Industrial management
Mohsen Kochaki; Behnam Vahdani
Abstract
The correct storage and arrangement of products in the warehouse increases the efficiency in responding to requests, accelerates the identification of products, increases the accessibility of the items in the warehouse, makes more use of the available space in the warehouse, determines the position of ...
Read More
The correct storage and arrangement of products in the warehouse increases the efficiency in responding to requests, accelerates the identification of products, increases the accessibility of the items in the warehouse, makes more use of the available space in the warehouse, determines the position of the products in the warehouse and does not damage it. maximum flexibility and optimal storage conditions are provided. By examining the studies that have been conducted in the field of warehousing and arrangement of products in the warehouse, and before arranging the products in the warehouse, comprehensively and comprehensively, according to the characteristics of the goods, they have not been categorized. Therefore, in this article, by using machine learning algorithms and data mining, according to the characteristics that are considered for the goods in the warehouse of Farasan industrial-production factory, the products are categorized and then the products are arranged in the warehouse using We have paid mathematical programming model. The purpose of the investigated problem in the field of warehousing and arrangement of products, in addition to categorizing products based on their characteristics, is to minimize the cost function obtained in relation to the mathematical planning model. Therefore, for the category Classification of goods is done using density-based algorithm (DB), self-organizing mapping neural network (SOM) and hierarchical clustering method (AGNES). The obtained results show that the SOM network has a better performance than the density-based algorithm and also the density-based algorithm has a better performance than the hierarchical clustering method.
Industrial management
elham aghazadeh; Akbar Alem Tabriz
Abstract
In today's industrial units, operators monitor equipment performance, and the challenging coordination between units in vast operating environments with high volumes of equipment can lead to irreparable damage. Despite considerable technological advancements in inspection and surveillance, this responsibility ...
Read More
In today's industrial units, operators monitor equipment performance, and the challenging coordination between units in vast operating environments with high volumes of equipment can lead to irreparable damage. Despite considerable technological advancements in inspection and surveillance, this responsibility can be effectively delegated to smart devices and the Internet of Things (IoT). Furthermore, the emergence of "edge computing" technology has prompted researchers to explore edge-based computing designs due to their numerous benefits. This study presents a combined model of IoT and civilian drones for intelligent monitoring of industrial equipment performance, employing an edge computing approach. The model is specifically investigated through a case study involving wind turbines. The model evaluates the performance of drones for intelligent monitoring of wind turbines in three stages: 1) Detection process, 2) UAV computational evacuation process, and 3) UAV local computation process. Given the dual purpose of the final model, which involves a combination of the aforementioned three steps, a genetic method was employed for problem-solving with negligible sorting. The amplified epsilon restriction method, utilizing random numbers, was also considered, but the combination of genetic and negligible sorting methods outperformed it, particularly in large problems where the enhanced epsilon restriction method struggled to provide timely responses due to the inherent complexity of the problem. IntroductionToday, in various industries, the productivity and efficiency of equipment contribute to the advancement of production and the profitability of production units. Beyond repair costs, equipment breakdowns also result in the expense of lost opportunities for the production unit. Without a solution to prevent these costs, bankruptcy for production units becomes a real possibility. Therefore, consideration should be given to a solution for the optimal monitoring of equipment. Clearly, swift action is crucial when any equipment is damaged, and such rapid response is unattainable through human effort alone. Despite significant technological advances in inspection and monitoring, this task can be delegated to smart tools and the Internet of Things (IoT). The IoT is regarded as one of the most crucial factors for the prosperity and progress of today's and future industrial businesses. Modernizing equipment is a priority for today's industries to quickly adapt to the evolving market changes and harness existing technologies. Businesses incorporating IoT into their infrastructure experience substantial growth in areas such as security, productivity, and profitability. As the use of industrial IoT increases, productivity levels in industries are naturally expected to rise. The IoT can accumulate massive amounts of information and data, enabling factories and companies to optimize their systems and equipment without being hindered by technological and economic limitations. However, a challenge arises from the substantial volume of data generated by the IoT, which is sent to cloud computing centers for processing. Centralized (cloud) processing results in high communication delays and lowers the data transfer rate between IoT devices and potential users, creating operational challenges in the network. To address this issue, the concept of edge computing has been proposed. Edge computing allows IoT services to process data near