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
modeling and simulation
Fereshteh Koushki
Abstract
It is inevitable for a manager to consider the performance effects of each component of a multi-stage financial equity capital. These components serve as inputs in the first stage to raise investments. The investments, as outputs of the first stage, become inputs for the second stage and are used in ...
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It is inevitable for a manager to consider the performance effects of each component of a multi-stage financial equity capital. These components serve as inputs in the first stage to raise investments. The investments, as outputs of the first stage, become inputs for the second stage and are used in bank services, such as bank facilities, which are outputs of the second stage. Therefore, when evaluating bank performance, the connectivity between the stages must be considered; otherwise, efficiency may not be calculated correctly. Traditional methods often assess multi-stage systems as black boxes, neglecting the potential connectivity that may exist among the stages. We delve into the system and propose models to improve overall efficiency and the efficiency of each stage. Additionally, the continuity and relationships among stages introduce numerous variables and constraints to linear programming for evaluating the entire system. A centralized approach calculates the efficiency score of units simultaneously by solving only one linear programming problem, significantly reducing computational complexity. This approach, especially in large organizations, is commonly employed by central managers. In this paper, we introduce a centralized method for evaluating units with a multi-stage structure. We apply the proposed models to evaluate the efficiencies of bank branches and insurance companies, demonstrating the superiority of the improved network approach and centralized method in enhancing overall system efficiency. Bank branches typically have a two-stage structure, involving labor, physical capital, and other factors.IntroductionBank branches operate under the supervision of a central management team. The central manager, acting as the decision-maker, allocates resources such as labor and financial equity capital as inputs for these branches. The goal is to optimize the overall efficiency of the branches by minimizing the total consumption of resources while maximizing the desired outputs, such as security investments. A common approach to enhancing the performance of banks involves evaluating each branch separately. However, this method does not guarantee the minimization of total resource consumption and can be time-consuming. Since all bank branches are under the control of central management, the decision-maker can optimize the efficiency scores of branches by allocating resources to them simultaneously. This approach, known as centralized Data Envelopment Analysis (DEA), is particularly relevant when certain variables are controlled by a central authority, such as a Head Office, rather than individual unit managers. DEA is a mathematical programming technique used to assess the performance of homogeneous Decision Making Units (DMUs). However, in cases where DMUs have a network structure, such as banks, where the outputs of one division or sub-process serve as inputs for the next sub-process, traditional DEA models treat two-stage DMUs as black boxes and overlook potential connectivity among the stages. In our approach, we consider the internal activities within the system and propose a non-radial model to optimize multi-stage DMUs by taking into account the connectivity among the stages. Furthermore, in previous network DEA models, constraints related to intermediate activities were treated as inequalities, which, as we will demonstrate in this paper, can lead to contradictions in optimality. We address this issue by carefully considering the connectivity among stages. The presence of connectivity among stages introduces numerous variables and constraints to the corresponding model. This model, when used to measure the overall efficiency scores of all DMUs, would traditionally require solving as many problems as there are DMUs, which can be highly time-consuming. In our paper, we introduce a centralized approach that measures the efficiency scores of multi-stage structure DMUs by solving only one linear programming problem. We have applied these proposed models to evaluate bank branches and insurance companies. This approach provides a more comprehensive and efficient way to assess and improve the performance of multi-stage organizations like banks, taking into account the interconnected nature of their operations.MethodologyWe employ the Data Envelopment Analysis approach to evaluate systems with a multi-stage structure, often referred to as a network structure. Traditional DEA models treat two-stage DMUs as black boxes and overlook the potential for connectivity among these stages. In contrast, we delve into the internal activities of the system and propose a model that optimizes multi-stage DMUs by considering the interconnections among the stages. Moreover, in previous models designed to assess network systems, constraints related to intermediate activities were typically treated as inequalities, which could lead to inconsistencies in optimization. In our approach, we enhance these constraints associated with intermediate activities to ensure more robust optimization. Additionally, we apply a centralized approach to allocate resources to DMUs, allowing for the simultaneous optimization of the efficiency scores of all DMUs through the solution of a single linear programming problem. This centralized method streamlines resource allocation and improves the overall efficiency of the DMUs.ResultsWe evaluated 20 bank branches, treating them as 20 DMUs with a two-stage structure. In the first stage, inputs included paid interest, personnel costs, paid interest related to foreign currency transactions, and personnel costs related to foreign currency transactions. The first stage produced intermediate outputs in the form of raised funds and raised funds related to foreign currency transactions. In the second stage, the outputs consisted of loans and common incomes. Notably, some loans in the second stage might become non-performing, where borrowers are unable to make full or even partial repayments. To address this, we considered non-performing loans as undesirable or bad outputs and transformed them into inverse values to treat them as good outputs. To calculate the efficiency scores of the bank branches, we employed both our improved network model and the traditional DEA approach. Our network-based method revealed that many of the bank branches under evaluation were inefficient, in contrast to the traditional method, which inaccurately identified many of the bank branches as efficient. Subsequently, we extended our network method to a centralized case, significantly reducing computation time. The network-based assessment of bank branches took nearly 5 seconds, whereas solving the centralized model required only 0.1 second. In addition to evaluating bank branches, we applied our methods to assess insurance companies. The results demonstrated that our model provided more accurate efficiency scores compared to previous network-based approaches.ConclusionIn multi-stage production systems, the production process comprises several stages. Banks, for example, operate with a network structure in which labor, physical capital, and financial equity capital serve as inputs in the first stage to generate deposits as intermediate outputs. In the second stage, these banks utilize the deposits obtained from the first stage to create loans and security investments. We have introduced models to assess the efficiency of each stage, whether it's the first, intermediate, or final stage, individually. Additionally, we have developed a non-radial SBM model designed for evaluating DMUs with multi-stage structures. The Centralized DEA approach is a valuable method for central managers, particularly in large organizations like bank branches, to allocate resources effectively. We have extended our network-based method to a centralized approach, allowing us to calculate efficiency scores by solving just one linear programming problem. The results obtained from applying our proposed models to evaluate bank branches and insurance companies, both exhibiting network structures as DMUs, demonstrate the superiority of the network centralized approach over previous models.
