Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
28
2
2012
12
21
INVESTIGATING INFLUENCES OF ORGANIZATIONAL LEARNING CAPABILITY ON INNOVATION
INVESTIGATING INFLUENCES OF ORGANIZATIONAL LEARNING CAPABILITY ON INNOVATION
3
14
5216
FA
H.
Davarzani
Dept. of Industrial Engineering\r\nTarbiat Modares University
R.
Baradaran Kazemzadeh
Dept. of Industrial Engineering\r\nTarbiat Modares University
S.H.
Zegordi
Dept. of Industrial Engineering\r\nTarbiat Modares University
Journal Article
2010
01
10
Nowadays, innovation and creativity are main part of technology; important source of earning and competitive advantage. One of the basic initiatives of innovation is learning. On the other hand, based on organizational learning capability literature, the learning capability is the main block to learning and change. In this paper we have tried to identify the similarities between innovation and learning capability based on investigations in the literature. We have determined their relations, classified organizations by using cluster analysis based on their innovativeness and then illustrated each group via learning capability indicators. The main question in this study is: how do some practices in learning capability result in innovation? The employed methods of validity and reliability assessment in this paper are the evidence used to justify our proposed model. Most organizations admit to the importance of innovation and its vital role in company growth, existence and success. Different studies have been undertaken to investigate organizational and personal innovation to find out the factors affecting the facilitation and acceleration of the pace of innovation . Researchers have proposed different conceptual frameworks and structural processes in the literature to discuss how innovation is launched and developed in organizations and teams. A branch of these studies is devoted to the relation of organizational learning and innovation. If the significance of the relation of organizational learning and innovational activities is validated and justified, companies will be guided to employ the practices and basics of organizational learning to achieve and increase innovation. According to the published research, it is assumed that such a relation exists, and this paper tries to show how developing organizational learning capability provides organizations with the background necessary to start and advance the innovation process. Consequently, they can coordinate their activities with the requirements and expectations of the market. This research shows and discusses the justification of such a relation. Finally, we have shown how companies can employ building blocks of organizational learning capability to achieve organizational innovation.
Nowadays, innovation and creativity are main part of technology; important source of earning and competitive advantage. One of the basic initiatives of innovation is learning. On the other hand, based on organizational learning capability literature, the learning capability is the main block to learning and change. In this paper we have tried to identify the similarities between innovation and learning capability based on investigations in the literature. We have determined their relations, classified organizations by using cluster analysis based on their innovativeness and then illustrated each group via learning capability indicators. The main question in this study is: how do some practices in learning capability result in innovation? The employed methods of validity and reliability assessment in this paper are the evidence used to justify our proposed model. Most organizations admit to the importance of innovation and its vital role in company growth, existence and success. Different studies have been undertaken to investigate organizational and personal innovation to find out the factors affecting the facilitation and acceleration of the pace of innovation . Researchers have proposed different conceptual frameworks and structural processes in the literature to discuss how innovation is launched and developed in organizations and teams. A branch of these studies is devoted to the relation of organizational learning and innovation. If the significance of the relation of organizational learning and innovational activities is validated and justified, companies will be guided to employ the practices and basics of organizational learning to achieve and increase innovation. According to the published research, it is assumed that such a relation exists, and this paper tries to show how developing organizational learning capability provides organizations with the background necessary to start and advance the innovation process. Consequently, they can coordinate their activities with the requirements and expectations of the market. This research shows and discusses the justification of such a relation. Finally, we have shown how companies can employ building blocks of organizational learning capability to achieve organizational innovation.
https://sjie.journals.sharif.edu/article_5216_90542d43dbc1281219d41fc303af4ec6.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
28
2
2012
12
21
GENETIC AND MEMETIC ALGORITHMS FOR A FUZZY QUEUING MAXIMAL COVERING LOCATION-ALLOCATION MODEL FOR CONGESTED SYSTEMS WITH MULTIPLE DEMANDS
GENETIC AND MEMETIC ALGORITHMS FOR A FUZZY QUEUING MAXIMAL COVERING LOCATION-ALLOCATION MODEL FOR CONGESTED SYSTEMS WITH MULTIPLE DEMANDS
15
25
5217
FA
M.
