جستجوی مقالات مرتبط با کلیدواژه "o" در نشریات گروه "صنایع"

Ensuring an adequate and healthy blood supply is a persistent challenge that healthcare systems worldwide face. The need for blood donors and their products is constant, while the supply from donors is somewhat irregular, and the demand for these products is often unpredictable. Furthermore, the levels of demand and blood donation are uncertain. As a result, uncertainty plays a crucial role in the blood supply chain, especially during crises such as earthquakes and pandemics. In this regard, designing the  Blood Supply Chain Network (BSCN) under uncertainty is essential for meeting fluctuating demand, addressing logistical challenges, responding to emergencies, and ensuring the quality and safety of blood products throughout the supply chain. This research aims to present a Mixed-Integer Linear Programming (MIP) model under uncertainty for strategic and tactical decision-making in the blood supply chain over a determined planning horizon. The fuzzy theory approach has been used to incorporate uncertainty into the model's parameters. An interactive fuzzy solution approach based on credibility measurement has been developed to solve the fuzzy optimization model. The results obtained from designing and implementing the proposed model in a case study indicate the desirable efficiency of this model in determining the optimal number and location of facilities in a BSCN, including fixed facilities, temporary facilities, and blood banks, as well as the optimal amount of blood transfer between different entities of the blood supply chain. Furthermore, a sensitivity analysis of the parameters is performed to determine the most influential factors affecting the objective function of the problem.

Nowadays, online shopping plays a vital role in providing services and delivering goods to customers in the context of business intelligence and e-commerce. This research analyzes the customer purchase data of an Iranian online shopping company in Tehran. Among the available datasets provided by the company, 200 thousand records of one week of transactions have been selected for the present study. Several classification methods (i.e., Random Forest, gradient-boosted trees, K-Nearest Neighbor (KNN), Naïve Bayes, Kernel Naïve Bayes, and Neural Networks) and clustering approaches have been applied to discover the knowledge and patterns. The results show that before balancing the dataset, the KNN algorithm with K=5 is the best classification method among the existing methods. However, after balancing, gradient-boosted trees outperform the other classification methods. For clustering methods, the results show that the K-Means algorithm with K=3 is more efficient regarding the average within centroid distance for each cluster. Finally, concluding remarks and suggestions for future studies are stated.

The common presuppositions and limitations regarding the Resource Constrained Project Scheduling Problem (RCPSP) were investigated in addition to their reliability in modeling in order to investigate the possibility of availability of renewable resources using a new attitude. The objective of modeling RCPSP was the quantification of total costs and minimization of delays in projects. Hence, in order to mathematically model RCPSP, the first non-linear complex integer math programming was transformed into a linear programming model using the features of exponential functions. To solve the final linear math problem, some experimental examples were designed in different dimensions aiming to study the performance and efficiency of the designed model. For solving low-dimension problems, the exact (epsilon) constraint multi-objective optimization method was used in the Lingo software. A meta-heuristic algorithm called NSGA-II was employed to find solutions for high-dimension problems that the Exact method could not solve. The results of using these algorithms and the statistical analysis (with 95% reliability) indicated that the performance was suitable for the Genetic Algorithm (GA). The calculation error between the Exact method and the meta-heuristic method for the three target categories of total cost, time delay, and reliability was calculated based on the obtained results. The number of errors in calculating the total cost was 26%, 19%, and 5%, respectively. Also, the delay objective function error was equal to 28%, 24%, 12 %, and 14%, respectively. Finally, the reliability objective function error value was equal to 8%, 3%, 29%, and 36%, respectively. Accordingly, it can be concluded that this meta-heuristic algorithm (GA) has more efficiency and more apposite performance for the recommended model compared with the Exact optimization software. The use of the math model designed in this study can result in decreasing the time delays in projects and the costs of scheduling problems, as well as increasing the reliability in multi-mode activities.

This study introduces an advanced performance measurement system for 31 municipalities in Tehran and Shahriar, integrating the Balanced Scorecard (BSC) and Data Envelopment Analysis (DEA) methodologies. The combination of BSC and DEA was chosen because BSC offers a multidimensional framework for assessing performance from diverse perspectives, while DEA provides a quantitative tool for evaluating efficiency, particularly useful when dealing with multiple inputs and outputs. Together, they allow for both qualitative and quantitative evaluation of municipal performance, addressing the need for comprehensive performance assessment. However, traditional DEA models often fail to account for dynamic changes and intermediate linkages between these perspectives over time. The Dynamic Network Slacks-Based Model (DNSBM) of DEA, proposed in this study, addresses these limitations by incorporating both network interdependencies and dynamic changes in performance evaluation. Field studies and expert interviews revealed interconnections between BSC perspectives, and dynamic changes were modeled by linking networks over multiple periods. The model estimated efficiency values for each period, showing an average overall score of 0.857, with specific scores for financial (0.94), learning and growth (0.83), internal processes (0.96), and customer (0.34). Statistically significant correlations were found between most perspectives, except financial and learning/growth. The model identified dynamic performance trends, inefficiency levels, and strategies to improve underperforming DMUs, offering a comprehensive approach to enhancing municipal performance.

