Journal of Industrial and Systems Engineering
Volume:15 Issue: 4, Autumn 2023

Today, the development of products and services is vital for companies that want to compete in existing markets as well as in new markets. Therefore, companies can improve their green image by improving their environmental performance, which in turn helps to create new business opportunities and improve competitive advantage. Whereas the relationship between culture and organization is like the relationship between personality and individual, the concept of organizational culture is considered as an effective factor in the productivity and performance of the organization, and if the appropriate work culture is well developed between management and employees, it will lead to consolidated organizational commitment, promotion of morals, higher performance and productivity. Therefore, in this research, the relationship between adaptations of green culture, innovation, green performance to achieve sustainability is investigated. This is a descriptive survey and applied research. The results indicate that the adaptation of green culture on innovation has a positive and significant relationship. Innovation has a positive effect on green performance to achieve sustainability and employee commitment. Employees' commitment to green performance to achieve the sustainability, has a positive and significant relationship. At the same time, the mediating role of employees' commitment was confirmed.

This article aims to present an information technology model in knowledge-based industries in pharmaceutical domain,considering country's economic conditions, relies on multi-grounded theory approach. study is applied in terms of its objective and is classified as qualitative research based on method of data collection. The statistical population comprises experts in knowledge-based pharmaceutical industries , academic specialists, gathered through in-depth interviews during spring of 2022. sample size for qualitative part was determined through theoretical saturation(15 individuals),using non-probabilistic purposeful sampling. data collection tool used in qualitative section was interviews. validity and reliability of the questionnaire were confirmed. To analyze data, Maxqda software was employed. The results show, with main variables being category of evaluating information technology strategies; formulating information technology strategies; and implementing information technology strategies. Other variables included in categories were defining organization's visions and goals; determining vital performance indicators for organization; analyzing organization's environment as causal conditions; variables related to foundations of strategic marketing orientation; foundations of strategic information technology orientation; and foundations of strategic organizational orientation as facilitators. This also encompassed challenges related to size of the organization; challenges regarding extent of information technology utilization; and challenges pertaining to type of industry as intervening conditions. Moreover, mechanisms for utilizing internet infrastructure to provide organizational services; rapid development mechanisms for services and products for customers; and mechanisms for meeting organizational needs in a competitive environment were regarded as strategies. Finally, variables included enhancing information technology governance; improving information technology architecture; and implementing a library of information technology infrastructure as considered outcomes.

Breast cancer (BC) is the most common cancer and one of the main causes of death among women. This study was conducted to investigate the relationship between BC and nutrition and lifestyle, as well as compare machine learning models in predicting this disease. We designed a questionnaire related to nutrition and lifestyle with a nutritionist's guidance and provided them to 569 patients. After data gathering, we developed some machine-learning algorithms like logistic regression (LR), K-Nearest Neighbor (KNN), Decision tree (DT), and Support vector machine (SVM) classifiers. To make more accurate models, we used an oversampling method to avoid skewing the model due to the lack of balance in the target classes, a grid search method to adjust the model's hyperparameters and finally random forest to identify each variable's importance. The results of this research showed that the accuracy of the DT model was 0.95, SVM and LR were 0.93, and KNN was 0.86. The results indicated the better performance of DT among other models. Our findings show that it is possible to predict the type of cancerous tumor with relatively high accuracy without using specific information about the tumor itself. In particular, in our study, the decision tree has shown better accuracy compared to other models.

This study dives into work-family conflict among construction workers, examining how work flexibility and organizational and family support impact job and family satisfaction in an industry known for high stress and risk due to its project-driven nature. Using system dynamics simulation, we crafted a model based on literature, navigating variable relationships, circular patterns, quantitative functions, validating behavior, and proposing scenario-based policies. Findings show elevated levels of work interference with family conflicts (WIFC) and family interference with work conflicts (FIWC) among construction workers. Increased work flexibility and organizational support enhance satisfaction and performance in both work and family domains, while improving family support significantly boosts job satisfaction. Departing from previous research based on surveys, this study introduces a new approach called system dynamics model simulation to describe the evolution of work-family conflict among construction workers. Crafting a two-way dynamic model from existing literature yields theoretical insights and actionable recommendations for managing this conflict in the construction industry.

Nowadays, the project scheduling problem with limited resources has become one of the most important optimization issues. Given the enormous costs spent on projects as well as materials, resources, and forces, projects are expected to be successful and finish at the planned time. This schedule in functional mode will have many uncertainties due to the needs of the moment. Such a goal requires careful planning and advanced algorithms to solve these complex issues in a short and reasonable time. In this research, a new method is presented using the Intelligent Water Drops (IWD) algorithm for the resource-constrained project scheduling problem. For this reason and because of the importance of these projects, in this research, an optimization model has been developed for project scheduling in the state of uncertainty, which can solve many implementation obstacles. For this purpose, first, the problem is formulated as a mixed-inter linear programming (MILP) model. Next, the model is optimized using the IDW algorithm. To evaluate the performance of the proposed method, the standard data set was used in previous research and articles, and four datasets with different scales were selected from the PSLIB library. The results show that the proposed method is capable of obtaining the best precision in terms of the least critical deviation from the optimal solution. Moreover, the results of the proposed method were compared with metaheuristic algorithms, such as the particle congestion algorithm (PSO) , which was able to get the best solution among these algorithms.

