M. J. Tarokh; Mahsa EsmaeiliGookeh
Abstract
The present study attempts to establish a new framework to speculate customer lifetime value by a stochastic approach. In this research the customer lifetime value is considered as combination of customer’s present and future value. At first step of our desired model, it is essential to define ...
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The present study attempts to establish a new framework to speculate customer lifetime value by a stochastic approach. In this research the customer lifetime value is considered as combination of customer’s present and future value. At first step of our desired model, it is essential to define customer groups based on their behavior similarities, and in second step a mechanism to count current value, and at the end estimate the future value of customers. Having a structure in modeling customer churn is also important to have complete customer lifetime value computation. Clustering as one of data mining techniques is practiced to help us analyze the different groups of customers, and extract mathematical model to count the customers value. Thereafter by using Markov chain model as stochastic approach, we predict future behavior of the customer and as a result, estimate future value of different customers. The proposed model is demonstrated by the customer demographic data and historical transaction data in a composite manufacturing company in Iran.
Zahra Jiryaei Sharahi; Yahia Zare Mehrjerdi; Mohammad Saleh Owlia; Masoud Abessi
Abstract
In a data-driven decision-making process, there are various types of data that should be thoroughly processed and analyzed. Data mining is a well-recognized method to obtain such information by analyzing data and transforming it into actionable insights for further use. Among the various data mining ...
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In a data-driven decision-making process, there are various types of data that should be thoroughly processed and analyzed. Data mining is a well-recognized method to obtain such information by analyzing data and transforming it into actionable insights for further use. Among the various data mining techniques such as classification, clustering, and association rules, this research focused on classification techniques and presented an innovative regression-based learning approach in the decision tree (DT) models. DT algorithms are easy-to-understood and can work with different data types including continuous, discrete, and non-numerical. Despite a large number of existing studies, which attempt to enhance the performance of the DT models, there is still a gap in accurately extracting knowledge from databases. In this research, this issue is addressed by exploiting regression and coefficient of determination (R2) methods in a DT. The proposed tree provides new insights in the following aspects: split criterion, handling continuous and discrete variables, labeling leaf node, pruning process by stopping criteria and tree evaluation. The superiority of the proposed algorithm is demonstrated using a real-world hospital database and a comparison with existing approaches is provided. The results showed that the proposed algorithm outperforms the existing methods in terms of higher accuracy and lower complexity.
M. Zandieh
Volume 3, Issue 1 , June 2016, , Pages 89-107
Abstract
This paper considers the job scheduling problem in virtual manufacturing cells (VMCs) with the goal of minimizing two objectives namely, makespan and total travelling distance. To solve this problem two algorithms are proposed: traditional non-dominated sorting genetic algorithm (NSGA-II) and knowledge-based ...
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This paper considers the job scheduling problem in virtual manufacturing cells (VMCs) with the goal of minimizing two objectives namely, makespan and total travelling distance. To solve this problem two algorithms are proposed: traditional non-dominated sorting genetic algorithm (NSGA-II) and knowledge-based non-dominated sorting genetic algorithm (KBNSGA-II). The difference between these algorithms is that, KBNSGA-II has an additional learning module. Finally, we draw an analogy between the results obtained from algorithms applied to various test problems. The superiority of our KBNSGA-II, based on set coverage and mean ideal distance metrics, is inferred from results.
Mohammad Sadeghi; Parisa Niloofar; Mohsen Ziaee; Zahra Mojaradi
Abstract
One of the key applications of operational research in health systems management is to improve the mechanism of resource allocation and program planning in order to increase the system efficiency. This study seeks to offer an innovative method for the planning and scheduling of health service units with ...
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One of the key applications of operational research in health systems management is to improve the mechanism of resource allocation and program planning in order to increase the system efficiency. This study seeks to offer an innovative method for the planning and scheduling of health service units with the aim of reducing the patients' Length of Stay (LOS) in the Cardiac Surgery Ward of Razavi Hospital of Mashhad. Also, to estimate the patients' LOS, two methods have been applied: multiple One of the key applications of operational research in health systems management is to improve the mechanism of resource allocation and program planning in order to increase the system efficiency. This study seeks to offer an innovative method for the planning and scheduling of health service units with the aim of reducing the patients' Length of Stay (LOS) in the Cardiac Surgery Ward of Razavi Hospital of Mashhad. Also, to estimate the patients' LOS, two methods have been applied: multiple linear regression models and Bayesian networks. The introduced method takes into account all treatment processes of patients in an integrated system and by eliminating any undue waiting time, the length of stay can be reduced to a significant extent. Also, the system efficiency is considerably improved by resolving the current conflicts in the workflow of on-call physicians and optimum allocation of resources, gaining satisfaction of health sector officials and patients. linear regression models and Bayesian networks. The introduced method takes into account all treatment processes of patients in an integrated system and by eliminating any undue waiting time, the length of stay can be reduced to a significant extent. Also, the system efficiency is considerably improved by resolving the current conflicts in the workflow of on-call physicians and optimum allocation of resources, gaining satisfaction of health sector officials and patients.