The supplier is a crucial component of the supply chain, with the supply chain encompassing vendor management. Selecting an industry-appropriate supplier involves assessing the supplier's performance. This selection process not only satisfies customer requirements and generates profit for the organization but also ensures compliance with all defined supplier attributes. The task of choosing an appropriate supplier is essential and can often be challenging. The objectives of the supply chain include enhancing productivity, reducing costs, and meeting the demands of emerging markets. This thesis introduces the concept of relative reliability risk assessment, particularly for new suppliers. Measuring performance is critical for vendor selection, as failure to do so can negatively impact the organization's reputation. The paper employs a four-tier method establishing a functional structure, applying the Analytical Hierarchical Process (AHP), utilizing the entropy method, and organizing alternative functionality graphs. The model's effectiveness is demonstrated through empirical case studies and comparisons with traditional supplier selection methods, showcasing its capability to manage uncertainty and improve decision accuracy.Multiple alternatives and criteria are considered to facilitate the decision-making process for selecting suitable vendors. The AHP is utilized for multi-attribute decision-making among various vendors, effectively addressing the inherent biases, vagueness, and subjectivity in human decision-making. The Eigenvalue of the AHP is calculated in Excel using specific formulas, with the consistency index being determined each time. The entropy method is applied to calculate the weights of different attributes, which are then used to compute the Relative Reliability Risk Index. The alternative functionality graph displays the strengths and weaknesses of all alternatives concerning multiple attributes. A real-life case study demonstrating the application of Multiple Attribute Decision Making (MADM) with the proposed method is presented in this paper. The suggested model offers valuable insights for practitioners aiming to enhance supplier selection strategies and boost overall supply chain performance.

Human resources are undoubtedly the most crucial resources of organizations. A significant part of the human resources includes retirees of the organization. Organizations must provide adequate support to acknowledge their years of service to facilitate retirees' adaptation to new circumstances. This study investigates retirement adjustment among personnel of the Yazd Electricity Distribution Company (YEDC). For this purpose, the challenges and problems that discourage people from retiring are identified first. Based on these challenges, retirement adjustment solutions are proposed. The retirement adaptation solutions have been ranked based on three criteria: financial promotion, identity improvement, and interaction improvement, using Shannon’s Entropy and TOPSIS techniques. The extraction of factors in two categories of challenges and solutions represents a contribution of this research. Furthermore, this research examines the different views of personnel with varying job levels, work experience, and genders through statistical analysis, which is another contribution of this research. Finally, the results of this research show the ranking of solutions using combined Shannon’s Entropy and TOPSIS techniques, which emphasize the novelty of this research.

Nowadays, one of the major concerns of investors is choosing a realistic stock portfolio and making proper decisions according to an individual's utility level. It is essential to consider two conflicting goals of return and risk for profitability; as a result, balancing the above goals has been identified as an investment concern. This paper modifies and optimizes a multi-objective and multi-period stock portfolio considering cone constraints and uncertain and stochastic discrete decisions. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was used to solve the model due to the issue's complexity. Two objective functions in the model could be explained by maximizing expected returns and minimizing investment risk. The Pareto chart of the problem was drawn, which allows investors to make decisions based on various levels of risk. Another result obtained in this study is calculating the percentage of optimal amounts assigned to each asset, providing a base for investors to avert investing in unsuitable assets and incurring losses. Finally, a sensitivity analysis of essential parameters was performed, which is critical in this issue. According to the results, increasing the number of problem constraints provides a base for the model reaction, and the optimal percentage allocated to each asset varies. Therefore, this prioritizes restrictions in different situations and according to the investors' choice.

Blockchain technology has emerged as a revolutionary tool for enhancing transparency, efficiency, and security across various industries, particularly in supply chain management. This study investigates the dimensions, components, and key indicators integral to a blockchain-based supply chain. Drawing on an extensive literature review and empirical analysis, the research highlights the transformative potential of blockchain in fostering trust, reducing costs, and ensuring traceability. The study employs qualitative and quantitative methods to explore the adoption barriers, implementation strategies, and critical success factors. Findings underscore the pivotal role of smart contracts, decentralized data sharing, and interoperability standards. This paper discusses implications for practitioners and policymakers, outlining future research avenues to optimize blockchain deployment in supply chains.

