Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
31
2.2
2016
02
20
MONITORING TWO-STAGE PROCESSES WITH A PROFILE QUALITY CHARACTERISTIC IN THE SECOND STAGE
MONITORING TWO-STAGE PROCESSES WITH A PROFILE QUALITY CHARACTERISTIC IN THE SECOND STAGE
3
11
5399
FA
H.
Esmaeeli
Dept. of Industrial Engineering-Islamic Azad University, North Tehran
A.
Sadegheih
Dept. of Industrial Engineering-Yazd University, Iran
A.
Amiri
Dept. of Industrial Engineering-shahed University
0000-0002-2385-8910
Y.
Samimi
Dept. of Industrial Engineering-\nK. N. Toosi University of Technology
Journal Article
2013
04
30
In most processes, the quality of a final product depends on the quality of several quality characteristics at previous stages. This is referred to as a cascade property in multi-stage processes. On the other hand, recently, profile monitoring, in which the quality of a process or product is characterized by a relationship between a response variable and one or more explanatory variable(s), has been considered by many researchers. In some applications, the quality of a product in a two-stage process is characterized by a simple linear profile. In this case, monitoring the profile separately leads to misleading results, due to ignoring the cascade property in a two stage process. In this paper, we specifically consider a two stage process where the quality characteristics of the first and second stages are characterized by a univariate normal distribution and a simple linear profile, respectively. Then, we use a model to relate the profile quality characteristic in the second stage to the univariate normal quality characteristic in the first stage. After that, we propose two approaches to account for this problem. In the first approach, the mean and standard deviations of the first stage are monitored by two Shewhart type control charts, namely, $bar{X}$ and R control charts. In addition, we propose using $T^2$ hotelling, as well as a $x^2$ control chart, as selecting control charts to monitor the estimated parameters of the model relates the first and second stage quality characteristics. This implies that getting a signal from these control charts is equivalent to the shift in the second stage. In the second approach, we use EWMA and R control charts to monitor the mean and standard deviations of the normal quality characteristics in the first stage. Then, we use EWMA/$x^2$control charts to monitor the mean and standard deviations of the residuals of the model, which relates the two stages to each other. The EWMA/$x^2$ control charts are similarly selected control charts. The performance of the two proposed approaches is compared through simulation studies and using an average run length (ARL) criterion. The results show the suitable performance of both proposed approaches.
In most processes, the quality of a final product depends on the quality of several quality characteristics at previous stages. This is referred to as a cascade property in multi-stage processes. On the other hand, recently, profile monitoring, in which the quality of a process or product is characterized by a relationship between a response variable and one or more explanatory variable(s), has been considered by many researchers. In some applications, the quality of a product in a two-stage process is characterized by a simple linear profile. In this case, monitoring the profile separately leads to misleading results, due to ignoring the cascade property in a two stage process. In this paper, we specifically consider a two stage process where the quality characteristics of the first and second stages are characterized by a univariate normal distribution and a simple linear profile, respectively. Then, we use a model to relate the profile quality characteristic in the second stage to the univariate normal quality characteristic in the first stage. After that, we propose two approaches to account for this problem. In the first approach, the mean and standard deviations of the first stage are monitored by two Shewhart type control charts, namely, $bar{X}$ and R control charts. In addition, we propose using $T^2$ hotelling, as well as a $x^2$ control chart, as selecting control charts to monitor the estimated parameters of the model relates the first and second stage quality characteristics. This implies that getting a signal from these control charts is equivalent to the shift in the second stage. In the second approach, we use EWMA and R control charts to monitor the mean and standard deviations of the normal quality characteristics in the first stage. Then, we use EWMA/$x^2$control charts to monitor the mean and standard deviations of the residuals of the model, which relates the two stages to each other. The EWMA/$x^2$ control charts are similarly selected control charts. The performance of the two proposed approaches is compared through simulation studies and using an average run length (ARL) criterion. The results show the suitable performance of both proposed approaches.
https://sjie.journals.sharif.edu/article_5399_9704a4fc48ae88598dcbdcdf57f3fdef.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
31
2.2
2016
02
20
MODELING OF INTEGER PROGRAMMING FOR SELECTION OF USEFUL VARIABLES IN A MULTIVARIABLE SYSTEM USING THE MAHALANOBIS-TAGUCHI SYSTEM CASE STUDY: DAMAGE
MODELING OF INTEGER PROGRAMMING FOR SELECTION OF USEFUL VARIABLES IN A MULTIVARIABLE SYSTEM USING THE MAHALANOBIS-TAGUCHI SYSTEM CASE STUDY: DAMAGE
13
18
5400
FA
A.H.
Barahimi
Dept. of Industrial Engineering-K. N. Toosi University of Technology
A.
Aghaie
Dept. of Industrial Engineering-K. N. Toosi University of Technology
Journal Article
2013
09
18
For many years, analysis of real systems has attracted much attention. Such systems are hard to describe because of their complex behavior and their huge number of parameters and mutual effects. This issue has made analysis methods of a multivariable system develop rapidly. Multivariate analysis consists of a collection of methods that can be used when several measurements are made of each individual or object in one or more samples. In practice, multivariate data sets are common, although they are not always analyzed in the same way. However, the exclusive use of univariate procedures with such data is no longer acceptable, given the availability of multivariate techniques and inexpensive computing power. The Mahalanobis-Taguchi system is such a novel method. Nowadays, rapid development of technology has made it possible for organizations to gather large amounts of data for analyzing processes. But, on the other hand, an appropriate approach for dealing with these huge amounts is also required in modern commerce. The main purpose of this paper is to use Mahalanobis-Taguchi systems for effective selection after reorganizing and omitting non-meaning data. This method is for multivariable systems and consists of two parts: The first part deals with a useful variable selection for complexity reduction and the second part contains recognition and prediction processes and identification of abnormal groups. The conventional method uses orthogonal Taguchi arrays for variable selection. In fact, the main novelty of this paper is the use of concepts such as mis-classification and Integer Programming. The solution in our method is based on a creative algorithm, which is also accurate enough. It will be shown that this method is faster than former methods, and that its performance is generally better than previous methods.