Amiri
Dept. of Industrial Management\r\nAllameh Tabatabaei University
M.
Alipour
Dept. of Industrial Engineering\r\nUniversity of Science and Culture
M. H.
Farsangi
Dept. of Industrial Engineering\r\nUniversity of Science and Culture
Journal Article
2010
04
24
Customer satisfaction has always been one of the most important issues in public and private service centers. Management of these centers is more sensitive than production centers because the direct presence of customers when receiving services is the main feature of these service centers. In designing service centers, such as medical centers, fire fighting facilities, police stations, and so on, the location of service facilities and the allocation of service calls to servers, affect the congestion of demand. So, location and allocation of these services in an efficient mode, which has the capability of quality and time control, is inevitable. In this paper, we present a Fuzzy Queuing Maximal Covering Location-Allocation Model (FQMCLAM) for congested systems with several types of demand. Previously published papers, including fuzzy queuing maximal covering location-allocation problems, have only one type of demand in their congested systems. We extend a previously proposed model for a state in which there are several types of demand in the congested system. In this paper, a multi-objective integer programming model, in which each objective function represents the amount of covered populations for related demand, is presented. Furthermore, there are some other objective functions in the model that try to concentrate various types of server in a node to decrease the initial set up cost and help customers receive most of their services from a few different service nodes; saving travel time and costs. For example, in emergency service centers, it is efficient to concentrate several service centers in one node to decrease the initial set up cost. It is important to say that these objective functions are the same type of covered population objectives. This issue helps us to sum those objectives that simplify analysis of solution results. In addition, we assume a quality of service constraint in our model by considering service time or queue length in servers. The model is evaluated using two test problems. These test problems consists of 16 and 25 nodes, correspondingly, which are generated randomly, according to the literature. As mentioned in the literature, QMCLAM is a NP-Hard problem, so, our problem is also NP-Hard, and it is appropriate to develop heuristic or meta heuristic methods to solve the model within a reasonable time. Finally, we investigate genetic and memetic algorithms to solve the proposed model. Both are hybridized with a heuristic algorithm to solve this problem. Solution results show that both genetic and memetic algorithm solutions are appropriate in comparison with solutions achieved by a lingo solver, and also have lower run times. Also, we see that memetic algorithms obtain better solutions than genetic algorithms, even though run time is greater.
Customer satisfaction has always been one of the most important issues in public and private service centers. Management of these centers is more sensitive than production centers because the direct presence of customers when receiving services is the main feature of these service centers. In designing service centers, such as medical centers, fire fighting facilities, police stations, and so on, the location of service facilities and the allocation of service calls to servers, affect the congestion of demand. So, location and allocation of these services in an efficient mode, which has the capability of quality and time control, is inevitable. In this paper, we present a Fuzzy Queuing Maximal Covering Location-Allocation Model (FQMCLAM) for congested systems with several types of demand. Previously published papers, including fuzzy queuing maximal covering location-allocation problems, have only one type of demand in their congested systems. We extend a previously proposed model for a state in which there are several types of demand in the congested system. In this paper, a multi-objective integer programming model, in which each objective function represents the amount of covered populations for related demand, is presented. Furthermore, there are some other objective functions in the model that try to concentrate various types of server in a node to decrease the initial set up cost and help customers receive most of their services from a few different service nodes; saving travel time and costs. For example, in emergency service centers, it is efficient to concentrate several service centers in one node to decrease the initial set up cost. It is important to say that these objective functions are the same type of covered population objectives. This issue helps us to sum those objectives that simplify analysis of solution results. In addition, we assume a quality of service constraint in our model by considering service time or queue length in servers. The model is evaluated using two test problems. These test problems consists of 16 and 25 nodes, correspondingly, which are generated randomly, according to the literature. As mentioned in the literature, QMCLAM is a NP-Hard problem, so, our problem is also NP-Hard, and it is appropriate to develop heuristic or meta heuristic methods to solve the model within a reasonable time. Finally, we investigate genetic and memetic algorithms to solve the proposed model. Both are hybridized with a heuristic algorithm to solve this problem. Solution results show that both genetic and memetic algorithm solutions are appropriate in comparison with solutions achieved by a lingo solver, and also have lower run times. Also, we see that memetic algorithms obtain better solutions than genetic algorithms, even though run time is greater.