Mohammadreza Dabiri; Mehdi Yazdani; bahman naderi; Hasan Haleh
Abstract
In the real world, firms with hybrid flow-shop manufacturing environment generally facethe human resource constraint, salary cost increasment and efforts to make better use oflabor, in addition to machine constraint. Given the limitations of these resources, productdelivery requierements to customers ...
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In the real world, firms with hybrid flow-shop manufacturing environment generally facethe human resource constraint, salary cost increasment and efforts to make better use oflabor, in addition to machine constraint. Given the limitations of these resources, productdelivery requierements to customers have made the job rejection essential in order to meetdistinct customer requirements. Therefore, this research has studied the dual resourceconstrained hybrid flow-shop scheduling problem with job rejection in order to minimizethe total net cost (the sum of the total rejection cost and the total tardiness cost of jobs)which is widely used in many industries. In this article, a mixed integer linear programmingmodel has developed for the research problem. In addition, an improved sooty ternoptimization algorithm (ISTOA) has proposed to solve the large-sized problems as well asa decoding method due to the NP-hardness of the problem. In order to evaluate theproposed optimization algorithm, five well-known algorithms in the literature including(immunoglobulin-based artificial immune system (IAIS), genetic algorithm (GA), discreteartificial bee colony (DABC), improved fruit fly optimization (IFFO), effective modifiedmigrating birds optimization (EMBO)) have adapted with the proposed problem. Finally,the performance of the proposed optimization algorithm has investigated against theadapted algorithms. Results and evaluations show the good performance of the improvedsooty tern optimization algorithm.
Behnam Vahdani
Abstract
Today, intense competition in global markets has forced companies to design and manage of supply chains in a better way. Since the role of three factors: location, routing and inventory is important to continue the life of a supply chain so, integration of these three elements will create an efficient ...
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Today, intense competition in global markets has forced companies to design and manage of supply chains in a better way. Since the role of three factors: location, routing and inventory is important to continue the life of a supply chain so, integration of these three elements will create an efficient and effective supply chain. In this study, we investigate the problem of supply chain network design that including routing and inventory problem consist of flow allocation, vehicle routing between facilities, locating distribution centers and also consider the maximum coverage for respond to customer demand. Proposed mathematical model is a nonlinear mixed integer programming model for location-routing-inventory problem in the four-echelon supply chain by considering the multiple conflicting goals of total cost, travel time and maximum coverage. In order to solve the proposed model, three meta-heuristic algorithms (MOPSO, MSGA_II and NRGA) has been used. The accuracy of mathematical model and proposed algorithms are evaluated through numerical examples
modeling and simulation
mahboobeh golestanizadeh; Akbar Eetebarian; amirreza naghsh; reza ebrahim zadeh
Abstract
This study aims to design a model for measuring the level of maturity of business intelligence in electronic businesses, specifically for Internet Service Provider (ISP) companies. The study adopts a qualitative approach based on the phenomenological approach. A total of 10 specialists, experts, and ...
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This study aims to design a model for measuring the level of maturity of business intelligence in electronic businesses, specifically for Internet Service Provider (ISP) companies. The study adopts a qualitative approach based on the phenomenological approach. A total of 10 specialists, experts, and managers from electronic businesses involved in providing Internet services are selected as participants using the maximal differentiation method. Data are collected through in-depth and semi-structured interviews and analyzed using Colaizzi's method. The findings are classified into five levels of business intelligence maturity (Level 1: Primary maturity, Level 2: Repeatable maturity, Level 3: Defined maturity, Level 4: Managed maturity, Level 5: Optimized maturity) using the Delphi technique. Subsequently, a model consisting of 33 dimensions and 232 indicators is designed based on the relevant literature and the researcher's viewpoint, with confirmation from experts. Finally, the model is validated using confirmatory factor analysis in Smart PLS software.IntroductionDue to the fact that businesses face numerous challenges, such as the need for increased responsiveness and transparency towards customers, the growing number of tasks and organizational activities, and rapid technological changes, they require mechanisms capable of real-time data analysis and integration. Business intelligence serves as one of these mechanisms. Additionally, businesses need to assess and evaluate their current performance, compare their existing processes, tools, and methods with the best practices, and measure indicators of predictability, control, and effectiveness to effectively implement business intelligence. Therefore, they require a model to gauge the maturity level of business intelligence within their organization. Consequently, the objective of this research is to present a model for measuring the maturity level of business intelligence in electronic businesses.Materials and MethodsSince the researcher aims to extract the components of business intelligence maturity based on people's mentalities and experiences, the phenomenological method, specifically Colaizzi's method, was employed. To achieve this, 10 experts from Internet service provider companies were interviewed and selected using the maximal differentiation sampling method. The analysis of these interviews resulted in the extraction of 277 significant codes. Given the research's focus on measuring the maturity level of business intelligence, 40 experts were then asked to classify the obtained concepts into five levels of business intelligence maturity (Level 1: Primary maturity, Level 2: Repeatable maturity, Level 3: Defined maturity, Level 4: Managed maturity, Level 5: Optimized maturity) using the Delphi technique and snowball sampling method. After three rounds of Delphi, 232 codes remained out of the initial total of 277 codes. These 232 indicators were then categorized into 33 dimensions based on the definitions, functions of business intelligence, and the perceived concepts of each indicator. Subsequently, the researcher designed a measurement model for the maturity level of business intelligence in electronic businesses specifically tailored for Internet service providers. Finally, the designed model was validated through confirmatory factor analysis using SmartPLS software.Discussion and ResultsThis research has developed a model that enables companies, especially Internet service providers, to assess their current business state and their progress towards their goals. The model facilitates the decision-making process for e-business managers. With 5 levels, 33 dimensions, and 232 indicators encompassing technical, managerial, and human aspects, the model effectively enhances business capabilities and establishes a foundation for improving and advancing the level of maturity within the business. It is important to note that the model's Level 1 (Primary maturity) includes one dimension titled "reporting" with five indicators. Level 2 (Repeatable maturity) comprises five dimensions: advertising (eight indicators), management and performance evaluation (seven indicators), control (three indicators), documentation (five indicators), and automation (two indicators). Level 3 (Defined maturity) consists of six dimensions: access level (four indicators), customer orientation (16 indicators), process management (eight indicators), standardization of processes (10 indicators), improvement of information quality (five indicators), and improvement of service level (28 indicators). Level 4 (Managed maturity) encompasses 13 dimensions: assessment and analysis skills (14 indicators), business development and organizational processes (nine indicators), organizational