their own data sources and data sinks instead of relying on the cloud environment. This approach leads to reduced communication delays and more efficient utilization of computing, storage, and network resources. It also minimizes execution time and energy consumption, proving to be highly beneficial for IoT applications. Consequently, with the advent of "edge computing" technology, many researchers have embraced edge computing-based designs due to its numerous advantages.Materials and Methods In this research, a combined model of the Internet of Things and civilian drones was presented for the intelligent monitoring of industrial equipment, utilizing an edge computing approach. The model was investigated through a case study involving wind turbines. The performance of UAVs for intelligent monitoring of wind turbines was examined in three stages: 1) Detection process, 2) UAV computational evacuation process, and 3) UAV local computing process. Given the dual purpose of the final model, which involved a combination of the aforementioned three steps, the model was addressed using genetic methods with sparse sorting and the enhanced epsilon constraint method employing random numbers. The genetic method with sparse sorting outperformed the enhanced epsilon limit method, particularly in problems with large dimensions. The complexity of the problem made it challenging for the enhanced epsilon constraint method to provide timely responses in such cases.ResultsThe findings of this research offer valuable insights for the effective and accurate management and monitoring of industrial equipment across various industrial units, aiming to optimize costs, quality, and inspection time. Additionally, this research can provide guidance in considering regulatory restrictions in equipment placement before constructing an industrial unit. During the equipment arrangement phase, the model presented in this research can be utilized for optimal energy consumption and time management. As the combined model of the Internet of Things and civilian drones for intelligent monitoring of industrial equipment is a novel concept in the literature, there exist numerous opportunities for further development in this field. This may include the application of the model in additional case studies, such as enhancing the intelligent monitoring of power supply systems, fire services, etc. Moreover, there is potential for refining the mentioned model under conditions where drones operate simultaneously without a specific sequence.ConclusionFailure to monitor industrial equipment properly can result in substantial financial losses for factories and production units. The improper operation of equipment may lead to complete failure, necessitating the need for replacement. Additionally, increased equipment downtime, quality issues, reduced production speed, safety hazards, and environmental pollution can be consequences of equipment failure, ultimately diminishing the profitability of the production unit. Considering factors such as embargoes, emphasis on domestic production, and self-sufficiency, accurate supervision becomes economically crucial for factories.Effective management of the proper operation of industrial equipment is a fundamental requirement for every production unit, given that industrial equipment represents a significant investment for the unit. If device maintenance is limited to repairs only after breakdowns occur, production devices will consistently face unexpected halts, preventing production productivity from reaching its predetermined goals. Therefore, designing a framework for the "intelligent monitoring of the performance of all relevant industrial equipment" stands as one of the most crucial actions for any production unit. Depending on the type of equipment, monitoring the performance of industrial equipment may encompass periodic inspections, maintenance and repair planning, and scheduling the optimal operational time for the equipment
Industrial management
davod dehghan; Kiamars Fathi Hafshejani; Jalal Haghighat monfared
Abstract
The importance of mass biology has increased due to pollution caused by biomass burial, the profitability of biomass energy, and the demand for energy in the supply chain network. The goal of this research is to design a model for the biomass supply chain network with an economic and ecological approach ...
Read More
The importance of mass biology has increased due to pollution caused by biomass burial, the profitability of biomass energy, and the demand for energy in the supply chain network. The goal of this research is to design a model for the biomass supply chain network with an economic and ecological approach to reduce costs and carbon emissions. Research gaps have been addressed, which include determining desired and undesired process outputs, along with simultaneously examining material supply disruptions and final product demand. The mathematical model used is a mixed-integer linear programming model. The primary objective is to minimize costs, and the secondary objective is to minimize carbon emissions. To address this in a single-target function under uncertainty, the fuzzy TH mathematical model has been employed. Uncertainty and disruptions have been studied through scenario building. The model's validation includes a case study in Fars province, where the findings justify the construction of four power plants. The proposed model improved the accuracy of electricity production predictions by 2.1 percent. An analysis and sensitivity study was performed on the TH method's parameters and changes in customer demand values according to predictions. The results show that the proposed model performs well, achieving cost-effectiveness through the integration of economic and ecological approaches. It also successfully reduces greenhouse gas emissions, enhances energy security and stability, and demonstrates a positive impact.
