International Journal of Applied Operational Research

Mathematical modeling to evaluate knowledge management in the development of industrial clusters

M. Abolghasemian

Industrial clustering refers to the concentration of related industries or companies in a specific geographic area. On the other hand, knowledge management focuses on capturing, organizing, and leveraging knowledge within organizations. In the context of industrial clustering, knowledge management plays a vital role in facilitating collaboration, innovation, and competitiveness among the clustered companies. In the current research, an approach to knowledge management in the development of industrial clusters is proposed. Thus, an integer binary mathematical programming model is presented to select the programs with the highest knowledge-enhancing effect. The proposed model allocates the most important programs in its specialized portfolios. To validate the proposed mathematical model, five types (program portfolio) including training workshop, training course, industrial tour visiting industries, exhibition visiting tour, and participating in the exhibition are considered for ten production units. Each of the programs improves the six types of knowledge of its employees in the fields of design, production, purchasing, finance, marketing, and administration in three skill levels (low, medium, and high). According to the results obtained from solving the model with the GAMS software, the programs are assigned to each cluster. The most important finding of the research is that if the programs are implemented in each production unit separately, the improvement of knowledge is less than when they are implemented as clusters. Therefore, it can be concluded that the proposed model has a suitable efficiency in creating knowledge alignment in the organization with the approach of industrial clusters. Moreover, the results indicate that if these programs are implemented separately, the cost of knowledge promotion would increase significantly. Knowledge management plays a crucial role in nurturing collaboration, innovation, and competitiveness within industrial clusters. It helps cluster members unlock the potential of shared knowledge and resources, leading to mutual benefits and the overall development of the cluster.

Greenhouse gases allocation efficiency assessment in the electricity supply chain using the zero-sum gains model: a case study in the power industry

M. Pouralizadeh

The energy and power plant sectors of the power industry are among the major greenhouse gas emission sources. Oil and gas fields, refineries, and power plants should control harmful emissions to prevent pollutants from damaging the environment. A practical way of doing this is to monitor the total emissions amount in the electricity supply chain divisions and establish emissions trading rights, assuming that the allocation of the total emissions amount will be determined based on the target total amount. The current paper, applying the input-oriented ZSG-DEA model, computed the allocation efficiency of nitrogen oxides, sulfur oxides, carbon dioxide, and methane emission rights in the energy and power plant sectors of the electricity supply chain. For this to happen, the inefficient divisions had to decrease their emissions and search for partners that enabled them to reduce their emissions to keep the global emissions unchanged. With this in mind, the proposed approach in the present study distinguished effective sectors of an electricity supply chain with a high emission level as a cooperative set that provided a compensatory reduction to achieve the established limit. The results suggested that oil fields had a fundamental need for sulfur monoxide and carbon dioxide reduction in 70% of the supply chains, while the gas field emission efficiency of 50% of the supply chains was approximately close to 1. Although power plants were efficient in at least 70% of the supply chains, some power plants emitted the highest amounts of sulfur oxide because they lacked investment and effective cooperation for pollution abatement.

How customers select online transportation platforms: An ISM based model

M. R. Ramazanian

Identifying the factors affecting the selection of online transportation platforms and their ranking is a topic that has received much attention in the field of urban transportation and information technology, especially in the last decade. By identifying and ranking these factors, the quality and performance of online transportation platform services can be improved. The purpose of this research is to identify the factors affecting the selection of online transportation platforms and their ranking. In this study, a Interpretive - Structural approach has been used to classify factors. This research is applied in terms of purpose and descriptive-survey in terms of study and data collection. The study population is experts, managers, and specialists of online transportation platforms, with a sample size of 16 people. The sampling method is available sampling. First, by studying previous research, the indicators and criteria affecting the selection of online transportation platforms were identified, and then a questionnaire of factors affecting the selection of online transportation platforms was designed. This questionnaire was distributed among experts, managers and specialists active in the field of online transportation platforms. This questionnaire was distributed among experts, managers and specialists active in the field of online transportation platforms. They were then leveled using interpretive structural modeling. Based on the results, the factors of the level of security perception and commitment and responsibility of the company were placed at the first level. User satisfaction, time saving, innovation and up-to-dateness are the second level and accessibility is the third level. Also, based on the analysis of the influence-dependency of the variables of user-friendliness, time-saving as an independent variable; the variables of sense of security and commitment and responsibility of the company as dependent variables; also the variable of ease of access as an independent variable and also the variable of innovation and being up-to-date as a linked variable. The results showed that the factors of ease of access to the platform, control, innovation, and platform up-to-dateness are keys in the selection of online transportation platforms by customers in Iran and should be prioritized.