management (12 indicators), organizational training (nine indicators), human resource management (16 indicators), organizational value (five indicators), security (two indicators), support (five indicators), business strategies (three indicators), management and development of essentials (11 indicators), business performance management (five indicators), policy making (four indicators), and cost-benefit (two indicators). Lastly, Level 5 (Optimized maturity) includes eight dimensions: predictive analysis (six indicators), dashboard (two indicators), knowledge management (six indicators), innovation (four indicators), competitive advantage (six indicators), technology development (four indicators), expansion of investment (three indicators), and data mining (three indicators).ConclusionsThis research has designed a model to facilitate the decision-making process of e-business managers, particularly those in Internet service providers. The model enables companies to assess their current business state and their progress towards their goals. The model encompasses 5 levels, 33 dimensions, and 232 different indicators, taking into account technical, managerial, and human aspects. With this comprehensive approach, the model has the potential to enhance business capabilities and establish a solid groundwork for improving and advancing the maturity level of the business. Internet service provider companies not only gain an understanding of their business intelligence maturity level and have the opportunity to elevate it through long-term planning, but they also empower themselves to navigate future changes and meet evolving customer expectations. The business intelligence maturity model introduced in this study serves as a framework for continuous improvement in their business activities. It provides a foundation and context for controlling processes and facilitates the ongoing enhancement of their operations.This study aims to design a model for measuring the level of maturity of business intelligence in electronic businesses, specifically for Internet Service Provider (ISP) companies. The study adopts a qualitative approach based on the phenomenological approach. A total of 10 specialists, experts, and managers from electronic businesses involved in providing Internet services are selected as participants using the maximal differentiation method. Data are collected through in-depth and semi-structured interviews and analyzed using Colaizzi's method. The findings are classified into five levels of business intelligence maturity (Level 1: Primary maturity, Level 2: Repeatable maturity, Level 3: Defined maturity, Level 4: Managed maturity, Level 5: Optimized maturity) using the Delphi technique. Subsequently, a model consisting of 33 dimensions and 232 indicators is designed based on the relevant literature and the researcher's viewpoint, with confirmation from experts. Finally, the model is validated using confirmatory factor analysis in Smart PLS software.IntroductionDue to the fact that businesses face numerous challenges, such as the need for increased responsiveness and transparency towards customers, the growing number of tasks and organizational activities, and rapid technological changes, they require mechanisms capable of real-time data analysis and integration. Business intelligence serves as one of these mechanisms. Additionally, businesses need to assess and evaluate their current performance, compare their existing processes, tools, and methods with the best practices, and measure indicators of predictability, control, and effectiveness to effectively implement business intelligence. Therefore, they require a model to gauge the maturity level of business intelligence within their organization. Consequently, the objective of this research is to present a model for measuring the maturity level of business intelligence in electronic businesses.Materials and MethodsSince the researcher aims to extract the components of business intelligence maturity based on people's mentalities and experiences, the phenomenological method, specifically Colaizzi's method, was employed. To achieve this, 10 experts from Internet service provider companies were interviewed and selected using the maximal differentiation sampling method. The analysis of these interviews resulted in the extraction of 277 significant codes. Given the research's focus on measuring the maturity level of business intelligence, 40 experts were then asked to classify the obtained concepts into five levels of business intelligence maturity (Level 1: Primary maturity, Level 2: Repeatable maturity, Level 3: Defined maturity, Level 4: Managed maturity, Level 5: Optimized maturity) using the Delphi technique and snowball sampling method. After three rounds of Delphi, 232 codes remained out of the initial total of 277 codes. These 232 indicators were then categorized into 33 dimensions based on the definitions, functions of business intelligence, and the perceived concepts of each indicator. Subsequently, the researcher designed a measurement model for the maturity level of business intelligence in electronic businesses specifically tailored for Internet service providers. Finally, the designed model was validated through confirmatory factor analysis using SmartPLS software.Discussion and ResultsThis research has developed a model that enables companies, especially Internet service providers, to assess their current business state and their progress towards their goals. The model facilitates the decision-making process for e-business managers. With 5 levels, 33 dimensions, and 232 indicators encompassing technical, managerial, and human aspects, the model effectively enhances business capabilities and establishes a foundation for improving and advancing the level of maturity within the business. It is important to note that the model's Level 1 (Primary maturity) includes one dimension titled "reporting" with five indicators. Level 2 (Repeatable maturity) comprises five dimensions: advertising (eight indicators), management and performance evaluation (seven indicators), control (three indicators), documentation (five indicators), and automation (two indicators). Level 3 (Defined maturity) consists of six dimensions: access level (four indicators), customer orientation (16 indicators), process management (eight indicators), standardization of processes (10 indicators), improvement of information quality (five indicators), and improvement of service level (28 indicators). Level 4 (Managed maturity) encompasses 13 dimensions: assessment and analysis skills (14 indicators), business development and organizational processes (nine indicators), organizational management (12 indicators), organizational training (nine indicators), human resource management (16 indicators), organizational value (five indicators), security (two indicators), support (five indicators), business strategies (three indicators), management and development of essentials (11 indicators), business performance management (five indicators), policy making (four indicators), and cost-benefit (two indicators). Lastly, Level 5 (Optimized maturity) includes eight dimensions: predictive analysis (six indicators), dashboard (two indicators), knowledge management (six indicators), innovation (four indicators), competitive advantage (six indicators), technology development (four indicators), expansion of investment (three indicators), and data mining (three indicators).ConclusionsThis research has designed a model to facilitate the decision-making process of e-business managers, particularly those in Internet service providers. The model enables companies to assess their current business state and their progress towards their goals. The model encompasses 5 levels, 33 dimensions, and 232 different indicators, taking into account technical, managerial, and human aspects. With this comprehensive approach, the model has the potential to enhance business capabilities and establish a solid groundwork for improving and advancing the maturity level of the business. Internet service provider companies not only gain an understanding of their business intelligence maturity level and have the opportunity to elevate it through long-term planning, but they also empower themselves to navigate future changes and meet evolving customer expectations. The business intelligence maturity model introduced in this study serves as a framework for continuous improvement in their business activities. It provides a foundation and context for controlling processes and facilitates the ongoing enhancement of their operations.