Introduction
More than 70 thousand tons of biomass waste are produced in Iran daily. These waste products result in the generation of methane gas and carbon dioxide, leading to severe air pollution and climate changes in the country. Given that 14% of Iran's electricity production comes from hydropower, and the nation is grappling with drought, electricity generation has decreased, leading to government-imposed power cuts, particularly in industrial areas. To address the need for biomass resource investment in energy production, the main challenge is the absence of an optimization model for the biomass supply chain that encompasses all relevant factors. Hence, this research aims to design a flexible optimization model for the biomass supply chain, offering insights to investors on how to produce energy with reduced costs and lower carbon emissions. Key research gaps identified are as follows: 1-Simultaneously addressing uncertainty arising from disruptions in the first two levels of the supply chain, encompassing biomass supply from raw materials, and examining the fourth level - the customer level - by defining scenarios. 2- Innovatively considering capacity levels in the context of the biomass supply chain, a subject not widely explored before. 3- Focusing on the production of bioenergy in conjunction with by-products. 4- Deliberating on the definition of desired outputs at separation centers. 5- Highlighting the importance of considering undesired outputs at separation centers. 6- Proposing a stochastic-probabilistic-fuzzy planning approach to enhance flexibility, particularly in managing risks and operational disruptions.
Research Method
This network encounters two types of uncertainty, both of which cause disruptions. Consequently, four scenarios have been devised to address these disruptions: 1- The scenario involving reduced raw material supply due to drought's impact. 2- The scenario in which electricity demand decreases in response to specific conditions. 3- The scenario where both of the aforementioned scenarios occur simultaneously. 4- A scenario without any disturbances. As a result, a resilient model has been developed to manage disturbances while ensuring environmental sustainability. The proposed model is a mixed-integer linear programming mathematical model with two objective functions: cost minimization and carbon emission minimization. The model is solved using the exact solution method in conjunction with Gomes software. To address function targeting under uncertainty, the fuzzy TH mathematical model has been employed. The model's validation has been examined through a case study in Fars province.
Findings
Several findings have emerged from the study: The construction of four power plants is recommended, each to be located at one of the ten proposed sites, with each having a different capacity. The proposal includes the establishment of four biomass separation centers. Different types of biomass are utilized in the power plants in varying proportions. Biomass transportation involves three types of transporters with capacities of ten tons, fifteen tons, and twenty tons. The quantity of these transporters varies across different separation centers and power plants. Electricity is supplied to six different applicants. The quantity of fertilizer produced varies according to different scenarios and time periods. The sensitivity analysis reveals that increasing the coefficient of the first objective function results in a decrease in the values of the first objective function. Conversely, decreasing the coefficient of the second objective function simultaneously leads to an increase in the value of the second objective function.
Conclusion
The model designed for this purpose is a sustainable development model that encompasses two of the three sustainability aspects, namely, the reduction of greenhouse gas emissions and the minimization of economic costs. Therefore, it is a resilient model that employs a scenario-based approach to address various forms of uncertainty. In the case of this study, raw materials were procured from nine out of ten biomass supply centers, indicating resilience in terms of biomass supply. The model optimally allocates resources among the supply chain members to minimize greenhouse gas emissions while also considering cost-effectiveness. The inclusion of favorable and unfavorable outputs in the model impacts the annual electricity production of each power plant. Without these variables, the model would overestimate electricity production. Sensitivity analysis reveals the trade-off between objective functions, confirming the model's correct and logical performance. Therefore, the model's validity is established. It is recommended that, in further development of this model, specific travel times for trucks between locations be included in the model.
Industrial management
Mina Kazemian; Mohamad Ali Afshar Kazemi; Kiamars Fathi Hafshejani; Mohammad reza Motadel
Abstract
IntroductionThe field of supply chain management has focused on crucial topics such as coordination, cooperation, and competition among chain members. Game theory has emerged as a valuable tool for examining supply chain management issues, as it analyzes various situations and their impact on supply ...
