Journal of Applied Research on Industrial Engineering
Volume:11 Issue: 1, Winter 2024

Ghana is currently grappling with a formidable electronic waste (e-waste) challenge that poses significant threats to both the ecosystem and human well-being. This study delves into the e-waste management and recycling practices within a crucial sector of the industry, aiming to shed light on the issue and formulate comprehensive strategies for policy implementation and recycling initiatives in the Ho municipality, the regional hub of the Volta region, as well as in Ghana as a whole. Between March and May of 2022, a meticulous sampling process identified 150 electronic service technicians for participation. The data gathered was processed and analyzed using SPSS v16. The analytical techniques employed included one sample T-test, Likert scale assessments, and binary logistic regression. The results unveiled a significant gap in knowledge among repairers regarding government laws and e-waste disposal protocols. Qualifications emerged as a noteworthy factor influencing awareness levels (5.066, 95% CI: 1.098–3.860, p = 0.024, <0.05). While respondents generally acknowledged the environmental impact of e-waste, they exhibited limited awareness concerning the hazardous substances contained within it. Notably, two predominant e-waste management strategies surfaced: storage and eventual sale to scrap dealers. Regarding recycling methodologies, a majority (52%) expressed a preference for a nominal fee-based approach. These gaps highlight the need for stakeholders to publicize appropriate methods to recycle e-waste and the associated legal framework across all members of the Ghana Electronic Service Technicians' Association (GESTA) social media platforms in conjunction with local government assemblies to reshape repairers' perceptions of e-waste and increase environmental awareness, aligning with the Sustainable Development Goals (SDGs).

The purpose of the study was to develop a framework utilizing the Constant Returns to Scale (CCR) model of Data Envelopment Analysis (DEA) to evaluate the performance of workers and ergonomic risk and identify their postural models from efficient frontiers. Surface Electromyography (EMG) data and upper limb joint angle data were collected from volunteers (Decision-Making Units (DMUs) to carry out the DEA analysis. The data was collected for both maximum voluntary isometric contractions (MVC) and simple dynamic exercises. The DEA analysis was performed in several phases, including problem formulation and Single-Input-Multiple-Output (SIMO) model analysis. The study used muscle activation levels and upper limb joint angles to evaluate the ergonomic risks and performance of workers and identify role models for typical workers to follow. The study found that incorporating kinematics and EMG data into the DEA model's CCR framework identified efficient frontiers for workers who exhibit less muscle activation and use optimal arm angles while performing their work. The study also showed that workers can learn from their role models who exhibit efficient techniques, including the appropriate arm angle for performing a particular task, to improve their own efficiency. By following these superior work procedures, workers can increase their efficiency, reduce the risk of musculoskeletal problems, and enhance their output. The study concluded that the DEA framework utilizing the CCR model, combined with kinematics and EMG data, can assist in determining the performance of workers and best practices for workers to improve their performance and reduce ergonomic risk.

In this paper, a hybrid method based on a linear programming model for solving Multi-Attribute Decision-Making (MADM) problems by combining two new methods, the COmplex PRoportional ASsessment (COPRAS) and the Multi-Objective Optimization Ratio Analysis (MOORA) and also using the concept of discrimination intensity functions are presented. Further interaction with the Decision Maker (DM) to determine the weights of the attributes and calculate the weights by solving a linear programming problem without determining the predetermined weight are two of the advantages of the new method. In the proposed method, for each alternative, attributes are weighted with optimism for that alternative, and then alternatives are ranked through efficiency intervals. The proposed method is implemented on a real-world problem derived from the subject literature and compared with other MADM methods. The difference in the final results is evident due to the consideration of more details in determining the rankings.

This study aims to verify the main factors influencing turnover intention in the Iran hospitality industry. The objective of this study is to construct a fuzzy AHP and fuzzy TOPSIS model to evaluate the dimensions of the hotel employee turnover intention model. The performance evaluation for employee turnover intention includes work itself, supervision, coworkers relationship, salary and benefit, career opportunities, job stress, perceived risk, and job insecurity. These dimensions generate a final evaluation for ranking priority among the employee turnover intention of the proposed model. The importance of dimensions is evaluated by 20 experts, and decision-making is processed through the fuzzy concept and fuzzy environment. From the critical fuzzy AHP and fuzzy TOPSIS analysis results, the study shows that the most important dimensions of employee turnover intention in the hotel industry model are salary and benefits. Moreover, the results indicate that the least important dimensions are the Co-workers Relationship, Supervision, and Career Opportunities. The second group dimensions that impact employee turnover in the context of the COVID-19 epidemic are work itself, job stress perceived risk, and job insecurity. In addition, this study’s results show that three-star hotels have the highest value of turnover intention; the second is the Four and Five-star hotels, and the third is the below three-star hotels. The results of the study will help businesses in the field of hospitality have a more comprehensive view of human resource management activities. Especially, this study provides implications for hotel managers in understanding employee behavior and their turnover intention during the context of the COVID-19 epidemic based on the eight proposed dimensions.

The temperature has been a highly discussed issue in climate change. Predicting it plays an essential role in human affairs and lives. It is a challenging task to provide an accurate prediction of air temperature because of its complex and chaotic nature. This issue has drawn attention to utilizing the advances in modelling capabilities. ARIMA is a popular model for describing the underlying stochastic structure of available data. Artificial Neural Networks (ANNs) can also be appropriate alternatives. In the literature, forecasting the temperature of Tehran using both techniques has not been presented so far. Therefore, this article focuses on modelling air temperatures in the Tehran metropolis and then forecasting for twelve months by comparing ANN with ARIMA. Particle Swarm Optimization (PSO) can help deal with complex problems. However, its potential for improving the performance of forecasting methods has been neglected in the literature. Thus, improving the accuracy of ANN using PSO is investigated as well. After evaluations, applying the seasonal ARIMA model is recommended. Moreover, the improved ANN by PSO outperforms the pure ANN in predicting air temperature.

