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 ...
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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.
Abolfazl Kazzazi; Maghsood Amiri; Fatemeh Rahbar Yaghoobi
Volume 8, Issue 20 , March 2011, , Pages 49-79
Abstract
In This paper, application of MCDM techniques in evaluation and ranking of Temad Co. strategies has been considered. Statistical target group, as "expert group" has been chosen among company's managers to identify strategies and evaluate them. At the first phase, strategies have been deducted with SWOT ...
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In This paper, application of MCDM techniques in evaluation and ranking of Temad Co. strategies has been considered. Statistical target group, as "expert group" has been chosen among company's managers to identify strategies and evaluate them. At the first phase, strategies have been deducted with SWOT technique and at the second phase, they have been ranked by ELECTR III technique. Criteria for evaluating strategies gathered among different resources and got summarized according to strategic experts' ideas. Weight of criteria has determined by RTC or PCT technique and thresholds have been dedicated by decision makers. To reduce uncertainty of decision making, ranking has been done in fuzzy situation. Furthermore, for assessing efficiency of results, sensitivity analysis has been implemented with two approaches. According to final results, evaluation and ranking of strategies can be done well by means of ELECTRE III technique.
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.