Soroush Avakh Darestani; Fatemeh Fazel
Abstract
Training and development are considered as the facilitator of Green Human Resource Management. Green training related to environmental issues and enables all employees to the organization's performance with environmental issues are integrated. The aim of this research was about investigating the relationship ...
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Training and development are considered as the facilitator of Green Human Resource Management. Green training related to environmental issues and enables all employees to the organization's performance with environmental issues are integrated. The aim of this research was about investigating the relationship between green training and green supply chain management and finally its effect on organizational performance. In this context, a developed conceptual model was designed. The statistical population included all experts and managers of the National Iranian Oil Products Distribution Company of Guilan’s region. The innovation of the present research is to measure the impact of green human resources management and supply chain management in integrating these two processes into the organization's performance. The results of the hypothesis with 95% confidence interval using structural equation modeling using LISREL software show that green education has significant impact on organizational performance through mediating role of green supply chain management and green education can be a source of competitive advantage for companies. Green supply chain management also improves the performance of the organization in terms of efficiency, effectiveness, and environmental distinction in achieving a sustainable competitive advantage
Meisam Jafari Eskandari; Hani Emami-Solot
Abstract
In this research, a model for a sustainable closed-loop supply chain with economic, social and environmental considerations, along with the risk arising from uncertainty in parameters, is presented. Stochastic programming has been used for modeling this problem and also using the scale of value Exposure ...
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In this research, a model for a sustainable closed-loop supply chain with economic, social and environmental considerations, along with the risk arising from uncertainty in parameters, is presented. Stochastic programming has been used for modeling this problem and also using the scale of value Exposure to conditional risk is measured by risk. The aim of this model is to maximize network design benefits, reduce unemployment and increase job opportunities resulting from the construction of facilities and minimize the production of carbon produced through intranets, production centers, recycling, repair, re-production. Other goals include minimizing the risk posed by uncertainty in transportation costs and customer demand. In the end, in order to demonstrate the efficiency of the model, an example is solved with certainty and uncertainty with the risk measurement criterion, and the pareto optimal solutions are compared. Results show that, with increasing risk, the profit from the supply chain network has decreased and should be costlier to face the risk.IntroductionToday, the necessity and importance of corporate responsibility and the social impact of companies have led managers and planners to give special attention to these aspects in their organization's missions, visions, and strategies. Corporate social responsibility encompasses the influence of a company's activities on various social groups, including employee rights, workplace safety, favorable working conditions, and job creation, among others. Furthermore, the significance of environmental standards and organizations' efforts to reduce pollution and promote efficient waste management and recycling practices have become crucial for organizational success, considering legal requirements and customer expectations. In recent years, the integration of reverse logistics, social responsibility, and environmental objectives in supply chain management has gained increasing attention due to factors such as resource reduction, pollution mitigation, environmental pressures, customer demands, and transportation costs in a competitive market. This integration, known as the closed-loop supply chain network, aims to ensure sustainability. Additionally, risk management within the supply chain has become a vital concern for supply chain management, considering the uncertainties prevailing in the global economy and trends such as increased outsourcing and advancements in information technology. The growing interest in achieving sustainability as an effective strategy for addressing challenges in the global supply chain has led to extensive research in the field of sustainable closed-loop supply chain management. However, previous studies in this area have lacked a comprehensive measure for assessing risk. Therefore, it is essential to address this issue, which involves considering stability goals in a closed-loop supply chain alongside risk management in uncertain conditions. The necessity for such research is evident, given the complexity of global supply chains and the increased vulnerability and risk exposure faced by organizations.Materials and MethodsGiven the existing gaps in the literature and the presence of uncertainty in real-world data, a mathematical model was proposed to help decision-makers reduce risk by considering identified risks and utilizing a comprehensive and effective risk measurement scale. In the designed model and forward network, suppliers are responsible for procuring raw materials. The manufactured products are then delivered to the market's customers through distributor networks. In the reverse flow of products, returned items are categorized into two groups: separable and non-separable products, after collection and inspection. Products that can be disassembled are sent to separation centers where they are transformed into components. The components are further divided into recoverable and non-recoverable categories. Non-recoverable components are transferred to disposal centers for safe disposal, while recoverable components are sent to inspection, cleaning, and sorting centers. After inspection and cleaning, the products are classified into repairable, remanufacturable, and recyclable groups. In the remanufacturing process, reusable components, after inspection, cleaning, and sorting, are sent to factories based on the production center's capacity. They are then combined with other parts to create new products that reenter the distribution cycle. In the recycling process, separated recyclable components are transported to recycling centers for direct production of raw materials, based on the capacity of the recycling centers, after collection and inspection.Discussion and ResultsModel 1 represents the initial approach, where scenario analysis for future conditions is not utilized, and the average values of uncertain parameters are taken into account. On the other hand, Model 2 incorporates various scenarios of future conditions. It is a linear model that considers possible future conditions as well. Model 1 exhibits lower costs compared to Model 2. The predictability of this problem arises from the fact that the risk associated with future market conditions was largely disregarded in Model 1. However, in Model 2, the consideration of introduced triple conditions for possible future outcomes necessitates a higher cost. Nevertheless, this higher cost brings us closer to real-world approximation and facilitates better decision-making in supply chain management when confronted with risks.ConclusionIn this article, we conducted a literature review on the topic of risk models in supply chains and identified existing gaps. We found that most of the work in this field has certain weaknesses. Firstly, the focus has primarily been on risks in conventional and single-objective supply chains, neglecting the consideration of new risks and uncertainties that may arise in sustainable supply chains. To address this, we proposed a model for risk management in sustainable closed-loop supply chains. Secondly, we noticed that most of the existing studies lack a suitable and effective scale for measuring risk, particularly in the design of sustainable closed-loop supply chains. Drawing from the financial literature, we introduced the CVaR scale to fill this gap. Lastly, we developed and analyzed a model based on research gaps, using a case study in the home appliance industry as an example. The examination of the model's results, along with comparisons to real-world outcomes and previous research, validates the credibility of the proposed model.