Identify and prioritize industry 4.0 technology in supply chain finance based on sustainability approach

M. Moradi

This study seeks to systematically identify and prioritize the Industry 4.0 technologies that influence sustainable supply chain finance and assess the individual impacts of these technologies on the sustainability of supply chains. Industry 4.0 represents a transformative paradigm shift in manufacturing and service sectors, characterized by the integration of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), cloud computing, robotics, and other cutting-edge innovations into production and service operations. These technological advancements are associated with significant improvements in productivity, cost efficiency, product and service quality, speed of production and delivery, and worker safety, among other outcomes. In parallel, sustainable supply chain finance refers to the strategic deployment of financial resources aimed at supporting supply chains that adhere to economic, social, and environmental sustainability principles. By ensuring that both companies and their suppliers comply with sustainability standards, this approach plays a pivotal role in strengthening and maintaining a resilient and responsible supply chain, thereby delivering substantial benefits to society and the environment. As global supply chains become increasingly interconnected and complex, the ability to integrate Industry 4.0 technologies within the framework of sustainable finance has the potential to reshape not only operational efficiencies but also contribute to long-term societal welfare and environmental stewardship. This research provides valuable insights into how these technological innovations can drive sustainability within financial structures, offering a pathway for future-proofing supply chains in the face of emerging global challenges. This study is both applied and expert-oriented, focusing on the sub-criteria within the economic, social, and environmental dimensions of sustainable supply chain finance, in the context of emerging Industry 4.0 technologies.

Development of a comprehensive model to predict stock prices In the stock market with an interpretive structural modeling approach

M. Taleghani

Predicting stock prices has long been a topic of interest for analysts and researchers. The aim of this study is to develop a comprehensive model for predicting stock prices in the Tehran Stock Exchange using a combined approach of fuzzy Delphi interpretive structural modeling with the use of technical, fundamental, macroeconomic, and emotional factors. In this study, first, using the fuzzy Delphi method, 15 key criteria were identified from among 54 prediction criteria extracted from research literature according to investors' perspectives. Then, using the interpretive structural modeling approach, the relationships between them were examined and a hierarchical model was presented. Based on the findings in the ISM model, it is observed that the price is placed at the end of the hierarchy with high driving power, depending on earnings per share and cash flow index. The criteria that are placed at the bottom of the hierarchy are exchange rates and relative strength index and exponential moving average, which are the most influential indicators. This study is the first of its kind to identify stock price prediction criteria by considering all dimensions and developing hierarchical relationships between them using the ISM approach

Environmental productivity growth of selected Iranian economic sectors: A Malmquist approach

A. Dehghani

Evaluation of environmental efficiency has recently attracted an increasing interest. This paper focuses on environmental efficiency analysis and productivity growth of the economic selected sectors in Iran. These sectors are agriculture, oil, industry, transportation and domestic, commercial and public over the period 1997- 2014. For estimation environmental efficiency, data envelopment analysis (DEA) which is a mathematical programming based approach is used and to review the progress or regress environmental efficiency of each economic section Malmquist index is utilized. Labor and capital stock are used as inputs. Value added is considered as a desirable output and CO2 emissions as undesirable output. The empirical results show that the transportation sector survive a low level of environmental efficiency and agriculture and oil sectors have a good situation in this term. According to the results of growth analysis in selected sectors, despite fluctuations in different years, the average total factor productivity is generally associated with an increase. The largest increase productivity for the 1999-2000 can be seen in the oil sector, despite its technical efficiency in the overall economy swings for the whole period, there was not much change.