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.
supply chain management
R. Ghasemy Yaghin; Fateme Darvishi
Abstract
This paper presents a global supplier selection model for the textile and clothing industry using a fuzzy multi-criteria group decision making approach. Then, the order quantity of each supplier is determined by a mathematical programming model. In the first step, a group fuzzy analysis hierarchical ...
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This paper presents a global supplier selection model for the textile and clothing industry using a fuzzy multi-criteria group decision making approach. Then, the order quantity of each supplier is determined by a mathematical programming model. In the first step, a group fuzzy analysis hierarchical process approach is used to obtain the overall weight of the criteria and sub-criteria and then modified VIKOR is developed in order to calculate the vendor rating. In doing so, a modified VIKOR method with fuzzy-random data is extended due to the existence of both qualitative and quantitative criteria. The qualitative criteria are considered by fuzzy linguistic modeling and quantitative criteria from random data are formulated in a stochastic environment (based on historical data of suppliers). In the second step, a nonlinear programming model is developed to to determine the purchasing quantities from suppliers with multi-sourcing strategy. Finally, using a numerical study, the deployment of the above model is done in the clothing industry and crucial parameters are discovered by sensitivity analysis. Our findings indicate the critical role of customer’s demand and assigned capacity of suppliers in procurement plan.
production and operations management
Hadi Mokhtari; Hossein Shirani
Abstract
Today, in order to maximize the productivity, sales and profits of factories, variousfactors must be considered. One of these factors is energy saving, which leads tosuccess in any businesses. Another important factor is rework in the productionprocess, which reduces waste and optimal use of resources. ...
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Today, in order to maximize the productivity, sales and profits of factories, variousfactors must be considered. One of these factors is energy saving, which leads tosuccess in any businesses. Another important factor is rework in the productionprocess, which reduces waste and optimal use of resources. In this research, a linearmathematical programming model has been developed for a multi-stage productionsystem considering energy consumption and the possibility of rework. The objectivefunction of the model is calculated from a combination of energy costs and rawmaterial costs, and the proposed model has three categories of balance constraints,demand constraints and time constraints. The balance constraints, the task ofcalculating the number of raw materials required and the amount of input materialsto each part of the production stage, the demand constraints are the task ofcalculating the number of final products, and the inventory and time constraints arealso the task of calculating the time available to the production of each product.A hypothetical production system is flow shop. To understand the proposed modelbetter, a logical example is designed and solved and analyzed using GAMS software. In the current situation , energy consumption is one of the concerns of policy makers in the fields of production and industry , and therefore this research with the proposed model , helps decision makers in manufacturing industries to ensure optimal energy consumption , optimal decisions in adopt multi -stage rework and production condition
Mohammad Nikzamir; vahid baradaran; Yunes Panahi
Abstract
Health care solid wastes include all types of waste that are produced as a result ofmedical and therapeutic activities in hospitals and health centers. About 15% to20% of these waste materials are infectious waste, which falls within the categoryof hazardous materials. Infectious waste is the one that ...
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Health care solid wastes include all types of waste that are produced as a result ofmedical and therapeutic activities in hospitals and health centers. About 15% to20% of these waste materials are infectious waste, which falls within the categoryof hazardous materials. Infectious waste is the one that must be treated beforedisposal or recycling. Hence, this paper seeks to develop a bi-objective mixedinteger programming model for the infectious waste management. In the proposedmodel, in addition to minimizing the chain costs, the reduction of risks for thepopulation exposed to the spread of contamination resulting from infectious wasteis also considered. For this purpose, a multi-echelon chain is proposed by takinginto account the green location-routing problem, which involves the location ofrecycling, disposal, and treatment centers through various treatment technologiesand routing of vehicles between treatment levels and the hospital. The routingproblem has been considered to be multi-depot wherein the criterion of reducingthe cost of fuel consumption of heterogeneous cars is used for green routing.Finally, a hybrid meta-heuristic algorithm based on ICA and GA is developedand, following its validation, its function in solving large-scale problems has beeninvestigated. Results show that the proposed algorithm is effective and efficient.
