Journal of Industrial and Systems Engineering
Volume:16 Issue: 1, Winter 2024

Given that the cost of internal financing is lower than external financing and generally provides easier access to funds and resources, it is often the preferred choice for companies. However, internal financing may not always be feasible or effective, especially depending on the business cycle and the company's operational scale. In such cases, external financing becomes necessary, and selecting appropriate and low-risk methods for external financing requires careful and strategic consideration. Therefore, estimating the risks associated with external financing in the transportation industry is essential for managing the company’s capital structure and utilizing external financial resources more confidently. This study, conducted in 2023, aims to examine the impact of financing risk factors on the performance of companies in the transportation industry. The research is applied in nature, based on its objectives, and uses a descriptive-survey method for data collection. A field study was conducted by designing and distributing a questionnaire among 386 individuals, including CEOs, vice presidents, executives, and key personnel in the transportation industry, all of whom were well-versed in the financing process. The collected data were analyzed using structural equation modeling techniques with the Smart PLS software. The analysis results showed that, in addition to the validity of the relationships between the core variables and the confirmation of all elements of the model, the derived model demonstrated strong validity, reliability, and overall fit.

Iranian Commercial banks are always considered as one of the most important institutions active in the money and capital market, due to the economic structure of the country and the lack of development of the capital markets, which makes them in charge of financing the economic sectors of the country. However, these banks are not successful in fulfilling their mission. High level of banks' reserves shows that they do not pay enough attention to risk management and credit portfolio management. There are several models such as linear programming, integer programming, zero and one programming that can provide an optimal combination of the elements that make up the facility basket. However, entering financial information into mathematical planning by considering all conditions is not straightforward to achieve such a goal. In this research, using data mining to optimize the multi-objective model of facility allocation is done using neural network. First, the effective variables were extracted from the bank database and after preparation, the most important features were identified using different algorithms such as random forest algorithm, MARS, and step-wise regression. Then, these methods were compared with each other and the best method was selected. In order to cluster the customers, k-means and k-medoids models have been used. Using different criteria, including the silhouette and the best number of clusters, two clusters have been estimated and customers have been identified in two low-risk and high-risk categories. And finally, by using convolutional neural network, the risk and profit of each customer has been predicted.

The ever-increasing changes in the business world and the new requirements of production and trade in the present era have provided the basis for the emergence of new attitudes. The supply chain in the current environment is complex and diverse. Based on the change in the market environment, the demand for the supply chain has also created a lot of uncertainty. Also, with transformative technologies in the new era, managing supply chain uncertainties has taken on a new face. One of these technologies is blockchain technology. The importance of drug safety, always one of the biggest concerns, cannot be overstated as it directly affects the public health of society. It is a shared responsibility of all stakeholders in the pharmaceutical industry to establish a reliable and traceable pharmaceutical system. For this reason, in this research, an effort has been made to provide a model for sustainable supply chains in a state of uncertainty, emphasizing blockchain technology as a tool to fulfill this responsibility, fostering a sense of collective duty and commitment in the audience.

New Mobility Services (NMS) are more popular every year. Sharing economy solutions acquire the demand for travel services, especially for short distances. The evolution of business models on the mobility market has an impact on mobility choices of all urban residents, especially the younger ones, described as the Y generation. Therefore, the study aims at indicating the level of popularity of Mobility-as-a-Service (MaaS) and differences between the subcohorts of the Y generation in Poland.

Globalization of economic activities along with the rapid growth of technology as well as limited resources have placed companies in a tight competition. Among the competitive advantages for companies is to make activities such as the supply chain more efficient and effective. Since suppliers exert a fundamental influence on the success or failure of a company, it is known as a strategic task. Considering the importance of supplier selection, in this paper a reverse logistics network model is designed for supplier selection under uncertainty. The objective functions of the designed model include minimizing the total cost, the total number of defective parts, the timely delivery of all parts to the customer, and the hazardous environmental factors associated with suppliers. In order to be closer to reality, parameters such as demand, transportation cost, product production costs, and product purchase price are considered uncertainty, and robust-fuzzy approach is used to deal with uncertainty parameters in modeling. Finally, in order to avoid weighting in multi-objective model decision making, Monte Carlo simulation has been developed to determine the total number of Pareto solutions from the presented model. The results of the evaluation of the mathematical model with the robust-fuzzy approach show that as the penalty coefficients of the objective function increase, the cost of the total supply chain network increases, but its standard deviation decreases. This issue shows the high capability of the robust method in controlling the uncertainty model of the problem.

