Journal of Applied Research on Industrial Engineering
Volume:11 Issue: 2, Spring 2024

Construction trades are considered to be at high risk for Work-Related Musculoskeletal Disorders (WRMSDs) due to their nature. Previous reviews have addressed various risk factors for developing WRMSDs among construction workers. However, the results appear insufficient because no proper evidence was reported. Therefore, the purpose of this current review is to summarize the occurrence rates of WRMSDs and quantify the relationships between various risk factors and WRMSDs among construction workers. Literature searches were conducted through the following six electronic databases from 2000 to 2022: ScienceDirect, PubMed, Web of Science, Google Scholar, ResearchGate, and Medline. Selected articles were classified as having a strong, moderate, limited, and no effect, respectively, based on their association with WRMSDs. From the selected 66 articles, the highest occurrence rates of WRMSDs were found in construction workers (ranging from 33% to 89%). There were several significant risk factors for developing WRMSDs in construction workers, including age, working experiences, awkward working postures, vibration, repetitive body movement, manual material handling, biomechanical stress, and physical fatigue. However, most of the study was conducted through a cross-sectional survey to investigate the relationships between these risk factors and WRMSDs in construction workers. To determine the insights on risk factors and WRMSDs among construction workers, experimental, longitudinal, and real-time task-based studies can be conducted. This study may be helpful in improving awareness about risk factors for developing WRMSDs among construction workers.

Performance measurement is an annual process that evaluates employee performance and productivity against predetermined goals. Performance management is a determining factor in raising salaries and promoting employees. It can accurately examine the skills, strengths, and weaknesses of employees. So, it is very essential. In this paper, indicators are determined based on a Balanced Scorecard (BSC) to evaluate the performance of wood industry employees. For this purpose, 47 indicators are suggested and investigated by a questionnaire, of which 38 are confirmed using the nonparametric Wilcoxon's signed rank. The confirmed indicators are ranked using the TOPSIS and SAW methods. BCS has four dimensions: the dimension of growth and learning, the dimension of internal processes, the dimension of customer, and the dimension of finance. According to the results, the dimension of growth and learning is more important in the wood industry. Among the sub-indicators, the indicator of performing assigned tasks,  the indicator of traffic and attendance, and the indicator of trust and responsibility are three critical indicators in the performance of wood industry employees.

The inverse Data Envelopment Analysis (DEA) problem has been one of the most critical issues in the last decade. The inverse DEA permits the chief manager to increase (or decrease) outputs (or inputs) of Decision-Making Units (DMUs) in such a way that the level of the relative efficiency of the under–observed DMU is preserved. Due to the importance of network-structured production systems in real life, the primary purpose of the present research is to provide an inverse DEA model for a two-stage network-structured production system in the presence of undesirable factors. The weak disposability assumption is used to handle undesirable outputs in the proposed model. The focus of the proposed model is on estimating the amount of change in one or more indicators of one stage of the process by changing the indicators of another stage to preserve the level of efficiency. The most important advantage of the proposed procedure is that it can increase the level of outputs and simultaneously reduce the level of inputs. To demonstrate its practical use, the model is applied to a real-life example in poultry farming.

A review of previous research shows that a systematic and comprehensive approach to the factors and dimensions of Supply Chain Resilience (SCR) has received less attention in this field. Therefore, this study aims to design a model of factors influencing SCR. This research is considered applied and development-oriented in terms of its objective, and it was conducted in the tile and ceramic industry in Yazd province. In doing so, the study primarily investigated publications indexed in scientific databases, extracting the factors affecting SCR through a systematic approach and the meta-synthesis method. Following that, a model was constructed that consisted of 33 factors falling under five dimensions: 1) production and distribution management, 2) communication and participative management, 3) financial and information management, 4) human resources management, and 5) risk and crisis management. The findings revealed that such factors as "having a strong relationship with suppliers," "using lean production and eliminating wastes," "technological flexibility in production," "implementing integrated Information Technology (IT) infrastructures in the SC," and "safety stock of materials" were the most critical factors affecting resilience in the SC. Generally speaking, what distinguishes this study from others in this field is the new and scientific framework of factors that it proposes through a systematic approach and the identification of the most important factors, which could serve as a guideline to senior managers and decision-makers.