For many years, analysis of real systems has attracted much attention. Such systems are hard to describe because of their complex behavior and their huge number of parameters and mutual effects. This issue has made analysis methods of a multivariable system develop rapidly. Multivariate analysis consists of a collection of methods that can be used when several measurements are made of each individual or object in one or more samples. In practice, multivariate data sets are common, although they are not always analyzed in the same way. However, the exclusive use of univariate procedures with such data is no longer acceptable, given the availability of multivariate techniques and inexpensive computing power. The Mahalanobis-Taguchi system is such a novel method. Nowadays, rapid development of technology has made it possible for organizations to gather large amounts of data for analyzing processes. But, on the other hand, an appropriate approach for dealing with these huge amounts is also required in modern commerce. The main purpose of this paper is to use Mahalanobis-Taguchi systems for effective selection after reorganizing and omitting non-meaning data. This method is for multivariable systems and consists of two parts: The first part deals with a useful variable selection for complexity reduction and the second part contains recognition and prediction processes and identification of abnormal groups. The conventional method uses orthogonal Taguchi arrays for variable selection. In fact, the main novelty of this paper is the use of concepts such as mis-classification and Integer Programming. The solution in our method is based on a creative algorithm, which is also accurate enough. It will be shown that this method is faster than former methods, and that its performance is generally better than previous methods.
https://sjie.journals.sharif.edu/article_5400_8466a2b43729c29dcd7cc0fdfa1a9e7a.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
31
2.2
2016
02
20
ANALYSIS OF ECONOMIC VARIABLES AFFECTING THE IRANIAN BREAD INDUSTRY USING A SYSTEM DYNAMICS APPROACH
ANALYSIS OF ECONOMIC VARIABLES AFFECTING THE IRANIAN BREAD INDUSTRY USING A SYSTEM DYNAMICS APPROACH
19
26
5401
FA
Z.
Arasti
Faculty of Entrepreneurship University of Tehran
H.
Badri
Dept. of Industrial and materials Engineering-Amirkabir University of Technolo
T-H.
Hejazi
Dept. of Industrial and materials Engineerin-Sajad University of Technology
Z.
Geilari
Dept. of Industrial Engineerin-Sharif University of Technology
Journal Article
2013
09
25
In an Iranian household, the bread basket indicates its importance in family health and economics. Therefore, the subject of producing bread with desirable quality has always been considered by different governmental programs and plans. In this regard, the low quality of bread, the high rate of loss and low productivity in traditional bakeries are main challenges in this industry. As a consequence of executing supportive government plans, in recent years, many construction permits were issued, but few of them succeeded. Many factors could be obstacles in the desirable development of mechanized bakeries. Furthermore, traditional bread is considered the most important and the cheapest food in the Iranian diet. While the quality of this food has a great impact on public health, its waste can also be viewed as a major economic challenge. Different solutions have been provided to improve the quality of bread and thus reduce its waste. Among them, we can point out the development of mechanized bakeries. Due to the great importance of the bread industry, it is essential to achieve a unique perspective to provide solutions, and design policies in line with the intended objectives. Also, a shared view in all organizations and relevant bodies should be provided. This research studies the barriers of the start-up and growth of mechanized bakeries. In this study, seven main obstacles are identified using expert studies, interviews and expert opinion in the public and private sector, including managers and practitioners in associations, federations and the managers of small, medium and large bakery businesses. This paper analyzes effective financial factors in the system of the Iranian bread industry using system dynamics. Considering the limitations, such as the large number of variables from different areas, these effects have been analyzed. Consequently, the development of mechanized bakeries, as a main solution for increasing the performance indicators of the bread industry, has been considered.
In an Iranian household, the bread basket indicates its importance in family health and economics. Therefore, the subject of producing bread with desirable quality has always been considered by different governmental programs and plans. In this regard, the low quality of bread, the high rate of loss and low productivity in traditional bakeries are main challenges in this industry. As a consequence of executing supportive government plans, in recent years, many construction permits were issued, but few of them succeeded. Many factors could be obstacles in the desirable development of mechanized bakeries. Furthermore, traditional bread is considered the most important and the cheapest food in the Iranian diet. While the quality of this food has a great impact on public health, its waste can also be viewed as a major economic challenge. Different solutions have been provided to improve the quality of bread and thus reduce its waste. Among them, we can point out the development of mechanized bakeries. Due to the great importance of the bread industry, it is essential to achieve a unique perspective to provide solutions, and design policies in line with the intended objectives. Also, a shared view in all organizations and relevant bodies should be provided. This research studies the barriers of the start-up and growth of mechanized bakeries. In this study, seven main obstacles are identified using expert studies, interviews and expert opinion in the public and private sector, including managers and practitioners in associations, federations and the managers of small, medium and large bakery businesses. This paper analyzes effective financial factors in the system of the Iranian bread industry using system dynamics. Considering the limitations, such as the large number of variables from different areas, these effects have been analyzed. Consequently, the development of mechanized bakeries, as a main solution for increasing the performance indicators of the bread industry, has been considered.
https://sjie.journals.sharif.edu/article_5401_3ab6be46e1d6b21d59a3c3a0b9d0f6ef.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
31
2.2
2016
02
20
ANALYSIS OF CUSTOMER BEHAVIOR IN PURCHASING AND SENDING ONLINE GROUP SMS USING DATA MINING BASED ON THE RFM MODEL
ANALYSIS OF CUSTOMER BEHAVIOR IN PURCHASING AND SENDING ONLINE GROUP SMS USING DATA MINING BASED ON THE RFM MODEL
27
35
5402
FA
M.
Ebadi Jalal
Dept. of Industrial Engineering-K. N. Toosi University of Technology
S.