Majid Adeli; Mostafa Zandieh; Alireza Motameni
Abstract
In this research, the integrated sourcing and inventory policy problem in a pharmaceutical distribution company is investigated. In order to select the superior solution, a new tool is introduced. Sourcing is one of the most critical issues in pharmaceutical industry. In addition, drug inventory shortages ...
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In this research, the integrated sourcing and inventory policy problem in a pharmaceutical distribution company is investigated. In order to select the superior solution, a new tool is introduced. Sourcing is one of the most critical issues in pharmaceutical industry. In addition, drug inventory shortages can cause irreparable humanitarian crises. However, only a limited number of studies has been focused on integrated sourcing and inventory policy of drugs so far. In real-world problems, it is difficult to calculate the exact cost of inventory shortage such as company reputation and humanitarian crises. To overcome this obstacle, in this study, the number of shortage is considered as a separate objective. Likewise, demands of the distributors and breakdowns of suppliers are stochastic, and due to the complicated nature of the problem is difficult to calculate the objective function by using classic methods. So, simulation is used for estimating the objectives of the problem. It’s been proved that the problem of this study is NP-Hard. Therefore, a metaheuristic multi-objective particle swarm optimization (MOPSO) method is used to find the optimal solution. To test the reliability of the model and the proposed algorithm, a real drug distributing problem is used and after estimating a Pareto front, the best answer is chosen by The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method.
Mohammad Bagher Fakhrzad; Zahra Alidoosti
Abstract
In this paper, it was an attempt to be present a practical perishability inventory model. The proposed model adds using spoilage of products and variable prices within a time period to a recently published location-inventory-routing model in order to make it more realistic. Aforementioned model by integration ...
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In this paper, it was an attempt to be present a practical perishability inventory model. The proposed model adds using spoilage of products and variable prices within a time period to a recently published location-inventory-routing model in order to make it more realistic. Aforementioned model by integration of strategic, tactical and operational level decisions produces better results for supply chains. Due to the NP-hard nature of this model, a genetic algorithm with unique chromosome representation is used to achieve the optimal solution and reasonable time. Finally, the analysis is carried out to verify the effectiveness of the algorithm with and without considering the cost of spoiled products.
M. Rabbani; N. Heidari; H. Farrokhi Asl
Volume 3, Issue 2 , December 2016, , Pages 107-122
Abstract
Data envelopment analysis (DEA) is one of non-parametric methods for evaluating efficiency of each unit. Limited resources in healthcare economy is the main reason in measuring efficiency of hospitals. In this study, a bootstrap interval data envelopment analysis (BIRDEA) is proposed for measuring the ...
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Data envelopment analysis (DEA) is one of non-parametric methods for evaluating efficiency of each unit. Limited resources in healthcare economy is the main reason in measuring efficiency of hospitals. In this study, a bootstrap interval data envelopment analysis (BIRDEA) is proposed for measuring the efficiency of hospitals affiliated with the Hamedan University of Medical Sciences. The proposed method is capable to consider uncertainty and sampling errors. The inputs of this model include total number of personals, number of medical equipment, and number of operational beds. Also, outputs consist of number of inpatients, number of outpatients, number of special patients, bed-day, and bed occupancy rate. First, we estimate the efficiency by applying original DEA that does not consider any uncertainty and sampling error; then we utilize RDEA that considers uncertainty and after that we use BRDEA that consider both uncertainty and sampling error with an adaptation of bootstrapped robust data envelopment analysis and could be more reliable for efficiency estimating strategies.
Hamed Homaei; Iraj Mahdavi; Ali Tajdin
Abstract
One of the main concerns of all industries such as mine industries is to increase their profit and keep their customers through improving quality level of their products; but increasing the quality of products usually releases air pollutants. Nowadays the management of air pollutant emissions with harmful ...