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
Mahsa Pishdar; Atefeh Habibi
Abstract
To explain a framework for managing the risk of transportation in the food industry supply chain, the initial step involves identifying 12 risks that can potentially lead to transportation disruptions, based on the research background. Utilizing the Delphi method and gathering opinions from 15 academic ...
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To explain a framework for managing the risk of transportation in the food industry supply chain, the initial step involves identifying 12 risks that can potentially lead to transportation disruptions, based on the research background. Utilizing the Delphi method and gathering opinions from 15 academic and industrial experts across 3 stages, the risks were ultimately defined. Furthermore, expert opinions were sought to determine solutions to address the identified risks. The grey DEMATEL method was employed to investigate the interaction of risks. The findings revealed that weather problems, natural disasters, insufficient skilled labor/labor strikes, infrastructure capacity, and inflation and exchange rate changes are among the risks that exert a more significant influence on other risks than they are influenced by them. Subsequently, using the grey COPRAS method, the prioritization of solutions to mitigate the identified risks, based on expert opinions, was undertaken. The results indicated that the top-ranked solution is the definition of key performance indicators. Therefore, it is recommended to managers that, in order to establish a robust supply chain and proactively manage risks, they should identify stakeholders and critical processes. Afterward, an agreement on the financial flow in each situation should be obtained, and a value flow map drawn. This approach enables the implementation of preventive measures to reduce supply chain risk and facilitates the preparation of an emergency plan for unforeseen conditions, thereby enhancing resilience.IntroductionDisruption in transportation stands as the pivotal factor undermining the efficiency of the supply chain. Any significant interruption can result in delays or business flow cessation, leading to consequential impacts (Ali et al., 2021). The transportation supply network is susceptible to various technical, economic, and environmental factors (Tan et al., 2023). Simultaneously, research indicates that factors such as a workforce lacking sufficient skills, suboptimal selection of service providers, traffic accidents, and the inability to predict the systemic impact of these risks play a crucial role in transportation, causing disruptions in the flow. In addition to these factors, it is noteworthy that supply chain management in the food industry introduces its own complexities. Unlike other industries, the quality of products in this type of supply chain consistently diminishes during product movement and this issue of perishability intensifying the need for transportation risk management (Hosseini-Motlagh et al., 2019; Choe et al., 2021). Given this context, emphasis should be placed on establishing distribution channels with lower costs and implementing change management to enhance efficiency. However, existing studies have predominantly focused solely on analyzing transportation disruption within companies' supply chains. Clearly, it is insufficient to only address disorders or the risks that may lead to their occurrence. A comprehensive examination of the cause-and-effect relationships among these risks facilitates a systematic understanding of the risk network. This approach enables the development of a more effective program to enhance the resilience of the transportation system. Even in the face of risks and disruptions, this ensures minimal damage, a swift return to normal operational levels in the supply chain, or the application of knowledge management to learn from experiences and prevent the recurrence of disruptions through appropriate implementation solutions. Consequently, the overall performance of the supply chain can be improved. In consideration of these elements, this study aims to address the following main questions:- What are the risks associated with transportation in the food supply chain?- What are the pertinent solutions, according to expert opinions, and what is their prioritization?MethodologyIn terms of its objective, the current study falls within the domain of applied research as it aims to discover practical solutions to address a real-world problem. Regarding information collection, the study is categorized as survey research, wherein data is gathered based on the opinions of 15 experts. Among these experts, 9 are drawn from the industrial community, each possessing over 10 years of experience in the commercial sector and raw material procurement within the food industry. The remaining 6 experts are affiliated with the academic community and have published numerous articles in the field. The study unfolds in several stages. Initially, a compilation of transportation risks causing disruptions in the food supply chain is accomplished through a literature review. Subsequently, the Delphi method is employed to screen and refine these disruptions. The cause-and-effect relationships among the identified disruptions are then scrutinized using the Grey DEMATEL method. Experts are engaged to contribute not only by assessing the risks but also by providing their insights into coping strategies based on their experience. Finally, the coping strategies are prioritized using the Grey COPRAS method.Results and DiscussionAccording to the obtained results, it is evident that the climate problems risks of failure to choose logistics service providers that care about sustainability principles (C6) and frequent change of product delivery time (C9) exhibit the highest degree of interconnectedness with other risks. The weights of these risks have also been determined. Notably, the failure to choose logistics service providers committed to sustainability principles has secured the top rank with a weight of 0.1443. Following closely, the frequent change of product delivery time holds the second position with a weight of 0.1384, while natural disasters rank third with a weight of 0.1039. Turning to coping strategies, it is noteworthy that the solution of "Definition of Key Performance Indicators (KPI)" has claimed the top position. In today's business landscape dominated by Logistics 4.0 and Omnichannel, simplifying processes can create significant added value for any business, particularly in minimizing transfer time. Concurrently, many manufacturing companies are leveraging various logistics transportation modes as a critical factor for promptly responding to demands, thereby enhancing service reliability and minimizing travel time (Foroozesh et al., 2022). The adoption of modern technologies such as the Internet of Things facilitates real-time inventory monitoring, contributing to dynamic pricing policies. As product quality diminishes along the chain, electronic labels enable adjusting product prices based on features (Kumar & Agrawal, 2023).ConclusionStudies indicate that supply chain managers, particularly in food supply chains, have demonstrated significant commitments to sustainability goals, leading to the pursuit of a diverse array of performance improvement projects. This study identifies various risks and corresponding coping strategies. Outsourcing logistics activities to 3PL allows leveraging their expertise in supply chain management, thereby enhancing stability and efficiency. This approach can contribute to reducing the carbon footprint, increasing order fulfillment, and lowering energy consumption throughout the supply chain.For future research endeavors, it is recommended to prioritize strategies related to realizing the circular economy within the logistics system of the food industry. Providing a roadmap for the sustainable development of logistics clusters can enhance supply chain performance, minimize waste, and boost the social credibility of the supply chain. Additionally, attention to the concept of greenwashing in sustainable logistics, particularly concerning the fulfillment of social responsibility, can prove beneficial in improving overall supply chain performance
Ali Saeedi; Javad Shabzendedar
Volume 8, Issue 21 , June 2011, , Pages 143-165
Abstract
Price bubble is a phenomenon in which the assets prices go up considerably. The research shows that bubbles have Non-Linea characteristic, and common methods of stock valuation such a Discounted cash flow models and relative models, usually are unable to evaluate stock values. Common and Non-Systemic ...