Portfolio optimization is a widely studied problem in financial engineering literature. Its objective is to effectively distribute capital among different assets to maximize returns and minimize the risk of losing capital. Although portfolio optimization has been extensively investigated, there has been limited focus on optimizing portfolios consisting of cryptocurrencies, which are rapidly growing and emerging markets. The cryptocurrency market has demonstrated significant growth over the past two decades, offering potential profits but also presenting heightened risks compared to traditional financial markets. This situation creates challenges in constructing portfolios, necessitating the development of new and improved risk management models for cryptocurrency funds. This paper utilizes a new risk measurement approach called Conditional Drawdown at Risk (CDaR) in constructing portfolios within high-risk financial markets. Traditionally, portfolio optimization has been approached under certain conditions, considering risk and profit as decision criteria. However, recent approaches have addressed uncertainty in the decision-making process. To contribute to the advancement of scientific knowledge in this field, this paper proposes a new mathematical formulation of CDaR based on a chance-constrained programming (CCP) approach for portfolio optimization. To demonstrate the effectiveness of the proposed model, a practical empirical case study is conducted using real-world market data from 10 months focused on cryptocurrencies. The results obtained from this model can provide valuable guidance in making investment decisions in high-risk financial markets.

In response to the dynamic requirements of contemporary businesses, this research delves into the imperative for organizations to optimize the efficiency, flexibility, and sustainability of their value chains, especially concerning energy consumption. The study introduces a multi-objective model meticulously designed to align efficiency, flexibility, and sustainability, aiming for a well-balanced and optimal energy consumption profile throughout the entire value chain. The optimization process employs multi-objective programming, with the overarching objective of maximizing minimum levels of flexibility, stability, and efficiency while minimizing the maximum energy consumption. Addressing the intricacies of large-scale multi-objective models, the research proposes a two-phase Multi-Objective Evolutionary Algorithm (MOEA), leveraging the strengths of NSGA-II and MOACO. The effectiveness of the proposed model is substantiated through a series of numerical experiments and sensitivity analyses, providing conclusive evidence of its capability to navigate the complexities of optimizing energy consumption in value chains. Furthermore, the performance of the proposed algorithm is affirmed through the examination of indicators such as generation gap (GD), high volume (HV), error ratio (ER), and non-dominant vector generation (ONVG). Hence, the presented model and solution algorithm are suitable for real-world problems.

The aim of this research is to present a model for valuing new industrial projects in Iran's automotive industry. In this regard, a mixed-methods approach (qualitative and quantitative) was utilized. The qualitative part of the research employed grounded theory and involved interviews with 15 professors, experts, and specialists in the fields of valuation and automotive industry, using purposeful sampling to design the conceptual model of the research. In the next phase (the quantitative phase of the research), a fuzzy Delphi approach was used to screen and validate or reject these categories. The results showed that all indicators received a score higher than 0.7, thus confirming all 37 identified sub-categories by the experts. Subsequently, the fuzzy TOPSIS approach was applied to prioritize each of the sub-categories within the framework of the main categories. The results of this section also indicated that in the causal factors section (idea generation), the highest scores were related to organizational business policies and customer needs assessment. In the contextual factors section, the highest scores were related to market structure and product structure, respectively. In the intervening factors section, prioritization was given, in order, to the company's capabilities, market and stakeholders, and laws and regulations. The main categories were prioritized as including product portfolio management and quality management. The actions and interactions of the model also included process screening indicators, product value engineering, financial screening, economic screening, and after-sales services. Finally, the outcomes prioritized included initial prototyping, initial market testing, and pilot production.

Blockchain is a technology that can be used in various organizations. Blockchain, through a decentralized computer network, leads to the facilitation of high-level transactions of organizations as well as their registration, in order to better respond to people's needs. In this research, a preliminary conceptual model consisting of the behavioral factors of blockchain technology acceptance in the banking industry, which is derived from theoretical literature and research background, is presented. Behavioral factors include facilitating conditions, attitude, literacy and skill, perceived risk, technology, perceived behavioral control, external motivation, internal motivation, competition and mental norm. Then, in order to check the fit of the model, structural equation modeling and smart pls software were used using a researcher-made questionnaire extracted from the research model. For this purpose, due to the unlimited size of the statistical population, 384 samples were randomly selected and the questionnaire was distributed among them. The result indicates that all the relationships are significant and the factors cause more than 80% of changes in technology adoption. In addition, agent-based modeling and Anylogic software have been used in order to predict changes in the adoption of blockchain technology over time, affected by the identified behavioral factors. The results showed that with the improvement of behavioral factors, the adoption of blockchain technology increases over time. In this study, insight is generated for key decision makers and relevant policy makers to propagate the adoption of blockchain technology in the banking industry.

One of the constant problems that people with mental health conditions are faced with now is that they cannot establish a good relationship with their therapist, or the client's disease type is not in the therapist's specialty. These clients may not receive adequate treatment and stop the therapy before feeling well. Therefore, the classification of mental patients based on their disorder types and allocating a therapist with the same expertise to them could lead to better treatment and improve the quality of the therapy sessions. This paper will compare several machine learning (ML) algorithms to classify patients with mental conditions. Moreover, benefiting from the best ML algorithm, patients will be categorized into different classes based on their disorder types. Finally, a mathematical model will be developed to determine the allocation policy of therapists to each group of patients to maximize the summation of the utilization between therapists and patients. To explore the implementation of the proposed method, we have conducted a real-life case study to assess the validation of the model.

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.

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.

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.