Can Producers’ Price War End Up in an Optimal Allocation?

The paper presents a theoretical framework for the phenomenon of the price war in the context of general equilibrium, with special attention to the production system. The natural question that arises is whether Nash-optimal production plans being the reactions to the changing prices can finally approximate a Nash-optimal production plan at the end of this war. To provide an answer, the production system is described as a parametric-multicriteria game. Referring to some results on the lower semicontinuty of the parametric weak-multicriteria Nash equilibria, we provide a positive answer for the stated problem.

A Solution to Ensure Ventilation when Expanding the Area of Cam Thanh Underground Coal Mine, Ha Long Coal Company, Vietnam

In the process of underground mining, the mining system changes for various reasons. One ofthe main reasons is changes in the mining production plans, especially the scales and outputs. Nowadays,coal mines in Vietnam have been expanding in width and depth, and so have the mines’ ventilationsystems. Consequently, there will be changes in the alteration of the structure of the design ventilationsystem, which reduces the effectiveness of the ventilation and does not meet the main objective of mineventilation, directly affect the safety of the working environment in the mine. Therefore, it is necessary toresearch the improvement of the ventilation system with the development and specific conditions ofunderground coal mines in Vietnam, improving the efficiency of the ventilation work and assuring thesafety of the mine environment. Cam Thanh coal mine, Ha Long coal company, Vietnam, is the case studyfor this research. The article considers the plan of increases the mining output by more than 1.5 times,propose solutions to improve the ventilation system accordingly, helping the company proactivelyimplement the production plan, ensure the working environment's safety, and reduce the costs of mineventilation.

Time series analysis and forecasting of China’s energy production during Covid-19: statistical models vs machine learning models

COVID-19 is a huge catastrophe of global proportions, and this catastrophe has had far-reaching effects on energy production worldwide. In this paper, we build traditional statistical models and machine learning models to forecast energy production series in the post-pandemic period based on Chinese energy production data and COVID-19 Chinese epidemic data from 2018 to 2021. The experimental results showed that the optimal models in this study outperformed the baseline models on each series, with MAPE values less than 10. Further studies found that the LightGBM, NNAT and LSTM machine learning models worked better in unstable energy series, while the ARIMA statistical model still had an advantage in stable energy time series. Overall, the machine learning models outperformed the traditional models during COVID-19 in terms of prediction. Our findings provide an important reference for energy research in public health emergencies, as well as a theoretical basis for factories to adjust their production plans and governments to adjust their energy decisions during COVID-19.

Time series analysis and forecasting of China’s energy production during Covid-19: statistical models vs machine learning models

Covid-19 was a huge catastrophe for the whole world, and this catastrophe has had far-reaching effects on energy production worldwide. In this paper, we build traditional statistical models and machine learning models to forecast energy production series in the post-pandemic period based on Chinese energy production data and Covid-19 Chinese epidemic data from 2018 to 2021. The experimental results showed that the optimal models in this study outperformed the baseline models on each series, with MAPE values less than 10. Further studies found that the machine learning models LightGBM, NNAT and LSTM worked better in the unstable energy series, while the statistical model ARIMA still had an advantage in the stable energy time series. Overall, the machine learning models outperformed the traditional models in Covid-19 in terms of prediction. Our findings provide an important reference for energy research in public health emergencies, as well as a theoretical basis for factories to adjust their production plans and governments to adjust their energy decisions during Covid-19.

Time series analysis and forecasting of China’s energy production during Covid-19: statistical models vs machine learning models

Covid-19 was a huge catastrophe for the whole world, and this catastrophe has had far-reaching effects on energy production worldwide. In this paper, we build traditional statistical models and machine learning models to forecast energy production series in the post-pandemic period based on Chinese energy production data and Covid-19 Chinese epidemic data from 2018 to 2021. The experimental results showed that the optimal models in this study outperformed the baseline models on each series, with MAPE values less than 10. Further studies found that the machine learning models LightGBM, NNAT and LSTM worked better in the unstable energy series, while the statistical model ARIMA still had an advantage in the stable energy time series. Overall, the machine learning models outperformed the traditional models in Covid-19 in terms of prediction. Our findings provide an important reference for energy research in public health emergencies, as well as a theoretical basis for factories to adjust their production plans and governments to adjust their energy decisions during Covid-19.