The present article deals with the application of the Data Envelopment Analysis (DEA) methodology to identify the most weighty factors that are associated with student performance on large-scale assessments, amongst them, the permanent assessment system for basic education "SPAECE" test. The DEA Slacks-Based-Measure (SBM) model was used to estimate the relative efficiency of school units in the city of Sobral (CE), one of the most prominent Brazilian counties in the educational scenario. It was evident that the presence of libraries, computer labs, sports courts and rooms for special care in school units constitutes a significant factor associated with the high performance of students, impacting, therefore, school efficiency.

We developed a DEA-based resource re-allocation model based on environmental DEA technology for organizations with a central decision-making environment. The proposed model considered a weak disposability axiom for undesirable outputs and combined Data Envelopment Analysis (DEA) with Multiple-Objective Programing (MOP). The objective was to find the appropriate re-allocation model in order to save energy and reduce environmental pollution, so that the next steps could be taken toward improvement. Given that reducing the inputs and outputs of inefficient units is sometimes not achievable and does not seem logical, for the reduction in the values to be logical and achievable, we divided the Decision-Making Units (DMUs) into different levels of efficient frontier using the context-dependent DEA technique. For this purpose, the model was designed to move the DMUs from the current frontier to the efficient frontier of the previous layer, which has better efficiency conditions, or keep them on their own frontier. In addition, the opinion of the central decision maker regarding the amount of reduction in the inputs and outputs was expressed using Goal Programing (GP) in a way that does not make the model infeasible. By implementing the model in 8 regions of the world, suggestions were made regarding the amounts of energy saving and CO2 pollution reduction based on the conditions determined by the central decision maker aiming improve the efficiency of inefficient units in the next step.

One of the most critical aspects of credit risk management is determining the capital requirement to cover the credit risk in a bank loan portfolio. This paper discusses how the credit risk of a loan portfolio can be obtained by the stochastic recovery rate based on two approaches: beta distribution and short interest rates. The capital required to cover the credit risk is achieved through the Vasicek model. Also, the Black-Scholes Merton model for the European call option is utilized to quantify the Probability of Default (PD). Value at Risk (VaR) and Conditional Value at Risk (CVaR) are used as measures of risk to evaluate the level of risk obtained by the worst-case PD. A stochastic recovery rate calculates VaR related to the underlying intensity default. In addition, the intensity default process is assumed to be linear in the short-term interest rate, driven by a CIR process. The loan portfolio performance is evaluated by considering the relevant characteristics with the Data Envelopment Analysis (DEA) method. This study proposes the losses driven by the stochastic recovery rate and default probability. The empirical investigation uses the Black-Sholes-Merton model to measure the PD of eighth stocks from different industries of the Iran stock exchange market.

A system's approach depends on the low malfunction of the equipment and processes of that system, and maintenance plays an essential role in achieving this goal. In addition, over time, the equipment quality decreases, and a quality transfer from controlled to uncontrolled mode may occur, characterized by an increase in the rate of return of the product and the tendency to fail. One of the methods that researchers have widely used in analyzing the risk of net operations is the analysis of the effect and failure modes to identify critical failure modes and focus planning and net resources on them. In analyzing the effect and failure modes, one of the essential steps is prioritizing the equipment to determine the critical equipment, as well as determining the fundamental failure modes and prioritizing them to plan the net operation purposefully. This paper aims to dynamically rank equipment in intuitionistic fuzzy environments with interval values ​​to identify and prioritize critical equipment and present a mathematical model for combining optimization of preventive maintenance intervals and control parameters. For this purpose, a model is presented that calculates the dynamic weights of each piece of equipment according to the conditions of each piece of equipment in the indicators of failure probability, failure consequence, and lack of fault detection power. Therefore, dynamic ranking is provided for the equipment. In this research, for dynamic prioritization of equipment, the method of analysis of the ratio of intuitionistic fuzzy gradual weighting with quantitative values ​​(IVIF-SWARA) was presented. Then, a mathematical model was presented for the identified critical equipment. The proposed model can determine the optimal value of each of the four decision variables, i.e., sample size, inspection rotation time, control limit coefficient, and preventive repair intervals of each of the critical equipment of the Northern Oil Pipeline and Telecommunication Company and the total expected cost of integration per unit. Minimize time. The results show that the proposed model is much more flexible in calculating equipment's weight and dynamic rating and provides more logical rating results.

The process of transferring money from the treasury to the branches and returning it at specific and limited periods is one of the applications of the Vehicle Routing Problem (VRP). Many parameters affect it, but choosing the right route is the key parameter so that the money delivery process is carried out in a specific period with the least risk. In the present paper, new relationships are defined in the form of three concepts in order to minimize route risk. These concepts are: 1) the vehicle does not travel long routes in the first three movements, 2) a branch is not served at the same hours on two consecutive days, and 3) an arc should not be repeated on two consecutive days. The proposed model with real information received from Bank Shahr has been performed for all branches in Tehran. Because the  VRP is an NP-Hard problem, a genetic algorithm was used to solve the problem. Different issues in various production dimensions were solved with GAMS and MATLAB software to show the algorithm solution quality. The results show that the difference between the genetic algorithm and the optimal solution is an average of 1.09% and a maximum of 1.75%.