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One of the main concerns of all industries such as mine industries is to increase their profit and keep their customers through improving quality level of their products; but increasing the quality of products usually releases air pollutants. Nowadays the management of air pollutant emissions with harmful environmental and health effects is one of the most pressing problems. In this paper, authors study the decision behaviour and coordination issue of a mining metal three-level supply chain with one supplier (extractor), one mineral processor and one manufacturer. We compare the decentralized and the centralized systems and reduce air pollutant emission by designing a revenue sharing contract for the mentioned decentralized supply chain under cap-and-trade regulation. Finally, a numerical example shows that the designed contract not only provides win-win condition for all supply chain members and increases whole supply chain profit but also reduces harmful air pollutant emissions in the supply chain.
Seyed Ali Alavikia; Mohammad Taghi TaghaviFard; Maghsoud Amiri; Parham Azimi
Abstract
However, there is a lot of capital and plenty of manpower in the auto spare part industry, the enterprises and supply chains of this industry do not perform well in our country. This research models a three-level supply chain with multiple manufacturers, distribution centers and retailers, to minimize ...
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However, there is a lot of capital and plenty of manpower in the auto spare part industry, the enterprises and supply chains of this industry do not perform well in our country. This research models a three-level supply chain with multiple manufacturers, distribution centers and retailers, to minimize the total cost by taking into account various disruptions. The database of two active car spare parts companies for five strategic products in one year has been used. Then, the mathematical model is analyzed by considering disruptions based on three different sales policies: back orders, lost sales and outsourcing. Besides, to evaluate the performance of the model some numerical examples are used and analyzed to determine that algorithm works. Model solved efficiently by MATLAB software. The results show that the proposed algorithm of this research can neutralize the effect of the disruptions and cause a significant reduction in total cost of the system. The model is useful for helping decision makers to adopt an active approach to maintaining business benefits when disruptions take place in the supply chain.
Amir Estemari; Mohammad Taleghani; Hossein Safari
Abstract
Food and beverage industries have decisive rule due to amount of employment and significant income generation. At micro perspective, due to the wide range of influencing factors, trends do not follow linear behavior and their analysis with non-dynamic tools is challenging. In general, the food and beverage ...
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Food and beverage industries have decisive rule due to amount of employment and significant income generation. At micro perspective, due to the wide range of influencing factors, trends do not follow linear behavior and their analysis with non-dynamic tools is challenging. In general, the food and beverage industries in Iran, in addition to the global challenges, are facing many problems that make the prevailing environment more complicated. Among these problems, we can mention the old equipment, difficult access to quality raw materials due to sanctions and the instability of economic indicators. Therefore, if strategies performance cannot be adjusted in line with the market, the resulting losses can be significant and irreparable. In this paper, we developed a system dynamics model (simulation in Vensim) to investigating the production strategies and market in a complex space. For this purpose, we run the simulation 12 times with respect to 4 policies and 3 scenarios. The results show that due to profitability, the strategy of deleting loss products achieve the highest score of performance. Meanwhile, strategy of hybrid production (outsource and factory production) selected due to market penetration.
Neda Mozaffari; Hasan Mehrmanesh; Mahmud Mohamadi
Abstract
Balancing the production system’s resources like budget, equipment, and workers is one of the most important concerns of production managers. Managers seek to find an optimal way to balance their resources in production systems. By evaluating U-shaped assembly line papers, this investigation adds ...
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Balancing the production system’s resources like budget, equipment, and workers is one of the most important concerns of production managers. Managers seek to find an optimal way to balance their resources in production systems. By evaluating U-shaped assembly line papers, this investigation adds the literature on U-shaped assembly lines to the simultaneous examination of the balance ergonomic risks of human workers and current costs in the system when government offers tax benefits for using disabled workers. The mentioned outlook was not considered in previous papers. This study proposes a two-objective model to evaluate the effects of considering both robots and human workers in a U-shaped assembly line. The first objective is to minimize the system costs, and the second is to minimize the ergonomic risks. Human workers are divided into normal and disabled. The disabled workers are hired to enable tax benefits from the government. The constraint programming model for small and medium-sized problems and the grasshopper optimization algorithm (GOA) for big problems are developed to dissolve the problem. Numerical results show that two objective functions can also level system costs and ergonomic risks. The sensitivity analysis section analyzes three effective parameters (Production cycle time, Fatigue rate of human workers, and government tax benefit). It is shown that production cycle time directly affects using a robot or human workers (due to their mean time of speed), fatigue rate determines the allocation of tasks, and tax benefit helps to determine whether using disabled workers or not according objective functions. Also, it should be noticed the efficiency of GOA is shown by a comparison of several examples. Therefore, it is used for big-scale test problems.