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Price bubble is a phenomenon in which the assets prices go up considerably. The research shows that bubbles have Non-Linea characteristic, and common methods of stock valuation such a Discounted cash flow models and relative models, usually are unable to evaluate stock values. Common and Non-Systemic approach ha linear and cascading perspective for considering phenomenon bi systemic approach has Non-Linear approach. In this research, stock prices bubble in auto industry is considered by systemic thinking and is modeled by system dynamics approach. Two influencing factors are tested in the paper: belief changes speed and block purchasing. Bot factors make the stock more volatile; so, the higher the speed and the larger the amount, the greater the stock volatility. Also, bloc transaction causes sentimental atmosphere among investors and pushes retail investors.
Saviz Mohammadnabi; Sina Mohammadnabi
Volume 9, Issue 24 , March 2012, , Pages 161-182
Abstract
This study attempted to use data mining as a powerful analytical tool to find patterns for occurrence of accidents from 1845 recorded events in safety data warehouse in one of the largest project-based organizations active in construction industry in Iran between the years 2002 and 2008. High-risk nature ...
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This study attempted to use data mining as a powerful analytical tool to find patterns for occurrence of accidents from 1845 recorded events in safety data warehouse in one of the largest project-based organizations active in construction industry in Iran between the years 2002 and 2008. High-risk nature of construction industry, Geographic expansion of the projects sites and large number of accidents are the characteristics of this organization. Predicting and preventing models for occurrence of accidents have been proposed in this study by extracting 31 traceable Association rules from recorded events. Extracting the rules, the minimum amount of confidence, support and lift indicators have been set respectively in 73%, 5% and 1 levels.
Payam Hanafizadeh; Abolfazl Jafari
Volume 8, Issue 19 , December 2010, , Pages 165-187
Abstract
In this paper, a hybrid model of artificial neural networks is designed and used to evaluate the prediction ability of this hybrid model with individual Back Propagation feed forward. This study employs hybrid artificial neural networks consisting of Back Propagation and Kohonen Self Organizing Map (SOM) ...
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In this paper, a hybrid model of artificial neural networks is designed and used to evaluate the prediction ability of this hybrid model with individual Back Propagation feed forward. This study employs hybrid artificial neural networks consisting of Back Propagation and Kohonen Self Organizing Map (SOM) for better stock price prediction. Computational experience in predicting stock prices obtained from Tehran Stock Exchange reveals that the combination of Self Organizing Map and Back Propagation leads to better performance in comparison with the most popular individual Back Propagation feed forward networks.
JEL Classification: E37, C45, C51, C52, C53
Alireza Mamaghani; Soheyl Sarmad Saeedi; Mohammad Reza Kabaranzade Ghadim
Volume 8, Issue 20 , March 2011, , Pages 167-194
Abstract
New Product Development (NPD) and its significance for companies, as a new approach, have some risks, but the identification of key factors of this 'approach can be helpful in decreasing risks in managers’ decision-making. So in this research, it is tried through library studies and consulting ...
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New Product Development (NPD) and its significance for companies, as a new approach, have some risks, but the identification of key factors of this 'approach can be helpful in decreasing risks in managers’ decision-making. So in this research, it is tried through library studies and consulting with professors, managers and experts in departments related to product development in SAIPA Automobile Group to identify the key factors and their indicators as much as possible. So through pre-test from 12 experts, 4 key factors of technology, marketing, commercializing, and product development team management were determined and the importance of indicators were identified and 5 important indicators were chosen for each factor and 21 reliable questionnaires were gathered from 23 managers and experts in related departments (product development strategy, technical and providing-parts and marketing departments) through Analytical Hierarchical Process test approach. After compatibility ratio test, ideas were put together and the importance of factors and indicators was determined through pair comparison. Therefore respectively, marketing factor with index of Special profits in product, product development team with index of work incentives, in factor of technology consistent with new products and commercial key factor with flexibility and attention to customer needs are the most important factors in the NPD process. So, knowing and determining the priority of these factors in gathering and implementing product development strategy accelerate and facilitate the success procedure and decrease the decision-making risk.
Asghar Moshabaki; Mohammad Bashokouh; Vahideh Alipoor
Volume 5, Issue 14 , December 2006, , Pages 177-200
Abstract
The objective of this study was to examine the relationship of spiritual orientation of purported servant leaders to the intensity of perceived servant behavior in those leaders. Spirituality in the workplace and more holistic styles of leadership has grown in popularity ...
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The objective of this study was to examine the relationship of spiritual orientation of purported servant leaders to the intensity of perceived servant behavior in those leaders. Spirituality in the workplace and more holistic styles of leadership has grown in popularity among leadership consultants and the general public.
A sample of 80 managers and ] 80 employees from Iran Khodroo and Saipa and pars Khodroo companies was surveyed using the Spirituality Assessment Scale (SAS) and the Servant Organizational Leadership Assessment (SOLA) between November 2007 and March 2008.
Seventy - five managers (75%) and 140 employees (77%) responded. Spiritual orientation was measured using the SAS through two dimensions. The definitive dimension, considered essential for an individual to be spiritual, is demonstrated through a relationship with the transcendent through prayer or meditation, and the con-elated dimension, with is not exclusive to spirituality but may derive from moral philosophies, is the virtues of honesty, humility, and service to others. Servant leadership behavior was measured using the leadership portion of SOLA.
Results of the study indicate that the sample of leaders surveyed in this study may be more spiritual than less spiritual, but with a greater propensity for the correlated variables. Pearson con-elation indicates that spirituality leadership isn't relationship to self - perceived servant leadership behavior. The self - perceived manger scores for servant leader behavior were greater and statically different than the values assigned by the employees. The self - perceived servant leadership behavior mean score for managers was statically greater than the employees indicating a perceptual chasm between the two groups.
Jamshid Salehi Sadaghiani
Volume 6, Issue 17 , September 2007, , Pages 183-200
Abstract
In optimizing an investment portfolio, the aim is to determine optimal value of per security, where prepares the minimum risk and the maximum return. One of the methods to measure risk of portfolio is Value at Risk (VaR). In the VaR method, risk of securities portfolio is estimated for a time horizon ...