https://sjie.journals.sharif.edu/article_5217_cb2139863a66b9ec213beeb2aba007b7.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
28
2
2012
12
21
A VND META-HEURISTIC FOR AIRCRAFT LANDING PROBLEMS
A VND META-HEURISTIC FOR AIRCRAFT LANDING PROBLEMS
27
32
5218
FA
A.
Salehipour
School of Engineering\r\nIslamic azad University-garmsar branch
M.
Modarres
Dept. of Industrial Engineering\r\nSharif University of Technology
Journal Article
2010
05
31
Aircraft landing problems consist of determining an optimal schedule of landing aircraft on airport runways and also allocating arriving aircraft to available runways. The objective of the schedule is to minimize the total deviation of landing time from the target time (both earliness and tardiness). The problem is known to be NP-hard. Thus, it is very difficult to solve large instances of the problem in a reasonable time. Therefore, the major contribution of this paper is to derive optimal and near optimal solutions for problems with up to 200 aircrafts, by developing an efficient meta-heuristic. Although the computational complexity of the problem motivates heuristics, these routines have not been well studied. In this study, at first, a mixed integer goal programming formulation, with the objective of minimizing both earliness and tardiness, is developed. The model is solved using the Cplex solver on available instances. The results are promising and show that, except for the single runway problem with 100 aircraft, all problems with up to 100 aircraft can be solved optimally very quickly. Several multi-runway larger instances with up to 200 aircraft were also solved optimally, but, for the rest, the associated computational time of the solver grows exponentially. It is very difficult to solve those instances optimally, hence, a variable neighborhood descent (VND) meta-heuristic is developed such that initial solutions are randomly generated using a simple routine, and improved using four local searches. Computational results of the meta-heuristic show that the proposed algorithm can obtain optimal solutions for all small instances, as well as for several larger instances. Besides this, it is very fast, to the extent that the maximum spent time is about 1 minute. Comparing these results with previous studies reveals several important facts: 1) the proposed mathematical formulations solve larger instances, 2) for several instances, new best solutions were reported by the VND meta-heuristic, and 3) from a resource requirement point of view, the proposed VND competes well against state-of-the-art algorithms.
Aircraft landing problems consist of determining an optimal schedule of landing aircraft on airport runways and also allocating arriving aircraft to available runways. The objective of the schedule is to minimize the total deviation of landing time from the target time (both earliness and tardiness). The problem is known to be NP-hard. Thus, it is very difficult to solve large instances of the problem in a reasonable time. Therefore, the major contribution of this paper is to derive optimal and near optimal solutions for problems with up to 200 aircrafts, by developing an efficient meta-heuristic. Although the computational complexity of the problem motivates heuristics, these routines have not been well studied. In this study, at first, a mixed integer goal programming formulation, with the objective of minimizing both earliness and tardiness, is developed. The model is solved using the Cplex solver on available instances. The results are promising and show that, except for the single runway problem with 100 aircraft, all problems with up to 100 aircraft can be solved optimally very quickly. Several multi-runway larger instances with up to 200 aircraft were also solved optimally, but, for the rest, the associated computational time of the solver grows exponentially. It is very difficult to solve those instances optimally, hence, a variable neighborhood descent (VND) meta-heuristic is developed such that initial solutions are randomly generated using a simple routine, and improved using four local searches. Computational results of the meta-heuristic show that the proposed algorithm can obtain optimal solutions for all small instances, as well as for several larger instances. Besides this, it is very fast, to the extent that the maximum spent time is about 1 minute. Comparing these results with previous studies reveals several important facts: 1) the proposed mathematical formulations solve larger instances, 2) for several instances, new best solutions were reported by the VND meta-heuristic, and 3) from a resource requirement point of view, the proposed VND competes well against state-of-the-art algorithms.