Hiwa Farughi; Sobhan Mostafayi; Ahmadreza Afrasiabi
Abstract
In this paper, a bi-objective mixed-integer mathematical model is presented for configuration of a dynamic cellular manufacturing system. In this model, dynamic changes and uncertainty in parts demand and machines reliability are considered. The first objective function minimizes total costs and the ...
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In this paper, a bi-objective mixed-integer mathematical model is presented for configuration of a dynamic cellular manufacturing system. In this model, dynamic changes and uncertainty in parts demand and machines reliability are considered. The first objective function minimizes total costs and the second one maximizes the machines reliability through minimizing machines failure. In addition, some routes are considered to produce each part based on operational requirements. An appropriate route is selected respect to the costs and operational time. Some parameters are considered under uncertainty in two categories. The first category such as demand is dependent on market condition and the uncontrolled competitive environment. The second one includes some parameters for production system and machines that are directly related to plans organized by production management. A robust optimization approach is used to deal with parameters uncertainty to produce feasible and optimal solutions. Furthermore, for validation and implementation of results in real world, a case study is investigated. Computational results show that the robust model reports better values for objective functions compared to the scenario-based model. In fact, Pareto-front which are resulted by robust model are dominated by scenario-based models’ Pareto front. Sensitivity analyses on main parameters of the problem are performed to drive some managerial insights that help corresponding decision makers to provide suitable and homogenous decisions in a production system.
Ahmad Ali Abedinpour; Mohsen Yahyaei; Armin Jabbarzadeh
Abstract
Addressing an integrated decision-making structure for planting and harvesting scheduling may lead to more realistic, accurate, and efficient decision in fresh product supply chain. This study aims to develop an integrated bi-objective tactical and operational planning model for producing and distributing ...
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Addressing an integrated decision-making structure for planting and harvesting scheduling may lead to more realistic, accurate, and efficient decision in fresh product supply chain. This study aims to develop an integrated bi-objective tactical and operational planning model for producing and distributing fresh crops. The first objective of the model is to maximize total revenue of supply chain. Over the past few years, there has been a considerable shift in emphasis in social responsibility of supply chains. Therefore, a key purpose of this article is to plan a socially responsible fresh agricultural supply chain as the second objective function. The proposed bi-objective model seeks to make optimal decisions on planting, harvesting scheduling (harvesting pattern), selecting the transport fleet type, and products supply channel to the consumers. To conduct the analysis, numerical examples are provided based on a real case study and the true Pareto front is achieved with augmented ε-constraint method. The results indicated the applicability of the proposed model and verified its validity. Moreover, comparison between total weighting and ε-constraint method is provided to ensure the efficiency of Pareto solutions.
Arezoo Osati; Esmaeil Mehdizadeh; Sadoullah Ebrahimnejad
Abstract
The purpose of this paper is to optimize the integrated problem of lot-sizing and scheduling in a flexible job-shop environment considering energy efficiency. The main contribution of the paper is simultaneously considering lot-sizing and scheduling decisions, while accounting for energy efficiency. ...
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The purpose of this paper is to optimize the integrated problem of lot-sizing and scheduling in a flexible job-shop environment considering energy efficiency. The main contribution of the paper is simultaneously considering lot-sizing and scheduling decisions, while accounting for energy efficiency. In order to achieve this objective, a mathematical model has been developed for integrated optimization of scheduling and lot-sizing problems. The developed model uses a big bucket approach and is presented as a mixed integer nonlinear problem (MINLP). The BARON solver in GAMS software has been used to solve the proposed MINLP model. By defining the relative optimality limit (OPTCR) of 0.05 for the termination criterion in BARON solver, GAMS has not been able to solve large problems at a specified time to achieve relative optimality. Therefore, due to the NP-hard nature of the problem, a new genetic-based evolutionary algorithm has been developed to solve the problem of large scale. In the developed algorithm, a different approach (instead of cross-over and mutation operators) is used to generate a new solution. By presenting and solving various problems, the efficiency of this algorithm for solving big problems is shown. Comparing the values of the objective function obtained from the genetic algorithm and the exact method shows that, especially in large problems, the genetic algorithm has been able to achieve a better solution than GAMS software in a limited time. It has also been shown that energy efficiency has a significant effect on the solution of the problem.