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In optimizing an investment portfolio, the aim is to determine optimal value of per security, where prepares the minimum risk and the maximum return. One of the methods to measure risk of portfolio is Value at Risk (VaR). In the VaR method, risk of securities portfolio is estimated for a time horizon in future. In recent years, volatilities of exchange rate have been led to bankruptcy of many important industries of Iran. Hence in this paper, the investment risk in an exchange portfolio concluding five main exchange in the trades market of Iran is measured by using the VaR method and will be determined the weight optimal value of per security in exchange portfolio through to minimize the risk of investment portfolio.
Mohammad Reza Taghva; Seyed Mojtaba Hosseini Bamakan
Volume 9, Issue 23 , December 2011, , Pages 187-207
Abstract
During the past two decades, perspective of competition in the banking industry has been changed significantly. This is due to forces such as new laws, globalization, and grows of technology and becoming bank's services to products, and significant grows in customer's demands. Therefore banks try to ...
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During the past two decades, perspective of competition in the banking industry has been changed significantly. This is due to forces such as new laws, globalization, and grows of technology and becoming bank's services to products, and significant grows in customer's demands. Therefore banks try to offer appropriate service to potential customers, and new services offer with better analysis so that with customer behavior analysis, we can predict risk of new investment and obtain appropriate segmentation of customer demand for these services. With such as analysis, company can prevent additional cost of marketing and also increase acceptance of services. Whereas decision making environment made of different factors, we need models so we can analysis more variables and also enable capability of information provision for decision making. In this paper, we present a model for analyzing the behavior of customers with self-organizing map, and discovering association rule among bank's services.
Akbar Pourfaraj; Mehrdad Karami; Zahra Nekooee; Zahra Taleb-beydokhty
Volume 9, Issue 25 , July 2012, , Pages 189-214
Abstract
The significant role of investment in the process of societies’economic growth and development is highlighted in most theories oneconomic growth and development. Taking into account the shortageof investment sources and the necessity of optimal allocation of thesesources for promoting economic ...
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The significant role of investment in the process of societies’economic growth and development is highlighted in most theories oneconomic growth and development. Taking into account the shortageof investment sources and the necessity of optimal allocation of thesesources for promoting economic growth, it is necessary to rightlyidentify the relative advantages of a country as regards the allocationof investment sources and also direct investment sources in mostproductive and efficient sectors in order to provide for efficient use oflimited sources to accelerate economic growth. Thus, it is muchnecessary to use the scientific methods of economic engineering andeconomic evaluation of projects to economically and financiallyevaluate investment projects. This descriptive, analytic and appliedstudy evaluates economic indicators of investment in Tarom (locatedin Zanjan Province) as an international recreational-tourist town viadifferent criteria of economic and financial evaluation and the use ofCOMFAR (A computer software for simulating the financial andeconomic situation of investment projects). The results indicated thatconducting the project of Tarom Recreational-Tourist Town has nojustification in terms of indicators of financial and economicevaluation.
Mahdi Kazemi; Ali Akbar Niknafs; Vahid Ranjbar
Volume 9, Issue 22 , September 2011, , Pages 191-208
Abstract
Often, the nature of many real life processes, especially in management and business fields are nonlinear. Forecasting the behavior of these processes requires accurate and effective forecasting tools. Shortages of such processes are removable by artificial neural network as an important modeling tool ...
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Often, the nature of many real life processes, especially in management and business fields are nonlinear. Forecasting the behavior of these processes requires accurate and effective forecasting tools. Shortages of such processes are removable by artificial neural network as an important modeling tool in business forecasting problems. In a comparing analyze, this paper shows the excellent performance of neural network in forecasting nonlinear processes rather than other forecasting models. For this, production, import and import value (dollar) data, related to wood industry of Iran, from 1961 to 2007 are studied. First, applying this data to neural network model and nonlinear models obtained from MATLAB software, the Iran wood industry was forecasted and then based on MAPE1, yielded outcomes from both models compared. Study findings show that in all cases neural network has more successful performance than models from MATLAB.
Razieh Taghipour; Soroush Avakh Darestani
Abstract
In recent years, maintenance has been recognized as an effective and a significant improvement tool in the performance of equipments. Maintenance plays an important role in maintaining reliability, availability and quality of products, risk reduction, efficiency increase and safety, so maintenance and ...
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In recent years, maintenance has been recognized as an effective and a significant improvement tool in the performance of equipments. Maintenance plays an important role in maintaining reliability, availability and quality of products, risk reduction, efficiency increase and safety, so maintenance and its strategies have a special place in the industry. A lot of researches show the necessity of using fuzzy sets in decision making problems due to its high flexibility. The choosing maintenance strategy is also a problem with so many uncertainties so using fuzzy sets seems to be very useful. The aim of this research is to provide a suitable mathematical decision making model for assessment and selection maintenance strategies by using hierarchical fuzzy Axiomatic Design (HFAD) and hierarchical fuzzy Topsis (HFTOPSIS) in Haft Almas Co. Since many criteria such as added value, safety, and … so are vital in the maintenance strategy selection, in this article first these criteria are investigated in the literature review and expert's opinions, then for obtaining the weights of criteria the FAHP method is used and finally the strategies are ranked through the applying HFAD and HFTOPSIS. The results of FTOPSIS and FAD were compared in selecting a maintenance strategy. The results of comparisons indicate the similarity of rankings by two techniques. Above techniques show that TPM alternative has superior to other strategies.
hamidreza jafari; parivash torki
Abstract
Since the acquisition of the market share is a correlational parameter, (the sum of the total market share of the competing organizations must be 100 percent) and the maximum in the present study, a DEA-based mathematical model was proposed to specify the competition strategies considering the correlational ...
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Since the acquisition of the market share is a correlational parameter, (the sum of the total market share of the competing organizations must be 100 percent) and the maximum in the present study, a DEA-based mathematical model was proposed to specify the competition strategies considering the correlational parameters and the magnitude and dimensions of insurance organizations. To examine the efficiency and the reliability of the proposed model, a real problem was solved in the domain of insurance industry and the results were compared with those of the basic CCR model. The findings revealed that the use of the basic CCR model to solve the problem produces unrealistic results that contradict the conditions and constraints of the real world. This was the case despite the fact that the proposed model proved more efficient and the results suggested that the proposed model improved the weaknesses of the basic DEA models in the domain of insurance institutes
Ali Mohtashami; Amir Hossein Niknamfar
Abstract
Hazardous materials which are materials due to their chemical and physical properties impose significant risk to the safety of people and the environment. It's more complex routing transport of such material than normal materials. The Combination of the two subjects as the problem of locating and routing, ...