Alizadeh
Dept. of Industrial Engineering-K. N. Toosi University of Technology
Journal Article
2013
12
01
For effective Customer Relationship Management (CRM), it is important to gather information on customer value. The concept of segmentation is central to CRM. Segmentation is the method of knowing the customers and partitioning a population of customers into smaller groups. In other words, it means partitioning a population of customers into different segments, considering the most within-segment homogeneity and between-segment heterogeneity. It also allows a company to differentially treat consumers in different segments. The mass marketing approach cannot satisfy the needs of todays diverse customers. This diversity should be satisfied using segmentation that divides markets into customer clusters with similar needs and/or characteristics that are likely to exhibit similar purchasing behaviors. One-to-one marketing is the ideal marketing strategy, in which every marketing campaign or product is optimally targeted at each individual customer; but this is not always possible. Thus, segmentation is required to distinguish similar clients and put them together in a segment. Doubtlessly, using segmentation to understand customer need is much easier, faster and more economical than uniquely investing in an understanding of individuals. This paper provides a proper framework for customer segmentation based on their lifetime value. In this study, first, we try to calculate RFM variables; recency (R), frequency (F) and monetary (M), in two groups of data including purchasing SMS (331 clients) and sending SMS (248 clients). Then, values are weighted with research and based on the advice of experts, and the optimal number of clusters based on Davis was determined. The results of the present study are to analyze and compare customer behavior in both the purchasing and sending processes. Then, we identified customer segmentation, customer lifetime value in the form of a pyramid, and the value of the companys key customers. Finally, some suggestions are offered to improve the company purchasing and sending systems.
For effective Customer Relationship Management (CRM), it is important to gather information on customer value. The concept of segmentation is central to CRM. Segmentation is the method of knowing the customers and partitioning a population of customers into smaller groups. In other words, it means partitioning a population of customers into different segments, considering the most within-segment homogeneity and between-segment heterogeneity. It also allows a company to differentially treat consumers in different segments. The mass marketing approach cannot satisfy the needs of todays diverse customers. This diversity should be satisfied using segmentation that divides markets into customer clusters with similar needs and/or characteristics that are likely to exhibit similar purchasing behaviors. One-to-one marketing is the ideal marketing strategy, in which every marketing campaign or product is optimally targeted at each individual customer; but this is not always possible. Thus, segmentation is required to distinguish similar clients and put them together in a segment. Doubtlessly, using segmentation to understand customer need is much easier, faster and more economical than uniquely investing in an understanding of individuals. This paper provides a proper framework for customer segmentation based on their lifetime value. In this study, first, we try to calculate RFM variables; recency (R), frequency (F) and monetary (M), in two groups of data including purchasing SMS (331 clients) and sending SMS (248 clients). Then, values are weighted with research and based on the advice of experts, and the optimal number of clusters based on Davis was determined. The results of the present study are to analyze and compare customer behavior in both the purchasing and sending processes. Then, we identified customer segmentation, customer lifetime value in the form of a pyramid, and the value of the companys key customers. Finally, some suggestions are offered to improve the company purchasing and sending systems.
https://sjie.journals.sharif.edu/article_5402_d1359f9934ee5a75ed441ffce412a0a1.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
31
2.2
2016
02
20
IDENTIFICATION AND RANKING OF FACTORS AFFECTING PRIVATIZATION OF THE IRANIAN GAS REFINING INDUSTRY; AN INTEGRATED APPROACH USING DEMATEL-ANP: PARSIAN
IDENTIFICATION AND RANKING OF FACTORS AFFECTING PRIVATIZATION OF THE IRANIAN GAS REFINING INDUSTRY; AN INTEGRATED APPROACH USING DEMATEL-ANP: PARSIAN
37
49
5403
FA
R.
Eshgarf
Dept. of Industrial Engineering- Iran University of Science and Technology
S.
Mirzamohammadi
Dept. of Industrial Engineering- Iran University of Science and Technology
S.J.
Sadjadi
Dept. of Industrial Engineering- Iran University of Science and Technology
Journal Article
2013
12
16
The present study was designed to identify and rank factors that influence the privatization process of Iranian gas refining companies, in three stages, with the help of Delphi and DEMATEL techniques and the Analytic Network Process approach. Extracting factors affecting the privatization process, addressed in the literature of the subject, along with the Delphi survey of experts, led to the verification of factors that influence the privatization process of Iranian gas refining industries. Based on the findings of this study, based on the Delphi technique, the experts have identified a total of 39 key factors in the privatization of gas refining companies.Subsequently, using DEMATEL techniques, the influential factors extracted by the experts in the previous stage prioritized a structure based on graph theory.Accordingly, the relationships between the factors and the severity of the impact and their mutual interaction are among the results. Based on these results, cohesion and harmony among all political sections in the privatization process from legislation to implementation and supervision, has the highest impact factor and legal supervision as a factor to reform and reinforce the market and to support healthy economic activities without threatening and undermining it has the highest impressibility factor. Finally, using the Analytic Network Process approach, the end results of the ranking of factors influencing improvements in the privatization process of Iranian gas refining industries are obtained. Respectively, the political, economic, legal, management and administrative, technical and technological, social and cultural, international and institutional aspects are prioritized. In addition to the main aspects, associated sub-indexes have also been prioritized.
The present study was designed to identify and rank factors that influence the privatization process of Iranian gas refining companies, in three stages, with the help of Delphi and DEMATEL techniques and the Analytic Network Process approach. Extracting factors affecting the privatization process, addressed in the literature of the subject, along with the Delphi survey of experts, led to the verification of factors that influence the privatization process of Iranian gas refining industries. Based on the findings of this study, based on the Delphi technique, the experts have identified a total of 39 key factors in the privatization of gas refining companies.Subsequently, using DEMATEL techniques, the influential factors extracted by the experts in the previous stage prioritized a structure based on graph theory.Accordingly, the relationships between the factors and the severity of the impact and their mutual interaction are among the results. Based on these results, cohesion and harmony among all political sections in the privatization process from legislation to implementation and supervision, has the highest impact factor and legal supervision as a factor to reform and reinforce the market and to support healthy economic activities without threatening and undermining it has the highest impressibility factor. Finally, using the Analytic Network Process approach, the end results of the ranking of factors influencing improvements in the privatization process of Iranian gas refining industries are obtained. Respectively, the political, economic, legal, management and administrative, technical and technological, social and cultural, international and institutional aspects are prioritized. In addition to the main aspects, associated sub-indexes have also been prioritized.
https://sjie.journals.sharif.edu/article_5403_bb1443cc31d7396bf73e7858cea114e1.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
31
2.2
2016
02
20
PLANNING CLOSED LOOP SUPPLY CHAIN WITH DYNAMIC DETERMINISTIC DEMAND AND \r\nCONTINUOUS PRICE DECREASE
PLANNING CLOSED LOOP SUPPLY CHAIN WITH DYNAMIC DETERMINISTIC DEMAND AND \r\nCONTINUOUS PRICE DECREASE
27
35
5404
FA
H.R.