The main goal of the research is production timing with the approach of meta-heuristic algorithms. First, the mathematical model of the production schedule was presented, and then the model was solved with the genetic algorithm. All types of genetic operators were considered at this stage, and an attempt was made to achieve better answers by choosing appropriate methods. The result of applying these targeted selection methods was the rapid convergence of the population. However, this fast convergence did not provide an optimal solution because it quickly converged all the people of the population to a local optimal solution and did not allow the algorithm to search more of the solution space. Therefore, contrary to expectations, the targeted selection methods without a suitable generation method did not improve the algorithm's efficiency. At this stage, the generation methods were considered; the optimal solution for big problems was also obtained by implementing the selection methods. By finding the appropriate generation method, it was observed that even the operators who did not have much ability to see close to optimal solutions succeeded in finding optimal solutions.

This research aims "to study the approaches of electronic supply chain management on performance evaluation in the automobile industry." Therefore, the research method used in this research is applied, descriptive, and correlational. The statistical population of this research is the experts of the Ministry of Privacy. Out of 250 people, according to the Cochran formula, 148 people were selected as a sample. To measure the variables in the current research, the standard questionnaire in the base article was used, which was measured in a five-point Likert spectrum with a total of 19 questions. The face validity of this questionnaire has been given to specialists and experts such as the honorable teacher, and Cronbach's alpha test also tested its reliability. Many tables and graphs will be used to describe the findings. The Kolmogorov–Smirnov test measures the variables to confirm and disconfirm the normal distribution of the data. The structural equation test will be used in the PLS software environment to fit the model used in the research. The findings of the research showed that the environmental factor of the sustainable supply chain with the mediating role of digital-based supply chain integration is influential in evaluating the performance of the automobile industry. Finally, practical suggestions were presented.

The study aims to identify the components influencing the self-regulation of real traders in competitive financial markets. This research is applied in terms of its objective and it is qualitative based on a thematic analysis approach. The statistical population includes CEOs and chairpersons of boards of directors of companies active in cryptocurrencies, professors, and experts in financial markets. Sampling was conducted using a purposeful method based on a theoretical approach. Data collection continued until theoretical saturation was reached, resulting in 15 semi-structured individual interviews.The collected qualitative data was analyzed through open and axial coding. In the open coding, the researcher identified 41 concepts. Then, based on the initial raw data, preliminary categories related to the phenomenon under investigation were formed by breaking down the information, structuring categories of information about the studied phenomenon, questioning the data, comparing cases, events, and other states of phenomena to identify similarities and differences, which were categorized into 5 categories: 1) acceptance of high investment costs, 2) acceptance of instability in the business environment, 3) lack of trust in the market, 4) acceptance of sudden changes in domestic markets, and 5) emotion management. This systematic evaluation can help a trader correct mistakes, change bad habits, and improve overall returns. Investors need to be quick, be aware of their greed and fears, and also be able to act flexibly. As it is evident, for any trading, it is necessary to be aware of personal and market psychology beyond specialized trading knowledge.

Today, social networks have expanded a lot and a wide range of people are members of these networks, regardless of social stratification. The functions and services of these networks are rapidly changing and evolving, as many of the daily needs and services of people are offered in these networks. The present research aims to provide a model to explain the influence of social trust components on advertisements in online social networks (with the moderating role of innovative culture). For this purpose, first, the articles, sources, and references related to the topic were reviewed and the desired components were extracted. The statistical population in this research included 7 university experts and information and communication technology managers selected by the snowball method. The necessary formulas for implementing the model were checked with the opinion of experts. The validity of the research model was confirmed after performing various tests in VENSIM Software. After confirming the validity of the model, scenarios were developed and implemented. The results of the designed scenarios showed that the increase of social trust components such as the organization's credibility and security has the greatest effect on the effectiveness of advertising in online social networks.

In this paper, the problem of product pricing in a sustainable supply chain consisting of several production centers, several distribution centers, and customers in two direct and indirect channels is modeled. This model aims to achieve more profit in a sustainable supply chain by complying with environmental and social considerations. In order to solve the model, a hybrid of genetic algorithm (GA) and grey wolf optimizer (GWO) called genetic grey wolf optimizer (GGWO) was developed, with the ability to search the problem-solving space based on the simultaneous use of the operators of the two algorithms. Analysis of the numerical example with GGWO and its comparison with other solution methods indicate that pricing in direct and indirect channels is strongly related to the price elasticity coefficient of the substitute product and the price elasticity of demand so that the market boom and bust of the depends on the proper determination of these model parameters. It is also observed that in the conditions if market boom, demand increased in both direct and indirect channels and that product prices decreased in both channels. Therefore, the marginal profit increased due to the sale of more products. The GGWO algorithm has faster convergence compared to GA and GGWO, while the solution time in this method is higher than other solution methods. Analyzing different numerical examples suggest that the efficiency of the used algorithms, in particular GGWO, is higher than exact methods.

Sustainable banking has emerged as a pivotal approach to addressing environmental, social, and governance (ESG) challenges in the financial sector. This paper explores innovative strategies that banks can adopt to align profitability with sustainable development goals (SDGs). Banks can create value by leveraging green finance, digital transformation, and ethical investment practices while minimizing environmental footprints and promoting social equity. The study highlights the role of artificial intelligence, blockchain, and data analytics in enhancing transparency, reducing operational risks, and driving sustainable financial products. Furthermore, it examines policy frameworks, stakeholder engagement, and the integration of ESG metrics in decision-making processes. Case studies from leading institutions illustrate successful applications and the measurable impact of sustainable banking initiatives. The findings emphasize the necessity of a paradigm shift toward long- term value creation, fostering resilience in the financial sector and supporting global efforts to build a sustainable economy.