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Hazardous materials which are materials due to their chemical and physical properties impose significant risk to the safety of people and the environment. It's more complex routing transport of such material than normal materials. The Combination of the two subjects as the problem of locating and routing, it has created unified system to locating- routing problems. These problems determined optimal number and location of facilities at the same time and also set the optimal number of vehicles and their routes. The purpose of this study was design a network for the transportation of hazardous materials and includes supply levels, distribution (hub) and customers. Hence, it presented a mathematical model in order to minimizing costs and risk simultaneously. Hazardous materials sent from supplier to the hubs and deliveries to customers from there via routing by road transportation. It should be mentioned that in proposal model the hubs been locating In order to validate the model, prepared code GAMS In software and for the exact solution, sample problems with various dimensions were produced in the form of smart and random. For this purpose, was written an algorithm design in Matlab software. According to the problem was NP-Hard, presented a hybrid algorithm based on simulated annealing and genetic algorithms to solve large-scale. At the end of research the proposed algorithm were compared with the results of exact solution.
hadis drikvand; seyyed mohammad hajimolana
Abstract
Environmental concerns have spurred an interest in studying green supply chain. Nowadays, governmental and non-governmental organizations consider environmental management as a strategic requirement having numerous benefits. Therefore, they effort to increase customers' satisfactory and market share ...
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Environmental concerns have spurred an interest in studying green supply chain. Nowadays, governmental and non-governmental organizations consider environmental management as a strategic requirement having numerous benefits. Therefore, they effort to increase customers' satisfactory and market share considering external factors like environmental consequences in addition to internal factors. In this paper, a bi-objective mixed integer programming model is developed to identify the optimal location for manufacturers and disassembly sites in a green supply chain network design. This paper addresses the role of the reliability of facilities and vehicles to ensure effective stream among supply chain network, the objective functions are defined as total cost minimization, and total co2 emissions minimization. Besides, uncertainties on the network design are investigated through two-stage stochastic programming. with respect to the fact that the model is non-linear and bi-objective, at first, an approach is presented to linearize it and then the proposed bi-objective mathematical model is solved as a single-objective one by compromise programming method. The effectiveness of the proposed model is demonstrated by using of a numerical example derived from a real case.
mohammad akbari
Abstract
Shift scheduling is one of the production planning processes that develop organization and workforces by providing optimized time table. In this study we tried to present mathematical model with minimization function of human errors regarding human factor engineering. Learning, forgetting, fatigue and ...
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Shift scheduling is one of the production planning processes that develop organization and workforces by providing optimized time table. In this study we tried to present mathematical model with minimization function of human errors regarding human factor engineering. Learning, forgetting, fatigue and rest are important factors which increase or decrease human errors and is modeled here. Provided model is nonlinear integer model. To investigate model and study human factors we solved small instances with different parameters in three categories: easy tasks, medium and hard tasks. To solve model we used LINGO software. Results indicated that shift schedules are different regarding different human parameters. With increasing difficulty of tasks and decreasing learning, rest breaks were closer to start of working shift. With decreasing difficulty of tasks and increasing learning, optimized schedule close to schedule without rest breaks. Also results showed that we can use the model to optimize human reliability, and organizations can define optimized shift schedules with considering task types and human parameters.
MohammadHadi KhorashadiZadeh; Hassan Mehrmanesh; Zadollah Fathi
Abstract
Continuous changes in today’s business environment, have led institutions to recognize knowledge as their key capital. Knowledge has become a key source in every organization. This study while, considering importance of knowledge in current organizational activities, is looking for the antecedents ...
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Continuous changes in today’s business environment, have led institutions to recognize knowledge as their key capital. Knowledge has become a key source in every organization. This study while, considering importance of knowledge in current organizational activities, is looking for the antecedents as well as the consequences of knowledge sharing using the grounded theory.The present study is a developmental research in the first phase and an applied one in the second phase. In the first phase it is an exploratory research and in the second phase it is an explanatory research. Moreover, it is a qualitative type with grounded theory approach in the first phase, while a quantitative method employing the structural equations in the second phase. The statistical population is the Hamkaran System Group. A purposive snowball sampling is employed in the first phase and a random sampling in the second one. Sample size is 17 people for first phase and 253 people for second phase. Results reviewed by two professors and points were made for the purpose of either revising or modifying the theory. Results of the grounded theory led to detection of 22 key factors e.g: distribution of knowledge as a central phenomenon, intra-organizational order, synergy formation, knowledge retesting, maturity-oriented development and safe margins of knowledge distribution. Theoretical comparison, structural equations and path analysis were applied for their relations and influences
Saeed Mousakhani; Saeed Mousakhani; Mohamad Sadegh Sangari
Abstract
Integrated production-distribution planning is one of the main issues in supply chain management that plays an important role in reducing supply chain costs. In addition, choosing optimal location of distribution centers can facilitate achieving this goal. On the other hand, environmental laws and regulations ...
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Integrated production-distribution planning is one of the main issues in supply chain management that plays an important role in reducing supply chain costs. In addition, choosing optimal location of distribution centers can facilitate achieving this goal. On the other hand, environmental laws and regulations established by governments impose constraints to production and distribution activities and make necessary the adoption of green supply chain approach more than ever. In this paper, a novel model is developed for integrated location-production-distribution planning in a three-echelon green supply chain consisting of manufacturing plants, distribution centers, and customers with multiple products and multiple time periods. The objective function includes minimization of the total supply chain costs as wll as CO2 emissions throughout the chain. Also, the customer demand and service level are expressed as fuzzy Z-number in order to obtain the reliability of the values from the experts. The applicability and efficiency of the proposed model is demonstrated through a real case which, considering two indexes of customer service level and green production and distribution. In order to solve the proposed model, GAMS software package is used. Results show satisfactory performance of the proposed model in reducing costs in the green supply chain.