Kamali
Dept. of Industrial Engineering-University of Yazd
A.
Sadegheih
Dept. of Industrial Engineering-University of Yazd
M.A.
Vahdat-Zad
Dept. of Industrial Engineering-University of Yazd
H.
Khademi-Zare
Dept. of Industrial Engineering-University of Yazd
Journal Article
2013
12
25
In a global economy, providing products, at the right time in the right quantity and at a low cost, can be regarded as a key to success. Efficient supply chains have an important role in guaranteeing this success. The objective of this paper is to plan a single product, multi-echelon, multi-period closed loop supply chain (CLSC) for high-tech products, and, finally, the decisions made regarding component procurement, production, distribution, recycling and disposal. The considered planning problem is like a Knapsack problem. Therefore, it can be concluded that it is NP-hard. To plan the explored CLSC problem, the time horizon is divided into some equal periods, and planning is done for them. The more the number of divisions or periods and the closer the planning to reality, the more the dimensions of the problem and the more the amount of solving time needed. This is especially true in NP-hard problems. When analytic methods such as the branc and bound method (for solving MILP model) are used, an increase of the problem dimensions leads to a drastic increase in solving time. Thus, in the case of these problems, metaheuristic algorithms should be used to make a near optimal solution. So, four proposed heuristic-based variables, including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), and the artificial bee colony (ABC), were implemented in order to solve the mixed integer linear programming model (MILP). Finally, the computational results obtained through these four methods were compared with the solutions obtained by GAMS optimization software. The solution revealed that the DE methodology performs very well in terms of both quality of solution obtained and computational time. The results of this study indicated an approximate solution for selecting active markets among potential markets. Also, for determining the time and quantity of components and products to produce and ship in a CLSC, in general, and for high-tech products, in particular, by dividing the time horizon into many periods, which increases the accuracy of planning.
In a global economy, providing products, at the right time in the right quantity and at a low cost, can be regarded as a key to success. Efficient supply chains have an important role in guaranteeing this success. The objective of this paper is to plan a single product, multi-echelon, multi-period closed loop supply chain (CLSC) for high-tech products, and, finally, the decisions made regarding component procurement, production, distribution, recycling and disposal. The considered planning problem is like a Knapsack problem. Therefore, it can be concluded that it is NP-hard. To plan the explored CLSC problem, the time horizon is divided into some equal periods, and planning is done for them. The more the number of divisions or periods and the closer the planning to reality, the more the dimensions of the problem and the more the amount of solving time needed. This is especially true in NP-hard problems. When analytic methods such as the branc and bound method (for solving MILP model) are used, an increase of the problem dimensions leads to a drastic increase in solving time. Thus, in the case of these problems, metaheuristic algorithms should be used to make a near optimal solution. So, four proposed heuristic-based variables, including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), and the artificial bee colony (ABC), were implemented in order to solve the mixed integer linear programming model (MILP). Finally, the computational results obtained through these four methods were compared with the solutions obtained by GAMS optimization software. The solution revealed that the DE methodology performs very well in terms of both quality of solution obtained and computational time. The results of this study indicated an approximate solution for selecting active markets among potential markets. Also, for determining the time and quantity of components and products to produce and ship in a CLSC, in general, and for high-tech products, in particular, by dividing the time horizon into many periods, which increases the accuracy of planning.
https://sjie.journals.sharif.edu/article_5404_c4098249cc953bd98b54e57a1ad89d12.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
31
2.2
2016
02
20
MONITORING BINOMIAL RESPONSE VARIABLE BASED ON GENERALIZED LINEAR MODELS IN TWO STAGE PROCESSES
MONITORING BINOMIAL RESPONSE VARIABLE BASED ON GENERALIZED LINEAR MODELS IN TWO STAGE PROCESSES
61
71
5405
FA
M. S.
Owlia
Department of Industrial Engineering-\nYazd University
A.
Amiri
Department of Industrial Engineering-\nShahed University
0000-0002-2385-8910
M.H.
Doroudyan
Dept. of Industrial Engineering-\nYazd University
A.
Asgari
Dept. of Industrial Engineering-\nShahed University
Journal Article
2014
01
11
Nowadays, the process behind many productions and services has several sequential stages. According to the cascade effect in most of these processes, monitoring each stage without considering the relation between quality characteristics at different stages could lead to misleading results. Therefore, monitoring multistage processes under different assumptions is widely developed by many authors. Sometimes, quality characteristics at different stages have a binomial distribution; for example, the number of nonconforming items in one batch of products at different stages. To the best of our knowledge, there is no method to monitor this type of quality characteristic. Note that monitoring each stage using a conventional np control chart is a misleading approach. In this paper, a two stages process is considered, in which the quality characteristic in the second stage follows a binomial distribution. We propose a generalized linear model (GLM) based control chart for monitoring the process. To establish the relationship between the first and second stage quality characteristics, we use a Logit link function that is suitable for a binomial response variable. Then, the deviance residual (DR) control statistic is constructed using generalized likelihood ratio (GLR) test to monitor the binomial variable at the second stage. This study is investigated in Phase II, therefore, it is assumed that the distribution parameters of quality characteristics in stages, and the parameters of the link function are known, based on Phase I analysis. The performance of the proposed method is evaluated through two numerical examples, in terms of the average run length criterion, and is compared with that of the np-chart. In the first example, the quality characteristic at the first stage has normal distribution. The simulation results indicate that the proposed chart outperforms the np-chart, while the first stage quality characteristic in the second example has binomial distribution. The simulation study shows that for some parameter values in the latter example, the out-of-control average run length (ARL) is larger than the in-control ARL. This problem roughly can be solved by increasing the sample size. However, the proposed method leads to better results.