Read More
IntroductionThe field of supply chain management has focused on crucial topics such as coordination, cooperation, and competition among chain members. Game theory has emerged as a valuable tool for examining supply chain management issues, as it analyzes various situations and their impact on supply chain performance (Naimi Sediq et al., 2013; Shafi'i et al., 2018). While every action and performance within the supply chain influences the outcomes of the game, it does not solely determine them. The goal is to balance the parties involved in the supply chain and create satisfaction for the end customer (Metinfer et al., 2018).Although extensive research has been conducted in supply chain management within the steel industry, the impact of sanctions on Nash equilibria and the application of three approaches (Cournot, Stackelberg, and collusion) to achieve game balance in different scenarios have not been thoroughly investigated. This research aims to fill this gap by addressing the mentioned research question. The current study focuses on determining the optimal price using an intelligent decision-making system based on game theory within the steel industry, considering the presence or absence of the sanctions variable.Our country currently possesses several relative advantages in terms of steel production conditions, including abundant and affordable energy, iron ore and refractory raw materials, considerable steel production experience, and a skilled and cost-effective workforce. By acquiring new production technology, these advantages enable our country to play a competitive and influential role in the global steel market. However, the steel industry faces significant challenges such as price fluctuations, extreme price disparities across regions, and delayed delivery due to a lack of efficient supply chain management. Therefore, the main research question aims to provide a model that incorporates key variables, such as the supply and demand of final and intermediate products in the steelmaking industry and the future trends in production and quantity changes.Research methodThis article introduces a composite model that combines artificial neural networks and game theory to assist stakeholders in the steel industry in determining optimal production levels and price levels. To predict the price of steel, three techniques were employed: Bayesian neural networks, support vectors, and Grassberg anti-diffusion. Additionally, to address the issue of binary identification in the neural network, three different network structures were introduced: feedforward network structure, competitive network structure, and backward associative memory network structure.Research findingsThe first scenario is the non-cooperative game (Cournot model scenario) where each participant aims to maximize their profit and would not deviate from their strategy as it would lead to a reduction in their profits. The second scenario is the sequential non-cooperative game (Stackelberg model scenario), in which two chains engage in a confrontation of the Stackelberg game type. The leader's goal is to determine the best strategy while considering all rational strategies that follower players can employ to maximize their income. This scenario demonstrates that the rate of price and profit increase is lower compared to sequential and cooperative game modes. The third scenario is the cooperative game (collusion model scenario). In this scenario, the allocation of profits among the cooperating members is crucial to ensure the stability of their cooperation. The Grassberg anti-diffusion method exhibits higher accuracy due to its higher true positive (TP) and true negative (TN) values compared to other algorithms. Additionally, it has fewer false positives (FP) and false negatives (FN) because a higher TP and TN indicate more accurate predictions in the tested dataset, while FP and FN represent incorrect predictions. The inclusion of the sanctions variable as a moderating factor in the steel price forecasting model accounts for the potential reduction in production and increased cost price. Through the model, it was discovered that the Grossberg method yields more accurate steel price forecasting. Consequently, price forecasting in the model is based on the Grossberg method.Research resultsThe results indicate that transitioning from the Cournot game to the Stackelberg game and from the Stackelberg game to the collusion game in the steel industry's supply chain leads to a $6 increase in price per ton and a product supply ranging from 1500 to 4000 tons. In other words, as collusion in the steel market intensifies, more products are introduced into the market, resulting in an increase in product prices and a decrease in the welfare of steel consumers. According to the findings, increased competition in the industry reduces the profitability and production levels of producers while enhancing consumer welfare. Conversely, higher levels of monopoly exhibit the opposite effect. To maintain a balanced supply chain in the steel industry and prevent potential problems, it is recommended to adopt the Stackelberg game approach, which aligns more closely with reality. It is worth noting that the order in which players enter the game impacts the Nash equilibrium. Therefore, exploring market entry monitoring regulations and rules in this industry becomes crucial since the steel industry involves high entry and exit costs. Policymakers and industry managers should consider monitoring the entry and exit of players, formulate game standards and rules among market participants. Based on the results, the primary recommendation of this research is to increase competition intensity and adopt the Cournot approach in the industry to reduce prices and increase production. Additionally, enhancing international relations and diplomatic efforts will mitigate the impact of sanctions on the industry, leading to cost price improvements and an increase in the level of comparative advantage at the international level.
