Journal of Industrial Engineering and Management Studies
Volume:10 Issue: 1, Winter-Spring 2023

With the development and widespread use of social networks among people, high-volume data is produced and the analysis of this data can be useful in many areas, including people's daily lives. Classification of this volume of data using traditional methods is a very difficult, time-consuming, and low-accuracy task, therefore, using sentiment analysis techniques, people's opinions can be effectively summarized and categorized. To this end, we propose an algorithm that combines Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The reason for combining the two algorithms is that the GSA has a good ability to search overall, but in the last iterations, it has a low speed in exploiting the search space. Since the PSO algorithm has a special ability to exploit the search space, this algorithm is used in the exploitation phase to solve the problem. The accuracy obtained from our proposed algorithm (PSO-GSA) shows an improvement in the accuracy of the GSA algorithm.

This paper discusses the modeling and solution of a flexible flow shop scheduling problem with forward and reverse flow (FFSP-FR). The purpose of presenting this mathematical model is to achieve a suitable solution to reduce the completion time (Cmax) in forward flow (such as assembling parts to deliver jobs to the customer) and reverse flow (such as disassembling parts to reproduce parts). Other important decisions taken in this model are the optimal assignment of jobs to each machine in the forward and reverse flow and the sequence of processing jobs by each machine. Due to the uncertainty of the important parameters of the problem, the Fuzzy Jiménez method has been used. The results of the analysis with CPLEX solver show that with the increase in the uncertainty rate, due to the increase in the processing time, the Cmax in the forward and reverse flow has increased. GA, ICA and RDA algorithms have been used in the analysis of numerical examples with a larger size due to the inability of the CPLEX solver. These algorithms are highly efficient in achieving near-optimal solutions in a shorter time. Therefore, a suitable initial solution has been designed to solve the problem and the findings show that the ICA with an average of 273.37 has the best performance in achieving the near-optimal solution and the RDA with an average of 31.098 has performed the best in solving the problem. Also, the results of the T-Test statistical test with a confidence level of 95% show that there is no significant difference between the averages of the objective function index and the calculation time. As a result, the algorithms were prioritized using the TOPSIS method and the results showed that the RDA is the most efficient solution algorithm with a utility weight of 0.9959, and the GA and ICA are in the next ranks. Based on the findings, it can be said that industrial managers who have assembly and disassembly departments at the same time in their units can use the results of this research to minimize the maximum delivery time due to the reduction of costs and energy consumption, even though there are conditions of uncertainty

Supply chains in the world are increasingly exposed to disruptions caused by disasters in every part of the world. The emergence of risk in today's business environment due to globalization, disruptions, poor infrastructure, forecast errors and various uncertainties affects every management decision. The present study was an attempt to propose an emergency order and production planning for a multi-product multi-item problem where products are made up of several ingredients. A side from the main supplier, the backup supplier can be used to supply each component where orders must be delivered within a certain time interval (specified time window). In the present study attempts are made to use sourcing strategies to realize supply chain flexibility under disruptions. A scenario-based mathematical model encompassing different uncertainties such as those arising from disruption and operational risks is formulated. A case study analysis is carried out to appraise the output of risk attitudes adopted by different decision-makers (both risk-neutral and risk-averse). The present study presents strategies to create flexible supply bases that diminish the cost of the worst scenario in the face of supply chain risks. By increasing the number of primary and supporting suppliers, VAR and C-VR values will increase, so the management offer is that the number of suppliers should be kept constant within acceptable limits to prevent a sharp increase in the number of suppliers. Suppliers should release orders in time by establishing time windows and setting deadlines in order to receive orders. Also, this paper shows that the values of VAR and C-VR decrease with the increase of primary supply capacity, and with the increase of primary supply capacity, costs are reduced by about 99%, which reduces the effect of disruption on the capacity of primary suppliers.

Today, the proper and effective performance of employees is one of the keys to the success of organizations. Good performance refers to high efficiency, quality, profitability, and customer orientation. One of the most important duties of human resource managers is to design and establish employee performance evaluation systems. Since qualitative indices have a major share of these indices, judgmental methods are generally used for ranking them. Decision makers assign weights to these indices based on their attitudes and rank the employees. Hence, these methods fail to fully explain the performance of organizations’ employees and are influenced by some degrees and levels of ambiguity. Fuzzy logic methods are highly useful for resolving the ambiguities in these alternatives. In this paper, we propose an employee performance evaluation method with a type-2 fuzzy ranking approach. In our proposed method, a job ID is designed based on optimal models while an employee ranking method is developed and explained using the trapezoidal interval type-2 fuzzy ranking model introduced by Chen et al. 2012. In the end, the proposed method is utilized for the performance evaluation of employees in a real company.

