all four main factors of the study. Therefore, in the last step, the rank of all research variables was calculated using the Nonlinear analysis method. Based on the results of this study, the technical variable was ranked first, the economic variable was ranked second, and the environmental and social variable was ranked third. This article has three theoretical, practical, and technical contributions. Also, this article provides a clear explanation of the theoretical contribution related to the accumulated knowledge, both in the introduction and theoretical background sections of the article. Therefore, studying the past research describes a relatively complete background of the planned theoretical contributions of this article compared to the previous research. Therefore, the theoretical contribution of this article solves the scientific gap about effective indicators for determining the location of waste disposal. From the point of view of practical contribution, this article presents practical concepts related to managers and experts and has practical suggestions presented in the conclusion section. Also, the technical contribution of this article is presented by combining fuzzy logic and Nonlinear mathematical programming.

The corporate landscape is highly affected by two market factors, namely digitalization and sustainability. These two driving forces have been the topic of several studies on how they change management methods, businesses, and society in general. However, the point that these two trends meet each other has been mostly neglected by research studies. Modern organizations and corporations are dealing with the adopting digital transformation issue as a new strategic paradigm. The present study attempts to elaborate on the relationship between digital transformation and sustainability. Therefore, through an in-depth review of the relevant literature, critical factors and their indicators were determined, and based on the proposed conceptual model, six hypotheses were developed. Then, a questionnaire was designed and distributed among 120 Iranian experts, managers, and consultants, and 97 complete questionnaires were returned. Reliability, Content Validity Ratio (CVR), and Content Validity Index (CVI) of the questionnaire were calculated, and the hypotheses were tested through Structural Equation Modeling (SEM) using SmartPLS Software. The results showed that digital transformation significantly affected an organization's sustainability aspects through operation, customer, business model, technology, workforce, and collaboration. Digital transformation and sustainability should constitute integral parts of organizational strategy. Considering that business practices affect the environment, society, and economy, digital transformation can influence the business sustainability. Digital technologies transform markets and create novel paradigms in the industry. In addition, they present new solutions to organizations to cope with sustainability issues. Due to this importance for organizations as consumers and other stakeholders, they are sensitive to the effects of business on brand value, revenues, and company valuation.

The Hungarian Algorithm is the most famous method for solving Linear Assignment Problems (LAP). Linear Assignment Method (LAM), as an application of LAP, is among the most popular approaches for solving Multi Criteria Decision Making (MCDM) problems. LAM assigns a priority to each alternative based on a Decision Matrix (DM). The elements of the DM are often deterministic in MCDM. However, in the real world, the value of the elements of the DM might not be specified precisely. Hence, using interval grey numbers as the value of the DM to consider the uncertainty is reasonable. In this research, for providing a real circumstance, the classic Hungarian algorithm has been extended by using the concept of grey preference degree as the Grey Hungarian Algorithm (GHA) to solve LAM under uncertainty. To verify the proposed GHA, a real case for ranking several items of mining machinery warehouse from Sarcheshmeh Copper Complex has been solved by the GHA. Also, the same case study has been prioritized by two other Grey TOPSIS and Grey VIKOR. The results of all mentioned approaches are identical, showing the validity of the proposed GHA developed in this research.