https://sjie.journals.sharif.edu/article_5218_bc949de1501c3c0828e376b7f4c6d8f1.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
28
2
2012
12
21
PERFORMANCE EVALUATION IN FRACTAL ORGANIZATIONS (CASE STUDY: GAS TURBO-COMPRESSOR STATIONS)
PERFORMANCE EVALUATION IN FRACTAL ORGANIZATIONS (CASE STUDY: GAS TURBO-COMPRESSOR STATIONS)
33
41
5219
FA
L.
Roudak
Dept. of Industrial Engineering\r\nSharif University of Technology
A.
Eshraghniaye Jahromi
Dept. of Industrial Engineering\r\nSharif University of Technology
M.
Asadollahzadeh
Dept. of Industrial Engineering \r\nSharif University of Technology
Journal Article
2010
08
16
Since early classical theories presented in management, consistently, evaluation of organizations as a method of control and planning has been considered. Besides, due to the intensification of competition between organizations, it is very important to consider performance evaluation as an effective tool in this regard. Today, the wealth of a country is based on natural resources, hardware equipment and human resources, so, considering these parameters together with planning and sustainable development, can have considerable affects on the position of countries and also their value to each other. Increasing competition at a global level has shown that performance evaluation and productivity analysis is an important factor in this kind of study. So, many countries have attempted to introduce productivity and its measurement as the main part of performance evaluation. Several studies have shown that economic growth in different countries is dependent on the mentioned approach. Accurate performance evaluation shows a successful achievement in organizations and can be used as feedback in the decision making processes. By using performance evaluation, we can identify the weaknesses behind wasting resources, so that we can eliminate these weaknesses and help the organization to achieve their goals in improved economic ways . In this paper, a DEA/AHP approach has been proposed with a fractal structure for measuring the performance of organizations. Thus, regardless of other units, the first DEA model for evaluation of each pair of units is used, then, a matrix of pairwise comparisons is made and the Analytical Hierarchy Process (AHP) model for two-level ranking is used. One of the limitations of the DEA model is the separation of units into two groups; efficient units and inefficient units. So, the DEA model does not rank completely different units between each other. Also, one of the limitations of the AHP model is subjective judgment in the matrix of pairwise comparisons. Mixing these two methods eliminates the above limitations. It means that a combination of the two methods avoids subjective judgment and ranks the units completely . Finally, this model has been used for measuring the performance of gas turbo-compressor stations in district No.3. Because of the different locations of gas turbo-compressor stations and their importance in the gas transmission chain, it is very important for management to know their performance. Performance evaluation is able to show the degree of performance for each gas turbo-compressor station, based on the amount of budget and consideration of other resource consumption. As a result, we are able to have a sustainable and uniform system. Performance evaluation is rating facilities from the standpoint of efficiency, providing good management information to identify the strengths and weaknesses of each unit for strategic planning, developing appropriate methods to achieve higher goals and correct allocation of resources.