Nowadays, the process behind many productions and services has several sequential stages. According to the cascade effect in most of these processes, monitoring each stage without considering the relation between quality characteristics at different stages could lead to misleading results. Therefore, monitoring multistage processes under different assumptions is widely developed by many authors. Sometimes, quality characteristics at different stages have a binomial distribution; for example, the number of nonconforming items in one batch of products at different stages. To the best of our knowledge, there is no method to monitor this type of quality characteristic. Note that monitoring each stage using a conventional np control chart is a misleading approach. In this paper, a two stages process is considered, in which the quality characteristic in the second stage follows a binomial distribution. We propose a generalized linear model (GLM) based control chart for monitoring the process. To establish the relationship between the first and second stage quality characteristics, we use a Logit link function that is suitable for a binomial response variable. Then, the deviance residual (DR) control statistic is constructed using generalized likelihood ratio (GLR) test to monitor the binomial variable at the second stage. This study is investigated in Phase II, therefore, it is assumed that the distribution parameters of quality characteristics in stages, and the parameters of the link function are known, based on Phase I analysis. The performance of the proposed method is evaluated through two numerical examples, in terms of the average run length criterion, and is compared with that of the np-chart. In the first example, the quality characteristic at the first stage has normal distribution. The simulation results indicate that the proposed chart outperforms the np-chart, while the first stage quality characteristic in the second example has binomial distribution. The simulation study shows that for some parameter values in the latter example, the out-of-control average run length (ARL) is larger than the in-control ARL. This problem roughly can be solved by increasing the sample size. However, the proposed method leads to better results.
https://sjie.journals.sharif.edu/article_5405_ca6ab34959489659f8c3776aaf1f8efd.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
31
2.2
2016
02
20
IMPROVING THE FMEA METHOD USING A COMBINATION OF DEMATEL AND TAGUCHI METHODS, AND GRAY RELATIONAL ANALYSIS
IMPROVING THE FMEA METHOD USING A COMBINATION OF DEMATEL AND TAGUCHI METHODS, AND GRAY RELATIONAL ANALYSIS
73
82
5406
FA
M.
Shafiei Nikabadi
Dept. of Industrial Management-\nSemnan University
H.
Farahmand
Dept. of Industrial Management-\nSemnan University
S.
Fallah Sanami
Dept. of Industrial Management-\nSemnan University
Journal Article
2014
01
26
In manufacturing activities, issues such as the intensity of competition, rising and changing customer expectations, and ever-increasing technological developments, intensify manufacturer commitment to eliminate defects in its products and eliminate any deviation from standard performance. To realize the above objective, todays organizations use a tool called Failure Modes and Effects Analysis (FMEA) to ensure flawless products are sent to markets. This method, while being useful, has some downsides, including non-use of the weighting method. Most current risk assessment methods use the Risk Priority Number (RPN) value to evaluate the risk of failure. However, traditional RPN methodology has been criticized as having several shortcomings. Therefore, in this paper, an efficient simplified algorithm to evaluate the ordering of risks for failure problems is proposed. RPN also never considers the relationship between failure mode and failure reason. To resolve these problems and improve the FMEA method, it uses three algorithms, Taguchi, Gray Relational Analysis, DEMATEL and their combination. The case study is the TOOS automotive care company and the piece under examination is the door hinge of SAMAND.
In manufacturing activities, issues such as the intensity of competition, rising and changing customer expectations, and ever-increasing technological developments, intensify manufacturer commitment to eliminate defects in its products and eliminate any deviation from standard performance. To realize the above objective, todays organizations use a tool called Failure Modes and Effects Analysis (FMEA) to ensure flawless products are sent to markets. This method, while being useful, has some downsides, including non-use of the weighting method. Most current risk assessment methods use the Risk Priority Number (RPN) value to evaluate the risk of failure. However, traditional RPN methodology has been criticized as having several shortcomings. Therefore, in this paper, an efficient simplified algorithm to evaluate the ordering of risks for failure problems is proposed. RPN also never considers the relationship between failure mode and failure reason. To resolve these problems and improve the FMEA method, it uses three algorithms, Taguchi, Gray Relational Analysis, DEMATEL and their combination. The case study is the TOOS automotive care company and the piece under examination is the door hinge of SAMAND.
https://sjie.journals.sharif.edu/article_5406_ee389847678a3a9d1ce9e4ca69200d06.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
31
2.2
2016
02
20
DEVELOPING AN INVENTORY MODEL UNDER A VENDOR-\MANAGED INVENTORY (VMI) POLICY WITH DEFECTIVE ITEMS AND PRICE-DEPENDENT DEMAND
DEVELOPING AN INVENTORY MODEL UNDER A VENDOR-\MANAGED INVENTORY (VMI) POLICY WITH DEFECTIVE ITEMS AND PRICE-DEPENDENT DEMAND
83
91
5407
FA
M.
Seifbarghy
Dept. of Industrial Engineering-Alzahra University
M.
Setak
Dept. of Industrial Engineering-K.N. Toosi University of Technology
E.
T. Anari
Dept. of Industrial Engineering-Alzahra University
Journal Article
2014
03
11
Organizations in a supply chain are independent entities. Although a completely integrated solution may result in optimal system performance, supply chain members are interested in optimizing their own objective rather than that of the entire system. Thus, a key point in supply chain management is to develop mechanisms that can coordinate independent member decisions in order to optimize system performance. Vendor-managed inventory (VMI) is a new method in supply chain integration, in which the supplier is responsible for the retailers inventory replenishment and control. One of the most important tools for building an integrated supply chain is pricing, which increases the benefits of a supply chain through a better matching of supply and demand. Moreover, the demand rate of most items is price-dependent, and downstream members order to upstream members based on their demand. So, its necessary to consider the pricing problem besides inventory problems. In this article, the inventory model for two echelon single manufacturer-single retailer decentralized supply chain under the VMI policy is considered. This model is formulated with the aim of optimizing the replenishment frequency, replenishment quantity and pricing policies at the same time in order to maximizing supply chain profit. The demand is price sensitive, and the manufacturer, in order to meet the demand, sends the production lot to the retailer in several smaller lots. Producing defective items is an inevitable issue that the companies face during the production process, which happens when the system is out of control. In this model, the production process is imperfect and shortage is allowed. Game theory has become an essential tool in the analysis of supply chains with multiple agents, often with conflicting objectives. Often, analysis of a non-coordination situation is performed using the Stackelberg game. We present a simple algorithm and program to find the Stackelberg game equilibrium. Next, we solve a numerical example to illustrate the solution procedure, the algorithm and program. Finally, the effects of relevant parameters on chain member profits and optimal decision values are proposed by sensitivity analysis.