Environmental pollution, the depletion of fossil fuels, the swelling population, and the subsequent surge in energy consumption worldwide have prompted numerous industrialists, scientists, and researchers to embrace biomass products as an alternative to fossil fuels. Biomass-based products, such as biofuels, have emerged as viable sources of energy in light of economic and ecological considerations. Among the various generations of biomass, the second generation stands out for its particularly favorable attributes. In particular, Jatropha and Paulownia have garnered significant attention as extremely useful second-generation biomass plants, specifically for biofuel production. This study undertakes a comprehensive literature review and classification of the Jatropha and Paulownia supply chain network design, shedding light on their significance and potential. Furthermore, various scientific analyses are conducted to examine the works published in this field. These articles are classified based on the principles of sustainability, product type, biomass type, limitations, type of uncertainty, and solution approach, among others. Additionally, the studies in this field are reviewed, a number of research gaps are outlined, and areas are suggested for future research based on the findings of this study.

The Internet of Things (IoT) is a transformative network of physical devices equipped with sensors and connected technologies that efficiently collect and share vital information. Intelligent energy prioritizes using renewable energy sources while significantly enhancing energy efficiency and environmental sustainability. Smart energy is not just an option but essential across all sectors. In smart cities, remote meter reading is a powerful and precise tool for intelligent energy management. This system operates through three critical components: measurement, analysis, and action. This paper introduces an efficient, cost-effective, and highly reliable method for real-time monitoring of AC power consumption for local and remote loads. Understanding household energy consumption is imperative for consumers, as it enables them to pinpoint significant opportunities for energy savings. Our monitoring system is specifically designed to analyze and evaluate household appliances' output voltage, current, frequency, and energy, empowering users to make informed choices about their energy use.

Studying, understanding, and predicting human behavior has been the focus of many researchers for years and is the basis of many researches in the field of consumer behavior. The current study was conducted to provide a model of factors affecting purchasing behavior in a mixed method. In the qualitative part, semi-structured interviews were conducted with experts. In the quantitative part, four standard questionnaires were completed on Cloninger's personality, ordered pair, AHP, DNAP, and shopping behavior. In qualitative data analysis, using Maxqda software, 198 primary codes, 38 concepts, 25 components, and six main categories were identified. DEMATEL and ANP techniques were used to determine the weights and check the mutual relationship between the criteria extracted from the qualitative method. The results of DANP for screening dimensions, components, and categories and determining their importance indicated that environmental and climatic factors are influential, as well as purchasing behavior, personality, economic, marketing, and brand excellence.

This study optimizes the multi-commodity routing problem in a constrained network, integrating dynamic warehouse management, diverse vehicle ownership options, and congestion management. The model addresses the efficient routing of goods with limited vehicle and warehouse capacities, enabling the addition or removal of warehouses based on demand fluctuations. It incorporates a hybrid fleet strategy, balancing owned and outsourced vehicles to minimize costs while ensuring flexibility. The model also considers network congestion, optimizing routes and schedules to mitigate delays. This approach provides a comprehensive solution for cost-effective and responsive supply chain logistics. In this research, the complexity of the mathematical model and its multi-objective nature led to the use of the epsilon constraint method and the MOGWO and NSGA II algorithms in the model. Solving the model using the mentioned methods showed that the total costs increased with the improvement of the second objective function. This problem has been due to the use of vehicles with higher speeds and higher prices, and also by reducing the risk of transporting products, the total costs have increased again.

This study examines the impact of blockchain technology on the banking industry and identifies the indicators and applications of this technology using the meta-synthesis method. The findings include 28 indicators: preventing fraud, reducing banking costs, ensuring security and transparency, identity management, and strengthening the capital market. The results show that blockchain can facilitate domestic and international transactions and increase the productivity of the Iranian banking system. The research method is qualitative and applied, and a seven-step meta-synthesis process was used in data analysis. This study provides a valuable tool for banking planners by identifying key indicators. There are limitations, such as generalizability to other organizations and using the meta-synthesis method. Still, the research is valuable regarding innovation in theoretical foundations and methodology and can guide the development of blockchain technology in banking.

Resilient green supply chains integrate sustainability and robustness, enabling businesses to adapt to disruptions while prioritizing environmental responsibility. This research proposes a conceptual model that identifies crucial dimensions, components, and applications of resilient green supply chains. The study highlights critical dimensions such as sustainability, flexibility, risk management, and technological integration by synthesizing existing literature and applying a systematic approach. Components like supplier collaboration, circular economy practices, and renewable energy adoption are emphasized as vital for achieving resilience. Furthermore, practical applications, including disaster recovery frameworks and eco-friendly procurement strategies, are explored to demonstrate the model's relevance in real-world scenarios. The proposed model provides a holistic perspective, offering strategic insights for stakeholders seeking to enhance environmental and operational performance. This study is a foundation for future research and practical implementations, addressing the growing need for sustainable and resilient supply chain systems in today's dynamic global environment.