Since early classical theories presented in management, consistently, evaluation of organizations as a method of control and planning has been considered. Besides, due to the intensification of competition between organizations, it is very important to consider performance evaluation as an effective tool in this regard. Today, the wealth of a country is based on natural resources, hardware equipment and human resources, so, considering these parameters together with planning and sustainable development, can have considerable affects on the position of countries and also their value to each other. Increasing competition at a global level has shown that performance evaluation and productivity analysis is an important factor in this kind of study. So, many countries have attempted to introduce productivity and its measurement as the main part of performance evaluation. Several studies have shown that economic growth in different countries is dependent on the mentioned approach. Accurate performance evaluation shows a successful achievement in organizations and can be used as feedback in the decision making processes. By using performance evaluation, we can identify the weaknesses behind wasting resources, so that we can eliminate these weaknesses and help the organization to achieve their goals in improved economic ways . In this paper, a DEA/AHP approach has been proposed with a fractal structure for measuring the performance of organizations. Thus, regardless of other units, the first DEA model for evaluation of each pair of units is used, then, a matrix of pairwise comparisons is made and the Analytical Hierarchy Process (AHP) model for two-level ranking is used. One of the limitations of the DEA model is the separation of units into two groups; efficient units and inefficient units. So, the DEA model does not rank completely different units between each other. Also, one of the limitations of the AHP model is subjective judgment in the matrix of pairwise comparisons. Mixing these two methods eliminates the above limitations. It means that a combination of the two methods avoids subjective judgment and ranks the units completely . Finally, this model has been used for measuring the performance of gas turbo-compressor stations in district No.3. Because of the different locations of gas turbo-compressor stations and their importance in the gas transmission chain, it is very important for management to know their performance. Performance evaluation is able to show the degree of performance for each gas turbo-compressor station, based on the amount of budget and consideration of other resource consumption. As a result, we are able to have a sustainable and uniform system. Performance evaluation is rating facilities from the standpoint of efficiency, providing good management information to identify the strengths and weaknesses of each unit for strategic planning, developing appropriate methods to achieve higher goals and correct allocation of resources.
https://sjie.journals.sharif.edu/article_5219_6199ae52bb75c53d49c03bf01af22432.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
28
2
2012
12
21
A HYBRID INTELLIGENT SYSTEM BY COMBINING OPTIMIZED REGRESSION TREE AND SELF-ORGANIZING MAP FOR OPTIMAL CUSTOMER SEGMENTATION
A HYBRID INTELLIGENT SYSTEM BY COMBINING OPTIMIZED REGRESSION TREE AND SELF-ORGANIZING MAP FOR OPTIMAL CUSTOMER SEGMENTATION
43
54
5220
FA
A.R.
Soroush
Dept. of Industrial Engineering\r\nTarbiat Modares University
A.
Bahreininejad
Dept. of Industrial Engineering\r\nTarbiat Modares University
M.R.
Amin-Naseri
Dept. of Industrial Engineering\r\nTarbiat Modares University
Journal Article
2010
09
22
Customer segmentation (CS) is one of the most important aspects of customer relationship management. Machine learning (ML) algorithms for solving pattern recognition problems are often only successful if the available data are preprocessed based on appropriate feature selection. The feature selection process can be considered as a problem of global combinatorial optimization in ML. It can help to reduce the total number of features and to remove irrelevant and redundant data. Feature selection has received considerable attention in various areas where thousands of features are available. The main goal of feature selection is to identify a subset of features that are most informative, and, therefore, most predictive, for a given response variable. Finding an optimal feature subset is usually hard to control and many problems related to feature selection have been shown to be NP-hard. Successful implementation of feature selection not only provides important information for segmentation, but also reduces computational and analytical efforts for the analysis of high-dimensional data. Optimal segmentation based on related features can help to develop marketing strategies more accurately through spending resources effectively. However, the creation of a customer segmentation system (CSS) that has, simultaneously, both low complexity and optimal segmentation abilities, is a difficult task due to the large number of possible features. Although segmentation methods are popularly used, they cannot be useful unless irrelevant features are removed, because irrelevant features will present inappropriate CS and create poor results. Thus, the purpose of this paper is to present a hybrid intelligent CSS (HICSS) that is computationally efficient and optimal. At first, a pruned regression tree (PRT) is designed for optimal feature selection. However, performing appropriate feature selection is a hard job and there is no general applicable method available. Then, a self-organizing map (SOM) is developed to determine the optimal number of features based on the Davies-Bouldin Index. To measure the model, an insurance company dataset has been employed. The obtained results show that the PRT removes 93% of available features in this way, considerably reducing computation costs. In addition, the validation results show that the HICSS based on SOM has differentiated clusters very accurately. So, customers of the considered product have been segmented into 24 clusters and can simply spend marketing resources to attract similar customers to the best cluster customers.