Organizations in a supply chain are independent entities. Although a completely integrated solution may result in optimal system performance, supply chain members are interested in optimizing their own objective rather than that of the entire system. Thus, a key point in supply chain management is to develop mechanisms that can coordinate independent member decisions in order to optimize system performance. Vendor-managed inventory (VMI) is a new method in supply chain integration, in which the supplier is responsible for the retailers inventory replenishment and control. One of the most important tools for building an integrated supply chain is pricing, which increases the benefits of a supply chain through a better matching of supply and demand. Moreover, the demand rate of most items is price-dependent, and downstream members order to upstream members based on their demand. So, its necessary to consider the pricing problem besides inventory problems. In this article, the inventory model for two echelon single manufacturer-single retailer decentralized supply chain under the VMI policy is considered. This model is formulated with the aim of optimizing the replenishment frequency, replenishment quantity and pricing policies at the same time in order to maximizing supply chain profit. The demand is price sensitive, and the manufacturer, in order to meet the demand, sends the production lot to the retailer in several smaller lots. Producing defective items is an inevitable issue that the companies face during the production process, which happens when the system is out of control. In this model, the production process is imperfect and shortage is allowed. Game theory has become an essential tool in the analysis of supply chains with multiple agents, often with conflicting objectives. Often, analysis of a non-coordination situation is performed using the Stackelberg game. We present a simple algorithm and program to find the Stackelberg game equilibrium. Next, we solve a numerical example to illustrate the solution procedure, the algorithm and program. Finally, the effects of relevant parameters on chain member profits and optimal decision values are proposed by sensitivity analysis.
https://sjie.journals.sharif.edu/article_5407_110209d8fae7417509ba71ad97c17639.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
31
2.2
2016
02
20
A HYBRID APPROACH BASED ON A REVIEW NETWORK AND LOCAL FEATURES OF ENTITIES TO \r\nDETECT FAKE REVIEWS OF ONLINE CUSTOMERS
A HYBRID APPROACH BASED ON A REVIEW NETWORK AND LOCAL FEATURES OF ENTITIES TO \r\nDETECT FAKE REVIEWS OF ONLINE CUSTOMERS
93
103
5408
FA
M.
Fathian
Dept. of Industrial Engineering-\nIran University of Science and Technology
M.R.
Gholamian
Dept. of Industrial Engineering-\nIran University of Science and Technology
A.
Rezaee
Dept. of Industrial Engineering-\nIran University of Science and Technology
Journal Article
2014
03
16
With the growth of electronic commerce, the number of customer reviews on e-commerce websites is growing. These reviews contain valuable information that helps future customers make better decisions about their purchases and allows retailers to promote their products, services and marketing solutions. These reviews have been a major target for fraudster attacks, as they directly influence customer purchase decisions. Fraudsters are usually deployed by companies to write fake reviews to promote their products and to divert customers from Competitors. Submitted reviews are typically done by experienced professionals, with the aim of writing plausible criticism. Due to the above mentioned issue and the fact that customer review mining without removing fake reviews will have low efficiency, fraud detection and automatic identification of fraudsters in online reviews is a necessity for e-commerce. This study designs a hybrid system to detect online fake reviews that uses the features of objects and the relationships between various entities simultaneously. Local features are the textual and non-textual features of reviews, the behavioral and demographic attributes of reviewers and product properties. The relations and classes of related entities are also used as predictors. Local features and network effects, between them, simultaneously reveal some parts of fraud signs, and development of a system to detect fake reviews is the main aim of this research. A communication network of reviews and users is formed based on the trust and block network between users, feedback for reviews, and common users and products. To achieve this goal, relational data mining with a relational dependency network algorithm is used that combines local features and relations internally. This method is performed on two review data sets and the results show the improvement in the efficiency of this approach in comparison to similar methods. In addition, this method provides a flexible method for diverse comment networks, based on the considered dataset,without the need to intuitive assumptions about the relationship between entities.
With the growth of electronic commerce, the number of customer reviews on e-commerce websites is growing. These reviews contain valuable information that helps future customers make better decisions about their purchases and allows retailers to promote their products, services and marketing solutions. These reviews have been a major target for fraudster attacks, as they directly influence customer purchase decisions. Fraudsters are usually deployed by companies to write fake reviews to promote their products and to divert customers from Competitors. Submitted reviews are typically done by experienced professionals, with the aim of writing plausible criticism. Due to the above mentioned issue and the fact that customer review mining without removing fake reviews will have low efficiency, fraud detection and automatic identification of fraudsters in online reviews is a necessity for e-commerce. This study designs a hybrid system to detect online fake reviews that uses the features of objects and the relationships between various entities simultaneously. Local features are the textual and non-textual features of reviews, the behavioral and demographic attributes of reviewers and product properties. The relations and classes of related entities are also used as predictors. Local features and network effects, between them, simultaneously reveal some parts of fraud signs, and development of a system to detect fake reviews is the main aim of this research. A communication network of reviews and users is formed based on the trust and block network between users, feedback for reviews, and common users and products. To achieve this goal, relational data mining with a relational dependency network algorithm is used that combines local features and relations internally. This method is performed on two review data sets and the results show the improvement in the efficiency of this approach in comparison to similar methods. In addition, this method provides a flexible method for diverse comment networks, based on the considered dataset,without the need to intuitive assumptions about the relationship between entities.
https://sjie.journals.sharif.edu/article_5408_0678ca2eae02d542cc931e81b74de122.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
31
2.2
2016
02
20
DEVELOPMENT OF AN IMPROVED CLUSTERING ALGORITHM FOR USE ON SAREM HOSPITAL INFERTILITY DATA
DEVELOPMENT OF AN IMPROVED CLUSTERING ALGORITHM FOR USE ON SAREM HOSPITAL INFERTILITY DATA
105
112
5409
FA
N.