Emerging companies or start-ups are growing rapidly and their number is increasing every day so that the number of knowledge-based and start-up companies in Iran has increased from about 55 companies in 2013 to more than 5965 companies in 2021. The capital element is the main and most productive factor for the success of start-ups and choosing the right financing method to achieve success is inevitable. The start-up literature offers a number of ways to finance entrepreneurs that are often presented in other geographies (often in startups operating in the United States) and those models cannot be accepted as non-native. Developing a strategic local financing framework based on the tacit knowledge gained by emerging digital startups can address this issue. Based on this, the present study aims to fill the existing gaps by designing a strategic financing framework for digital start-ups based on local criteria in order to be effective in the success of digital start-ups. The statistical population of the quality sector includes entrepreneurs and digital business owners, 30 of whom were identified by snowball method and interviewed in a semi-authorized manner. The statistical population of the quantitative section includes 166 digital businesses operating in Tehran science and technology parks that have been selected using Cochran's formula in a simple random method. To collect data, the method of library review and interviews with experts and finally the distribution of questionnaires have been used. The analysis of the findings in the qualitative stage was performed with a thematic analysis approach and the results showed that 101 open codes were categorized in 17 sub-themes and 17 sub-themes were placed in 5 main themes. In the quantitative stage, confirmatory factor analysis and structural equation modeling with LISREL software were used. The results showed that five main factors including corporate factors, macro environmental factors, investment factors, business valuation factors and idea and product factors are effective in designing digital business financing strategy.

The firefighter problem on a graph, depending on the environment, the graph can be continuous or discrete, which includes tree, cubic, regular and irregular graphs, etc., is described in such a way that by starting a fire from a series of vertices, the goal is to contain the fire with the maximum number of vertices saved. Our main innovation is to model the firefighter problem with on a bi- objective model, which simultaneously saves the maximum number of vertices with the minimum number of firefighters. The firefighter problem is a type of Np-hard problem, and because we defined the problem as a bi-objective problem and added three constraints to it, the problem became more difficult, and the weighted bi-objective model is also Np-hard. To solve the NP-hard problem, we used multi-objective optimization4 such as Goal Programming (GP), ε- Constraint, Global Criterion Approach, Weighting Sum Method methods. To prove the performance of our method, we used a randomly generated sample.

Food and beverage industries have decisive rule due to amount of employment and significant income generation. At micro perspective, due to the wide range of influencing factors, trends do not follow linear behavior and their analysis with non-dynamic tools is challenging. In general, the food and beverage industries in Iran, in addition to the global challenges, are facing many problems that make the prevailing environment more complicated. Among these problems, we can mention the old equipment, difficult access to quality raw materials due to sanctions and the instability of economic indicators. Therefore, if strategies performance cannot be adjusted in line with the market, the resulting losses can be significant and irreparable. In this paper, we developed a system dynamics model (simulation in Vensim) to investigating the production strategies and market in a complex space. For this purpose, we run the simulation 12 times with respect to 4 policies and 3 scenarios. The results show that due to profitability, the strategy of deleting loss products achieve the highest score of performance. Meanwhile, strategy of hybrid production (outsource and factory production) selected due to market penetration.

In today’s growing world, the Green Supply Chain (GSC) is a new approach to include environmental impacts and economic goals in a supply chain network. This paper continues previous research studies by designing a new green supply chain network considering different social, economic, environmental, service level, and shortage aspects. This study introduces a fresh, comprehensive tradeoff model that considers factors such as overall expenses, quality of service, environmental pollution levels, and societal impacts within a sustainable supply chain. The proposed model is formulated as a multi-product multi-objective mixed-integer programming model to assist in planning a green supply chain. The suggested model has three objective functions: maximizing social responsibility, minimizing the cost of carbon dioxide (CO2) emissions, and minimizing economic costs. The model allows for shortages in the form of backorders and seeks to maximize service level in addition to the mentioned objective functions. Robust Possibilistic Programming (RPP) was employed to deal with the problem's uncertain input parameters in the solution approach. Also, a multi-objective model of the problem was solved using Fuzzy Goal Programming (FGP). To examine and evaluate the model in a simple framework, the proposed mathematical model of the problem was implemented in an industrial unit in the real world, and the results obtained from it were analyzed. Among the results that the output of the model provides to managers and decision-makers, it is possible to mention the determination of the optimal amount of production of each product in the manufacturing plants, quantity of products and parts transported between facilities, and also the determination of the of network's carbon emissions which is equal to 51.59 tons.