Customer segmentation (CS) is one of the most important aspects of customer relationship management. Machine learning (ML) algorithms for solving pattern recognition problems are often only successful if the available data are preprocessed based on appropriate feature selection. The feature selection process can be considered as a problem of global combinatorial optimization in ML. It can help to reduce the total number of features and to remove irrelevant and redundant data. Feature selection has received considerable attention in various areas where thousands of features are available. The main goal of feature selection is to identify a subset of features that are most informative, and, therefore, most predictive, for a given response variable. Finding an optimal feature subset is usually hard to control and many problems related to feature selection have been shown to be NP-hard. Successful implementation of feature selection not only provides important information for segmentation, but also reduces computational and analytical efforts for the analysis of high-dimensional data. Optimal segmentation based on related features can help to develop marketing strategies more accurately through spending resources effectively. However, the creation of a customer segmentation system (CSS) that has, simultaneously, both low complexity and optimal segmentation abilities, is a difficult task due to the large number of possible features. Although segmentation methods are popularly used, they cannot be useful unless irrelevant features are removed, because irrelevant features will present inappropriate CS and create poor results. Thus, the purpose of this paper is to present a hybrid intelligent CSS (HICSS) that is computationally efficient and optimal. At first, a pruned regression tree (PRT) is designed for optimal feature selection. However, performing appropriate feature selection is a hard job and there is no general applicable method available. Then, a self-organizing map (SOM) is developed to determine the optimal number of features based on the Davies-Bouldin Index. To measure the model, an insurance company dataset has been employed. The obtained results show that the PRT removes 93% of available features in this way, considerably reducing computation costs. In addition, the validation results show that the HICSS based on SOM has differentiated clusters very accurately. So, customers of the considered product have been segmented into 24 clusters and can simply spend marketing resources to attract similar customers to the best cluster customers.
https://sjie.journals.sharif.edu/article_5220_513ca6b9746d5ec0e3cfc01260069017.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
28
2
2012
12
21
PRESENTING A NEW FUZZY MULTIOBJECTIVE MODEL FOR SUPPLIER SELECTION IN A SUPPLY CHAIN
PRESENTING A NEW FUZZY MULTIOBJECTIVE MODEL FOR SUPPLIER SELECTION IN A SUPPLY CHAIN
55
65
5221
FA
A.
Kazemi
Dept. of Industrial and Mechanical Engineering Qazvin Branch Islamic Azad University
M.
Deldar
Dept. of Industrial and Mechanical \r\nEngineering Qazvin Branch Islamic Azad University
Journal Article
2010
09
26
One of the main and most important elements of supply chain management is the appropriate selection of proper suppliers in a manner which goes with the long term objectives of an enterprise or organization. The supplier selection problem is a multiple criteria decision making problem that is affected by various factors which are mostly conflicting. In a majority of commercial activities, such as supplier selection, the importance or weights of objectives and constraints are not equal for decision makers, and the criteria should be weighted subject to the specific status and situations of the purchasing activity. Moreover, it is impossible to determine input data clearly and deterministically in the real world. In such cases, fuzzy sets theory is one of the best tools to face the vagueness and impreciseness. Regarding former studies, the quality of products and raw materials has been considered a key criterion in supplier selection for the buyer, with respect to other factors affecting the decision-making process. In addition to the necessity of maximizing the quality level, considering an acceptable minimum level for quality seems to be essential in modeling the problem. Regarding this subject in the form of a constraint, helps to satisfy this issue persistently. Taking the acceptable quality level constraint imprecisely and having a fuzzy form is one of the new concepts explored in this research. Besides, in the real world, sellers often suggest a delay period in payment of goods sold to encourage buyers. In such situations, the buyers are able to take advantage of receiving their requests and not paying the cost, while the sellers lose the interest of their earning during this period. From the viewpoint of a buyer who selects the suppliers, minimizing the present value of the cost supposed to be paid is essential to be considered as an objective function in modeling the multi objective problem supplier selection. For the first time in the literature, this research tries to develop existing models in this context, regarding the delay in obtaining the payments of buyers and formulating it in the form of a NPV function, as well as considering the fuzzy constraint of an acceptable quality level for each supplier. This function points to the present value of money and must be minimized because of its cost nature. Also, the model is solved utilizing multi objective programming techniques. Finally, the model is illustrated and solved in a numerical example.