Aghabeigi
Dept. of Industrial Engineering-\nK.N Toosi University of Technology
S.
Alizadeh
Dept. of Industrial Engineering-\nK.N Toosi University of Technology
A.
Saremi
IVF & Fertility Specialist Sarem Hospital
Journal Article
2013
06
29
Nowadays, leading-edge advanced medical tools and new ways of communication are two important considerations in any medical discussions. Algorithms introduced in the field of data mining are able to appropriately interpret and analyze a variety of problems. Data mining is a category of methods helping extraction of information from given data in a way that the output is useful, either for decision making, prediction or estimation. Banking procedures, customer behaviour analysis, medical applications, classification of customers and their needs are some of the fields that employ data mining. In the medical field, data mining has been employed successfully in diagnosis and treatment of diseases. Along with global advances in medical science, researchers and the medical community in Iran have progressed notably in discovering new methods of dealing with infertility. Clustering is an unsupervised method used in data mining to partition a set of unlabeled objects by putting them into groups, such that elements in each group are more similar to each other, in some sense, compared to elements of other groups. One of the common clustering algorithms is known as K-means. Even though successful, the randomness embedded in the algorithm, while selecting initial clusters, causes a variation in results which is not desired. This body of work proposes a new method which uses a hierarchical algorithm to improve the initial cluster selections of K-means. This new ensemble method is applied to infertile patient data in Sarem Hospital and the results are shown. This data has been collected from patients with infertility problems being treated using the ICSI method.
Nowadays, leading-edge advanced medical tools and new ways of communication are two important considerations in any medical discussions. Algorithms introduced in the field of data mining are able to appropriately interpret and analyze a variety of problems. Data mining is a category of methods helping extraction of information from given data in a way that the output is useful, either for decision making, prediction or estimation. Banking procedures, customer behaviour analysis, medical applications, classification of customers and their needs are some of the fields that employ data mining. In the medical field, data mining has been employed successfully in diagnosis and treatment of diseases. Along with global advances in medical science, researchers and the medical community in Iran have progressed notably in discovering new methods of dealing with infertility. Clustering is an unsupervised method used in data mining to partition a set of unlabeled objects by putting them into groups, such that elements in each group are more similar to each other, in some sense, compared to elements of other groups. One of the common clustering algorithms is known as K-means. Even though successful, the randomness embedded in the algorithm, while selecting initial clusters, causes a variation in results which is not desired. This body of work proposes a new method which uses a hierarchical algorithm to improve the initial cluster selections of K-means. This new ensemble method is applied to infertile patient data in Sarem Hospital and the results are shown. This data has been collected from patients with infertility problems being treated using the ICSI method.
https://sjie.journals.sharif.edu/article_5409_e17a886efc21fa45b9dc49a17c29dcf1.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
31
2.2
2016
02
20
INTRODUCTION OF A METHOD TO COMPARE PROFILES WITH LTB AND STB TIME-ORIENTED QUALITY CHARACTERISTICS
INTRODUCTION OF A METHOD TO COMPARE PROFILES WITH LTB AND STB TIME-ORIENTED QUALITY CHARACTERISTICS
113
119
5410
FA
M.R.
Nabatchian
Dept. of Industrial Engineering-\nK.N.Toosi University of Technology
H.
Shahriari
Dept. of Industrial Engineering-\nK.N.Toosi University of Technology
R.
Shafaei
Dept. of Industrial Engineering-\nK.N.Toosi University of Technology
Journal Article
2013
07
27
In recent years, applications of quality characteristics, which take different values in time, have been widely used. These quality characteristics are called time oriented quality characteristics, and can be defined for different types of quality characteristic; such as, nominal the best (NTB), the larger the better (LTB) and the smaller the better (STB). For these quality characteristics, a target profile is defined and the goal is to adjust the control factors of the product in order to have a profile as close to the target profile as possible. The main application of this type of quality characteristic is in pharmaceutical industries. The best design for drug components is searched based on the properties of the drug, such as drug release in the human body, which must follow the target profile. Based on the input data, different approaches may be used. When the entire set of data is available and the relationship between a quality characteristic and the independent variables is defined, approaches such as minimizing the total mean square error, minimizing the sum of quality loss function and defective costs, desirability function, and etc. may be used to optimize control factor settings. However, in some situations with less available information or complex relationships, these approaches cannot be applied, and the only way is to compare the profiles obtained from different design parameters with the target profile to select the appropriate profile and the control factor designs. The most applicable indices for comparing the profiles of time oriented quality characteristics are f1 and f2, which were introduced in pharmaceutical papers for comparing the profiles of drug release with the target profile. These indices are applied for NTB quality characteristics and do not have any application to the LTB and STB quality characteristics. In this research, a new approach for selecting the appropriate profile among existing profiles for LTB and STB quality characteristics is introduced. The proposed method is evaluated by numerical examples. The results show the high ability of the suggested approach in selecting the appropriate profile, based on user policies.
In recent years, applications of quality characteristics, which take different values in time, have been widely used. These quality characteristics are called time oriented quality characteristics, and can be defined for different types of quality characteristic; such as, nominal the best (NTB), the larger the better (LTB) and the smaller the better (STB). For these quality characteristics, a target profile is defined and the goal is to adjust the control factors of the product in order to have a profile as close to the target profile as possible. The main application of this type of quality characteristic is in pharmaceutical industries. The best design for drug components is searched based on the properties of the drug, such as drug release in the human body, which must follow the target profile. Based on the input data, different approaches may be used. When the entire set of data is available and the relationship between a quality characteristic and the independent variables is defined, approaches such as minimizing the total mean square error, minimizing the sum of quality loss function and defective costs, desirability function, and etc. may be used to optimize control factor settings. However, in some situations with less available information or complex relationships, these approaches cannot be applied, and the only way is to compare the profiles obtained from different design parameters with the target profile to select the appropriate profile and the control factor designs. The most applicable indices for comparing the profiles of time oriented quality characteristics are f1 and f2, which were introduced in pharmaceutical papers for comparing the profiles of drug release with the target profile. These indices are applied for NTB quality characteristics and do not have any application to the LTB and STB quality characteristics. In this research, a new approach for selecting the appropriate profile among existing profiles for LTB and STB quality characteristics is introduced. The proposed method is evaluated by numerical examples. The results show the high ability of the suggested approach in selecting the appropriate profile, based on user policies.
https://sjie.journals.sharif.edu/article_5410_4ff6fa96179cdc2838e8d8ce64cd10a7.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
31
2.2
2016
02
20
MODELING AND SOLVING A THREE STAGE ASSEMBLY PRODUCTION SYSTEM FOR ON TIME DELIVERY
MODELING AND SOLVING A THREE STAGE ASSEMBLY PRODUCTION SYSTEM FOR ON TIME DELIVERY
121
129
5411
FA
S.M.H.