Faezeh Motevalli-Taher; Mohammad Mahdi Paydar
Abstract
In this study, tactical decisions considering the material and financial flows in a supply chain have been made. To achieve these aims and some effective solutions, a multi-objective mathematical model proposed for an integrated supply chain master planning problem. The multi-product, multi-period and ...
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In this study, tactical decisions considering the material and financial flows in a supply chain have been made. To achieve these aims and some effective solutions, a multi-objective mathematical model proposed for an integrated supply chain master planning problem. The multi-product, multi-period and capacitated supply chain network has three objective functions. Two first objective functions are maximizing the net present value of manufacturing centers and suppliers’ cash flow, and the third one minimizes the market price of the final product. Besides we considered the market price as a key variable in the model and investigate its effects. Then, improved multi-choice goal programming is used to transform the multi-objective model to its single-objective one. To find out the appropriateness of the proposed model, the results of an industrial example are illustrated, and sensitivity analyses to evaluate the results are provided to obtain better insight and access to different aspects of the problem.
Ismail Taifa; Tosifbhai Vhora
Abstract
Cycle time is one of the viable parameters which needs to be optimised as much as possible whenever the manufacturing industry is trying to improve efficiency, cost base and customer responsiveness. This systematic study presents on the reduction of cycle time for productivity improvement in the manufacturing ...
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Cycle time is one of the viable parameters which needs to be optimised as much as possible whenever the manufacturing industry is trying to improve efficiency, cost base and customer responsiveness. This systematic study presents on the reduction of cycle time for productivity improvement in the manufacturing industry. In industries, cycle time should be focused due to the high need of balancing man, machine, materials, methods and management. It must be renowned that the reduction of cycle time is not an easy task. Productivity improvement process involves many factors in achieving the maximum reduction of unnecessary time for higher improvement. The appropriate approaches to be implemented includes lean manufacturing tools, value stream method, method-time measurements, just in time for inventory control, motion study, process study, VAT plant classification, total productive maintenance, improved MRP (material requirements planning)-based production planning, theory of constraint, linear programming and other simulation related techniques. The V, A or T types of plant classification can also be classified using optimisation production technology (OPT).
Seyed Ahmad Razavi; Adel Aazami; Mohammad Reza Rasouli; Ali Papi
Abstract
This research focuses on the integrated production-inventory-routing planning (PIRP) problem, which persuades necessary decisions to study the supply chains (SCs). Previous research studies confirm that corporations coping with production, inventory, and routing problems, can remarkably decrease the ...
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This research focuses on the integrated production-inventory-routing planning (PIRP) problem, which persuades necessary decisions to study the supply chains (SCs). Previous research studies confirm that corporations coping with production, inventory, and routing problems, can remarkably decrease the total costs and meet the customers' demands efficaciously. Currently, because of severe obligations, corporations must consider environmental factors and cost optimization in their activities. Accordingly, in this article, a green PIRP (GPIRP) is addressed using mixed-integer linear programming (MILP), which simultaneously takes into account the economic and social decisions of the SCs. Furthermore, because the SCs routing-oriented problems belong to the NP-hard categories, we propose a two-phase heuristic solution method; in the first phase, the inventory and production decisions are determined using MILP formulation. The second phase seeks to find optimal vehicle routing and transportation decisions using a genetic algorithm (GA). Two main deals leading to this paper's unique position are to develop a bi-objective MILP model for the GPIRP and present a novel hybrid two-phase heuristic solution method that sequentially utilizes the CPLEX solver and the proposed GA. To validate the computational performance of the proposed solution method, we conduct a case study from the Ahvaz Sugar Refinery Company in Iran to demonstrate the advantages of the formulated model. Moreover, we handle sensitivity analyses to study the effectiveness of the suggested method for the large-sized examples
Peiman Ghasemi; Abdollah babaeinesami
Abstract
Considering the increasing growth of cities, population and urban fabric density, it seems necessary that emergency facilities and services such as fire stations are positioned optimally so that they can fulfill the demands well. The aim of this study is the optimization of equipment use in the fire ...