We consider a continuous model of the optimal control of the customer dynamics based on marketing policies as a non-autonomous system of ODEs. The model tracks the history of the simultaneous changes from the beginning to the current time for the evolution of the company's regular, referral, and potential customers. We then present a new supervised machine-learning algorithm for the numerical simulation of the problem. The proposed learning algorithm implements a polynomial kernel to simplify the formulation of the method. To avoid computational complexity, the Bernstein kernels are used to get a simple optimization marketing strategy by using the Support Vector Regression (SVR) in a least-squares framework. Some numerical experiments are carried out to support the proposed model and the method. The method provides approximate numerical results with high accuracy by kernels of polynomials of low degree. The running time of the technique is also illustrated versus the increasing number of training points to see the polynomial behavior of the running time.

Today, we can easily utilize drones to perform a wide range of tasks, whether by employing semi or fully autonomous flight modes or controlling them remotely. However, a significant drawback of current drone deployment methods is the limited operational time due to battery constraints. To address the battery limitations of indoor drones, a solution is proposed wherein wireless chargers are strategically placed within the coverage area of the drones' flight paths. This research paper presents an optimization algorithm that determines the ideal number of wireless charging stations needed to cover the entire area to ensure continuous charging capability throughout the flight. This work proposes an optimization framework that effectively solves this non-deterministic polynomial-time hardness problem. The algorithm is assessed by comparing its results with those of other algorithms. To evaluate the performance of our proposed approach, we compared several recent algorithms. Our algorithm has demonstrated superior speed compared to other algorithms.

A significant part of each system is determining system efficiency to conduct future planning-associated operations. Data Envelopment Analysis (DEA) is often employed to measure system efficiency. The paper considers a bi-level structure and proposes a new non-radial method by generalizing Russell's model to measure system efficiency. Furthermore, data from 33 branches of an Iranian state bank in 2021 are investigated to present an example of the application. The results indicate that among 33 branches, only two branches are regarded as efficient at both leader and follower levels.

In recent years, expanding the social responsibility concept, increased environmental considerations, economic incentives, and governmental pressure on manufacturers for waste management have caused organizations to focus on developing Closed-Loop Supply Chains (CLSC) and Reverse Logistics (RL) processes. Adopting these approaches will enable organizations to meet economic, social, and environmental goals simultaneously and consider the manufacturing cycle from supply and production to product reuse. Hence, this study deals with an optimization model within the framework of a multi-echelon, multi-product, and multi-period CLSC with hybrid facilities where cross-docking strategy and vehicle routing with soft time windows have been included. In the problem defined as a MILP model, decisions are made simultaneously at three strategic, tactical, and operational levels. Furthermore, to tackle the NP-hard problem and achieve near-to-optimal results in a reasonable time, two meta-heuristic algorithms, NRGA and MOPSO, are developed, and the algorithms' parameters are tuned using the Taguchi method. Finally, the computational results are examined using performance measures and statistical analysis. A sensitivity analysis is performed regarding the impacts of demand and the rate of returned products on the values of the objective functions.

Dental decay is the most common chronic disease in children. Fluoride Varnish (FV) is a preventive oral health service with proven effectiveness in reducing dental caries in dental and primary care settings. The objective of this study was to determine how long it takes to apply FV treatments during primary care well visits to address one of the most common barriers as reported by pediatricians – lack of time. FV treatment videos were collected at six clinics in Georgia, and rigorous time studies were conducted on each video to determine the standard time for the FV treatment process, the FV application component of the process, and the reasons for delays. Median standard times varied by clinic, ranging from 67.7 seconds to 166.9 seconds, with an overall median of 109.7 seconds. This results in, per FV application, labor costs of approximately $2.38 for pediatricians, $1.16 for registered nurses, and $0.53 for medical assistants. Findings from this study support the inclusion of FV applications as a common practice during primary care well visits.

This paper addresses the problem of constructing a performance assessment model. The developed model is constructed based on strategic management, intelligence systems, and expert ideas, and then it is implemented in Tehran municipality to show acceptable results for mayors. The proposed model consists of 10 steps: team construction, vision consideration, process identification, indicator selection, weight calculation, data collection, data Extract-Transform-Load, data warehouse, analysis and reporting, feedback, and improvement processes. Analytical Hierarchy Process and Technique for Order of Preference by Similarity to Ideal Solution are used for weight calculations and ranking the city's distinct. In addition, we use the Simple Weighted Mean method in four different data normalization ways and standardize the data to compare the results and use a criterion to select the robust answer. Moreover, we compare our model with the European Foundation for Quality Management model and Balance Scorecard. The proposed model is conducted at plan monitoring, project control, and performance evaluation offices in planning, human capital development, and the council affairs department in Tehran.