Total Productive Maintenance and its Effecting on the Application of Lean Production System

Increasing the amount of production, the diversity of products, a commodity and / or service, and increasing the productivity factor ratios contribute to developing the competitive strength of the organization in light of the increasingly difficult market conditions. That made all organizations work according to competitive strategies, including the production strategy for the purpose of achieving the organizations goal through the set of goals that they put. They rely on several new management systems of a strategic nature aimed at their survival and continuity in the production market. Hence, this research aims to evaluate the total productivity maintenance capacity in lean production throughout reducing the various kinds of losses, as the lean production is based on reducing each defective product (a commodity and / or service), costs, errors, and area, and all that and others aimed at improving product quality and customer satisfaction. However, the overall goals and production programs often encounter unexpected breakdowns at unexpected times, which lead to a breakdown in production and an imbalance in production plans as a result. Consequently, the companies suffer the expected revenue loss because they fail to reach the targeted production amount. The research has adopted a questionnaire that has been distributed to (50) employees of the General Company for Electric Power Production, the central region in Baghdad, which constituted more than 10% of the company’s employees. The results show that there is a strong direct relationship between the independent variable (total productivity maintenance) and the dependent variable (lean production). This distinction has explained 90% of the variables in the dependent variable.

A COMPARATIVE STUDY OF THE USABILITY OF DIFFERENT PRODUCTION SCHEDULING ALGORITHMS AND RULES

Production scheduling optimization is a very important part of a production process. There are production systems with one service object and systems with multiple service objects. When using several service objects, there are systems with service objects arranged in a parallel or in a serial manner. We also distinguish between systems such as flow shop, job shop, open shop and mixed shop. Throughout the history of production planning, a number of algorithms and rules have been developed to calculate optimal production plans. These algorithms and rules differ from each other in the possibilities and conditions of their application. Since there are too many possible algorithms and rules it is not easy to select the proper algorithm or rule for solving a specific scheduling problem. In this article we analyzed the usability of 33 different algorithms and rules in total. Each algorithm or rule is suitable for a specific type of problem. The result of our analysis is a set of comparison tables that can serve as a basis for making the right decision in the production process decision-making process in order to select the proper algorithm or rule for solving a specific problem. We believe that these tables can be used for a quick and easy selection of the proper algorithm or rule for solving some of the typical production scheduling problems.

Dynamic Modeling in Making Scientifically Grounded Business Decisions

The article considers the methods of dynamic modeling and features of their practical implementation for making scientifically sound business decisions. The article provides the classical theory of economic dynamics and forecasting, its development in the Ukrainian school of dynamic modeling with practical application in business management under certainty, risk, and uncertainty. The application of sequential analysis of variants, a new method of dynamic modeling, is substantiated. The authors suggest an original approach to forecast business development and optimize the investment allocation, logistical and human resources, the efficiency of calculations of production plans and programs, etc.

Analyzing Indicators of the Results of Applying Forecasting Methods for Production Plans: A Case Study at the Diyala State Company for Electrical Industries

Economic organizations operate in a dynamic environment, which necessitates the use of quantitative techniques to make their decisions. Here, the role of forecasting production plans emerges. So, this study aims to analysis of the results of applying forecasting methods to production plans for the past years, in the Diyala State Company for Electrical Industries. The Diyala State Company for Electrical Industries was chosen as a field of research for its role in providing distinguished products as well as the development and growth of its products and quality, and because it produces many products, and the study period was limited to ten years, from 2010 to 2019. This study used the descriptive approach in the theoretical side of the study, on the practical side, the current study used the statistical application (SPSS), and some other statistical means to process and analyze what was collected from data related to the company, the research sample, from its official website. The research concluded that flooding the Iraqi market with alternative imported products for the products of the Electrical Industries Company led to the cessation of production of some of the company's products, as a result, its resources were not invested efficiently and production costs were high. The current study suggested to the Ministry of Electricity to buy the company's products, as these products have a good quality comparing with the foreign ones. Received: 10 June 2021 / Accepted: 12 August 2021 / Published: 5 September 2021