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 ...
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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.
Akram Oveysiomran
Abstract
Input and output selection in Data Envelopment Analysis (DEA) has many important. In this research, inputs and outputs of reginal power companies are selected with artifitial neural network. The application of neural network in the selection of inputs and outputs of reginal power companies is not a precedent ...
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Input and output selection in Data Envelopment Analysis (DEA) has many important. In this research, inputs and outputs of reginal power companies are selected with artifitial neural network. The application of neural network in the selection of inputs and outputs of reginal power companies is not a precedent in the literature and it is considered the main advantage of the proposed method. In order to train two layers MLP neural network, after presenting of error resilience, learning method was used. After neural network training, neural network performance is examined by using the test set. RMSE value for 15 test set equals 0/0269 which reflects the high accuracy of training network. The Sensitivity Analysis of the studied parameters which are the same inputs and outputs of Data Envelopment Analysis, with ten percent increase of parameter, compared to the prior one was carried out and output relative error average for neural network parameters was calculated. Based on the output relative error average, inputs and outputs were determined. By comparing the efficiency scores of regional electricity companies before and after reducing the number of variables, it is noticed that the number of efficient companies during the above four periods decreased from 50 percent to 11 percent. Finally, the neural network application in inputs and outputs selection of the regional electricity companies was unprecedented in the literature and this is the main advantage of this method.
Abolfazl Kazemi; Javad Ghasemi; Vahid Zandieh
Volume 9, Issue 23 , December 2011, , Pages 131-161
Abstract
Previously, decision about granting facilities to clients of banks in Iran were made based on personal judgment about the risk of failure in reimbursement. But, increasing demand for bank facilities by economic firms and households in one hand, and increasing extensive commercial competition among banks ...
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Previously, decision about granting facilities to clients of banks in Iran were made based on personal judgment about the risk of failure in reimbursement. But, increasing demand for bank facilities by economic firms and households in one hand, and increasing extensive commercial competition among banks and economic- credit companies in the country and their efforts to alleviate the risk of failure in reimbursement of facilities on the other hand, have resulted in using modern methods such as statistical methods in this area. Today, to predict the possibility of failure in reimbursement of facilities and to classify their applicants, banks use the credit ranking of their clients. Savings in time and costs, removing personal judgments and increasing the accuracy of evaluating applicants of various facilities are some of the benefits gained in this method.
There are various statistical methods such as audit analysis, logistic regression, nonparametric smoothing and other methods including neural networks which have been used in ranking the credits. Among these methods, neural network method is of higher flexibility and has attracted more attention in recent years due to its ability to classify, generalize and learn the patterns.
In this paper, firstly we select some of the important criteria in granting various credit facilities such as financing loan, civil partnership, installment sale and unilateral contract to natural clients of a private bank in the country using questionnaire and the opinions of elite people in the field of banking. Then, we classify them by
presenting four models of neural networks namely MOE, MLP, LVQ and RBF and evaluate the accuracy of the ranking of these models. The obtained results indicate that MOE model is more accurate compared to MLP and RBF models and LVQ has not acceptable accuracy for ranking the credits of bank applicants.
Mirza Hassan Hosseini; Abdolhamid Safaee; Somayeh Alavy nezhad
Volume 8, Issue 19 , December 2010, , Pages 217-238
Abstract
Nowadays quantitative methods have become very important tools for forecasting purposes in markets as for improved decisions and investment. Forecasting accuracy is one of the most important factors involved in selecting method; Artificial Neural Network (ANNs) arc flexible computing frameworks that ...
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Nowadays quantitative methods have become very important tools for forecasting purposes in markets as for improved decisions and investment. Forecasting accuracy is one of the most important factors involved in selecting method; Artificial Neural Network (ANNs) arc flexible computing frameworks that can be applied to a wide range of forecasting with a high degree of accuracy, in this research, Fuzzy logic and Artificial Neural Network are integrated in to the Fuzzy Back- Propagation Network (FBPN) for sales forecasting in Wood and Paper industry. The proposed system is evaluated through the real world data provided by a Wood an d Paper company and experimental results indicate that the Fuzzy Back- Propagation approach outperforms are better other two different forecasting models (Linear Regression and ARIMA time series model) in MAPE measures.
Jamshid Salehi Sadaghiani; Seyed Amir Reza Abtahi
Volume 4, Issue 13 , June 2006, , Pages 89-122
Abstract
The purpose of this article is about soft computing and its different methods for modeling phenomena. Soft Computing refers to the evolving collection of methodologies used to build intelligent systems exhibiting human-like reasoning and capable of tackling uncertainty.
In this paper, we describe the ...
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The purpose of this article is about soft computing and its different methods for modeling phenomena. Soft Computing refers to the evolving collection of methodologies used to build intelligent systems exhibiting human-like reasoning and capable of tackling uncertainty.
In this paper, we describe the neural networks approach in soft computing at first. Then, other approaches such as genetic algorithm and machine learning will be described. Since the main goal of building the model is knowledge extraction, finally, we will describe the various methods of knowledge and rule extraction from neural networks.