The appropriate organizational structural capital is one of the most important issues to emerge innovation in knowledge-based companies. The purpose of this paper is to design and implement a structural capital model in knowledge-based companies. The research is developed based on qualitative and quantitative research methods. Firstly, the paper has used the Grounded Theory method to develop the structural capital model based on the 10 experts' interview data. The experts are related to the organizational structural capital subject. Secondly, the model is applied to a knowledge-based company. Therefore, the re-searcher-made questionnaire is used to assess the status of structural capital in the knowledge-based company. So, the reliability was estimated at 0.94%. This paper presents a model for measuring structural capital in knowledge-based companies. The nature of knowledge-based companies has made it necessary to utilize these organizations' measuring to examine the status of infrastructure, processes, and all elements of structural capital.

Customer churn prediction has been gaining significant attention due to the increasing competition among mobile service providers. Machine learning algorithms are commonly used to predict churn; however, their performance can still be improved due to the complexity of customer data structure. Additionally, the lack of interpretability in their results leads to a lack of trust among managers. In this study, a step-by-step framework consisting of three layers is proposed to predict customer churn with high interpretability. The first layer utilizes data preprocessing techniques, the second layer proposes a novel classification model based on supervised and unsupervised algorithms, and the third layer uses evaluation criteria to improve interpretability. The proposed model outperforms existing models in both predictive and descriptive scores. The novelties of this paper lie in proposing a hybrid machine learning model for customer churn prediction and evaluating its interpretability using extracted indicators. Results demonstrate the superiority of clustered dataset versions of models over non-clustered versions, with KNN achieving a recall score of almost 99% for the first layer and the cluster decision tree achieving a 96% recall score for the second layer. Additionally, parameter sensitivity and stability are found to be effective interpretability evaluation metrics.

In order to be competitive, it is an obligation for companies and service centers to identify, evaluate and control risk and environmental aspects of their activities. Due to technical and financial constraints, it is required to prioritize the risks and control measures with greater accuracy. In the framework of the HSE-MS system, for the first time, risk evaluation of industrial activities and services, has been implemented using fuzzy Quality Function Deployment. In this approach, characteristics such as mutual effects of different risks and environmental aspects of industrial activities, risk estimation, and positive and negative aspects of activities have been considered in RPN computation. The application of fuzzy logic reduces the ambiguity of the linguistic parameters. In the case study of the Iran barrit falat it appears, that operation and impact of risk assessment methods and environmental aspects of activities, evaluation criteria and the priority actions has been performed more precisely in comparison with traditional methods of risk assessment.

Generally, the safety management system (SMS) introduced in 1980 focuses on reducing the risk of potential injuries and fatalities in the construction industry. The key to considering the challenges of project safety management and risk assessment in the construction industry as a hazardous industry because of its peculiar nature is important. In line with this, this article aims at employing decision-making techniques to ensure the safety requirements of construction projects. Additionally, a questionnaire under fuzzy environments for identifying the candidate locations and strategies associated with each specific location was conducted. Also, the Empirical Bayesian (EB) approach has been considered to estimate the expected frequency of accidents. The objective of the novel proposed approach is to find the optimal safety project selection with respect to the economic indicators and time value of money under uncertain circumstances. For this purpose, a mathematical optimization model is proposed, and its efficiency is demonstrated by a numerical case study. The results of optimizing the mathematical model indicate that by modifying two factors, namely the safety level and uncertainty coefficient, several scenarios can be explored for cost reduction and a decrease in the number of construction projects. By maintaining a constant safety level of 1.37 (as determined by industry experts) and increasing the uncertainty coefficient from 0 to 0.2, costs decrease by a factor of 1.7, accompanied by a decrease in the number of construction projects by one unit. Furthermore, when the uncertainty coefficient is held constant at 0.2, costs can be reduced up to four times by reducing the safety level from 1.37 to 1. This decision-making framework can significantly contribute to minimizing building accidents and enhancing safety in construction projects.