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Considering the increasing growth of cities, population and urban fabric density, it seems necessary that emergency facilities and services such as fire stations are positioned optimally so that they can fulfill the demands well. The aim of this study is the optimization of equipment use in the fire stations, minimization the time to arrive at the incident through management of referral call to 125 Sari fire station center so that the referral call to the nearest fire station do not remain unanswered as much as possible and there will be no need to refer to another station. In this research, the resources required at Sari’s fire station were simulated using Enterprise Dynamic software. The input data of the simulation is based on the number and sequence of the time of people's phone calls. After collecting historical data from telephone calls using the function fitting method, the distribution function of available resources is calculated in Minitab software. In the following, the distribution functions of failure in the existing fire engines are calculated using the same method and the obtained information is simulated. The result indicates an improvement of 20% in relief time by adding one source in Sari fire station center.
Ehsan Dehghani; Peyman Taki
Abstract
This paper addresses an integrated multi-echelon location-allocation-inventory problem in a stochastic supply chain. In a bid to be more realistic, the demand and lead time are considered to be hemmed in by uncertainty. To tackle the proposed supply chain network design problem, a two-phase ...
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This paper addresses an integrated multi-echelon location-allocation-inventory problem in a stochastic supply chain. In a bid to be more realistic, the demand and lead time are considered to be hemmed in by uncertainty. To tackle the proposed supply chain network design problem, a two-phase approach based on queuing and optimization models is devised. The queuing approach is first deployed, which is able to cope with inherent uncertainty of parameters. Afterwards, the proposed supply chain network design problem is formulated using a mixed-integer nonlinear model. Likewise, the convexity of the model is proved and the optimal inventory policy as closed-form is acquired. Inasmuch as the concerned problem belongs to NP-hard problems, two meta-heuristic algorithms are employed, which are capable of circumventing the complexity burden of the model. The numerical examples evince the efficient and effective performance of the solving algorithms. Lastly, sensitivity analyses are conducted through which interesting insights are gained.
Parichehr Zamani Fard; Ahmad Goudarzi
Abstract
The high rate of outbreaks of the Coronavirus Disease 2019 (QUID-19) is a warning about health, economic, and social issues that are affecting the whole world. The COVID-19 pandemic has had many financial and economic implications worldwide. These economic turbulences, along with market uncertainty, ...
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The high rate of outbreaks of the Coronavirus Disease 2019 (QUID-19) is a warning about health, economic, and social issues that are affecting the whole world. The COVID-19 pandemic has had many financial and economic implications worldwide. These economic turbulences, along with market uncertainty, can affect investors' confidence in firms' financial performance and, consequently, may lead to various financial crises. Audit quality can affect the auditors' ability to detect material misstatements. This study is conducted to investigate the effect of COVID-19 on audit quality during social distancing in companies listed on the Tehran Stock Exchange. This is an applied study in terms of purpose and a descriptive-comparative one in terms of method. In the study, one main hypothesis and five sub-hypotheses were developed, and the Kruskal-Wallis test with SPSS software was used. The statistical population included companies listed on the Tehran Stock Exchange from 2017 to 2020. The findings suggest that COVID-19 affects audit quality during social distancing in companies listed on the Tehran Stock Exchange.
Nazila Adabavazeh; Mehrdad Nikbakht
Abstract
Airline industry is one of the main infrastructures for sustainable development of a country. The quality of the reverse support service will be effective in increasing the safety and health of the structures, reducing the impact of disasters and reducing costs. The aim of this study is to evaluate the ...
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Airline industry is one of the main infrastructures for sustainable development of a country. The quality of the reverse support service will be effective in increasing the safety and health of the structures, reducing the impact of disasters and reducing costs. The aim of this study is to evaluate the performance of an organization based on the main factors of reverse supply chain with the service quality approach using the Data Envelopment Analysis (DEA) model. In this research, firstly, performance indicators have been identified and then the efficiency of the 24 main factors of reverse supply chain success in the airline industry is determined by the output-oriented DEA-BCC model. The main efficient and inefficient factors are determined by EMS software. Performance measurement can be very useful for managers to allocate resources because it can provide patterns for inefficient units to achieve efficiency and performance improvements.
vida varahrami
Abstract
In every country, the efficiency and probability of small and medium firms will cause to economic growth. In this regard, present study aimed to investigate the effective factors on the performance of active industrial clusters in large and industrial provinces by Panel-VAR model during 2006-2015. Results ...