Hosseini
Dept. of Industrial Engineering & Management-Shahrood University
F.
Jolai
Department of Industrial Engineering-University of Tehran}
0000-0003-0824-8513
P.
Fattahi
Dept. of Industrial Engineering-Bu-Ali Sina University
Journal Article
2013
12
10
In this paper, the scheduling problem in a three stage assembly production system is studied. In this production system, there are two stages; to fabricate and provide the parts, and an assembly stage to assemble the parts and complete the product. Suppose that a number of products of different kinds are ordered to be produced, and each product needs a set of several parts to complete. At first, the parts are processed in the first stage with some parallel machines and, then, they are controlled and trimmed in the second stage. After completion of the set of parts for each product, the parts are assembled into the product in an assembly stage. The main assumptions are that the machines are uniform in the first stage. In addition, the assembly operation of a product will not start until all parts of its product are completed. Finally, if product A is going to be assembled before product B, then, at each stage, the processing of any part for product B does not start before the start of processing all parts for product A. The considered objective is to minimize the amount of earliness and tardiness of the products. At first, the parameters and decision variables are defined and, then, the mathematical model of the considered problem is presented. Since the considered problem is NP-hard, a heuristic algorithm is developed to solve the big sized problems. Finally, the performance of the proposed algorithm is evaluated in solving some different problems. Based on the proposed algorithm, the problem is solved in two phases: phase 1: to determine sequencing of the products, and phase 2: to determine the sequencing and scheduling of the parts for each product. In this evaluation, the result of the proposed algorithm that is compared with the optimum solutions obtained from solving their mathematical model is a small sized problem.
In this paper, the scheduling problem in a three stage assembly production system is studied. In this production system, there are two stages; to fabricate and provide the parts, and an assembly stage to assemble the parts and complete the product. Suppose that a number of products of different kinds are ordered to be produced, and each product needs a set of several parts to complete. At first, the parts are processed in the first stage with some parallel machines and, then, they are controlled and trimmed in the second stage. After completion of the set of parts for each product, the parts are assembled into the product in an assembly stage. The main assumptions are that the machines are uniform in the first stage. In addition, the assembly operation of a product will not start until all parts of its product are completed. Finally, if product A is going to be assembled before product B, then, at each stage, the processing of any part for product B does not start before the start of processing all parts for product A. The considered objective is to minimize the amount of earliness and tardiness of the products. At first, the parameters and decision variables are defined and, then, the mathematical model of the considered problem is presented. Since the considered problem is NP-hard, a heuristic algorithm is developed to solve the big sized problems. Finally, the performance of the proposed algorithm is evaluated in solving some different problems. Based on the proposed algorithm, the problem is solved in two phases: phase 1: to determine sequencing of the products, and phase 2: to determine the sequencing and scheduling of the parts for each product. In this evaluation, the result of the proposed algorithm that is compared with the optimum solutions obtained from solving their mathematical model is a small sized problem.
https://sjie.journals.sharif.edu/article_5411_3bc71faebe42e1639eb6fded38d714cd.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
31
2.2
2016
02
20
EXPLORING HIDDEN RELATIONS AMONG INDICES OF MULTIPLE DEPRIVATION USING A HYBRID APPROACH OF DATA MINING AND COMPLEX NETWORKS
EXPLORING HIDDEN RELATIONS AMONG INDICES OF MULTIPLE DEPRIVATION USING A HYBRID APPROACH OF DATA MINING AND COMPLEX NETWORKS
131
142
5423
FA
E.
Momeni
Dept. of Industrial Engineering\\nUniversity of Eyvanekey
E.
Akhondzade Noughabi
Dept. of Industrial Engineering-\nUniversity of Eyvanekey
B.
Minaei-Bidgoli
Dept. of Computer Science and Engineering-\nIran University of Science and Technolo??
Journal Article
2014
03
02
Overcoming poverty in less developed regions has particular importance for their main programs. Governments try to undertake actions in order to respond to the primary needs and deficiencies of their country by recognizing them. In this regard, this research endeavors to explore the correlation between deprivation indices and provision of results. Accordingly, a new hybrid approach towards using data mining tools and complex networks is presented that combines association of the rule mining technique and the structural concepts of complex networks. Firstly the hidden relations among the nine sub indices of deprivation are extracted using associated rule mining, and, after that, a new graph is constructed based on the extracted rules and analyzed to find the most effective and affected indices. The obtained results can be used in decision making in overcoming deprivation and for better funding of deprivation strategies in deprived areas.
Overcoming poverty in less developed regions has particular importance for their main programs. Governments try to undertake actions in order to respond to the primary needs and deficiencies of their country by recognizing them. In this regard, this research endeavors to explore the correlation between deprivation indices and provision of results. Accordingly, a new hybrid approach towards using data mining tools and complex networks is presented that combines association of the rule mining technique and the structural concepts of complex networks. Firstly the hidden relations among the nine sub indices of deprivation are extracted using associated rule mining, and, after that, a new graph is constructed based on the extracted rules and analyzed to find the most effective and affected indices. The obtained results can be used in decision making in overcoming deprivation and for better funding of deprivation strategies in deprived areas.
https://sjie.journals.sharif.edu/article_5423_7cc5ca26d6fbb6db2b134ef07cc68925.pdf