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In every country, the efficiency and probability of small and medium firms will cause to economic growth. In this regard, present study aimed to investigate the effective factors on the performance of active industrial clusters in large and industrial provinces by Panel-VAR model during 2006-2015. Results indicated that the access to loan, production rate, cluster size, marketing sector in cluster, closeness of cluster to the market, and the increase of manager’s experience have a positive effect while bank facility interest can negatively influence on the performance of industrial clusters.
Mohammad Aali; Shahram Saeidi
Abstract
In this research, a goal programming model is proposed for optimizing the production of Boehmite in the Iranian West Minerals Applied Research Center (IWMARC). This product can be produced using internal or external methods and currently is produced traditionally, and the production process is not optimal. ...
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In this research, a goal programming model is proposed for optimizing the production of Boehmite in the Iranian West Minerals Applied Research Center (IWMARC). This product can be produced using internal or external methods and currently is produced traditionally, and the production process is not optimal. This research optimizes the production process using the linear goal programming technique. A multi-objective model is proposed containing 20 goal constraints of effective parameters concerning production, sales, raw materials usage, water and energy consumption, customer needs, and workforce components. The main objectives are ranked using the AHP method, and the model is implemented in Lingo 11 software. The computational results show that due to the impact of the price of foreign raw materials and the limitations caused by its use, as well as the good efficiency of the gasification method in the internal(domestic) method, the domestic method can effectively tackle the major and minor objectives of the production system of in IWMARC and achieve 16 goals out of 20 goals with zero or positive (more than the expected level) deviations. Besides, changing the technical and production specifications according to customer needs can increase profitability up to 3.75 times the current amount (375%) and decrease inventory cost by 32%.
Iraj Mahdavi; Sara Firouzian; Mohammad Mahdi Paydar; Mahdi Saadat
Abstract
Cell formation problem (CFP) is one of the main problems in cellular manufacturing systems. Minimizing exceptional elements and voids is one of the common objectives in the CFP. The purpose of the present study is to propose a new model for cellular manufacturing systems to group parts and machines in ...
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Cell formation problem (CFP) is one of the main problems in cellular manufacturing systems. Minimizing exceptional elements and voids is one of the common objectives in the CFP. The purpose of the present study is to propose a new model for cellular manufacturing systems to group parts and machines in dedicated cells using a part-machine incidence matrix to minimize the voids. After identifying the exceptional elements, the machines required for processing the remained operations of corresponding parts which are not processed in the dedicated cells are specified. This results in a new matrix called part family-machine. Then, by clustering the part family-machine incidence matrix, the part families that should be assigned to a specific cell to achieve the highest similarity can be determined. The similarity can be translated to sharing machines required for completing the processes and form new cells called shared cells to minimize the number of exceptional elements and voids. Unlike previous models in which the similarity is considered only in the dedicated cells, in the proposed model, the similarity would be monitored and observed in the entire production process. Due to the complexity of our model, two meta-heuristic algorithms including artificial immune system (AIS) and simulated annealing (SA) are proposed. The efficiency of the algorithms is compared to that of exact solutions. Also, the algorithms are compared regarding the quality of solutions. Finally, according to grouping efficacy measure, SA algorithm has a superior performance in comparison with AIS by spending less CPU time.
Erfan Babaee Tirkolaee; Shaghayegh Hadian; Heris Golpira
Abstract
Multi-echelon distribution mechanism is common in supply chain design and logistics systems in which freight is delivered to the customers through intermediate depots (IDs), instead of using direct shipments. This primarily decreases the cost of the chain and consequences of environmental (energy consumption) ...
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Multi-echelon distribution mechanism is common in supply chain design and logistics systems in which freight is delivered to the customers through intermediate depots (IDs), instead of using direct shipments. This primarily decreases the cost of the chain and consequences of environmental (energy consumption) and social (traffic, air pollution, etc.) logistic operations. This paper develops a novel multi-objective mixed-integer linear programming model (MOMILP) for a two-echelon green capacitated vehicle routing problem (2E-CVRP) in which environmental issues and time windows constraints are considered for perishable products delivery phase. To validate the proposed mathematical model, several numerical examples are generated randomly and solved using CPLEX solver of GAMS software. The ε-constraint method is applied to the model to deal with the multi-objectiveness of the proposed model. Finally, the best Pareto solution for each problem is determined based on the reference point approach (RPA) as one of the most effective techniques to help the decision-makers.