One of the main and most important elements of supply chain management is the appropriate selection of proper suppliers in a manner which goes with the long term objectives of an enterprise or organization. The supplier selection problem is a multiple criteria decision making problem that is affected by various factors which are mostly conflicting. In a majority of commercial activities, such as supplier selection, the importance or weights of objectives and constraints are not equal for decision makers, and the criteria should be weighted subject to the specific status and situations of the purchasing activity. Moreover, it is impossible to determine input data clearly and deterministically in the real world. In such cases, fuzzy sets theory is one of the best tools to face the vagueness and impreciseness. Regarding former studies, the quality of products and raw materials has been considered a key criterion in supplier selection for the buyer, with respect to other factors affecting the decision-making process. In addition to the necessity of maximizing the quality level, considering an acceptable minimum level for quality seems to be essential in modeling the problem. Regarding this subject in the form of a constraint, helps to satisfy this issue persistently. Taking the acceptable quality level constraint imprecisely and having a fuzzy form is one of the new concepts explored in this research. Besides, in the real world, sellers often suggest a delay period in payment of goods sold to encourage buyers. In such situations, the buyers are able to take advantage of receiving their requests and not paying the cost, while the sellers lose the interest of their earning during this period. From the viewpoint of a buyer who selects the suppliers, minimizing the present value of the cost supposed to be paid is essential to be considered as an objective function in modeling the multi objective problem supplier selection. For the first time in the literature, this research tries to develop existing models in this context, regarding the delay in obtaining the payments of buyers and formulating it in the form of a NPV function, as well as considering the fuzzy constraint of an acceptable quality level for each supplier. This function points to the present value of money and must be minimized because of its cost nature. Also, the model is solved utilizing multi objective programming techniques. Finally, the model is illustrated and solved in a numerical example.
https://sjie.journals.sharif.edu/article_5221_867fac57e208a600e0d263f2fb522602.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
28
2
2012
12
21
title{DYNAMIC MODELING OF MUNICIPAL SOLID WASTE MANAGEMENT SYSTEMS FOR METROPOLITAN CITY OF TEHRAN
title{DYNAMIC MODELING OF MUNICIPAL SOLID WASTE MANAGEMENT SYSTEMS FOR METROPOLITAN CITY OF TEHRAN
67
77
5222
FA
M.A
. Afshar Kazemi
Dept. of Industrial Management\r\nTehran Center Branch,Islamic Azad University
L.
Eftekhar
Dept. of Industrial Management\r\nTehran Center Branch,Islamic Azad University
Journal Article
2010
06
27
Social systems are forming from interactive relationships between humans. Environmental pollutants, such as solid waste, are by-products of the different activities of these systems. Sound solid waste management is regarded as a problematic issue in this new era. Every day, a great volume of waste is generated in different forms, especially in metropolitan cities, which endangers the lives of mankind and other living creatures. Facing up to this problem in all countries, such as Iran, with a large volume and various compositions of municipal waste is very complicated and challengeable. The complexity of urban systems, including solid waste management systems and interactions and nonlinear relations between its elements, increases the difficulty of solving this problem. Although the solid waste management problem for Tehran city has been reviewed in different articles and studies from environmental and managerial aspects, modeling its system with a macro viewpoint in dynamic software is a novelty. In this research, the dynamic modeling of Tehran citys municipal solid waste management system has been performed based on literature reviews and system approaches. The dynamic simulation model of the system has been illustrated in Vensim PLE software with 20 level variables and 83 mathematical equations. Based on results from the simulation run, the behavior of key variables has been forecaste for a 14-year time horizon. After verifying the model by validation tests, such as structure verification, boundary adequacy, dimensional consistency and extreme conditions, it has been used to propose system policy building. After obtaining the valid model using policy and sensitivity analysis improvement, a scenario has been compiled. Finally, an improvement scenario for managing solid waste in Tehran city has been proposed; a motion according to a comprehensive waste management hierarchy pyramid based on parameters of an optimistic scenario.
Social systems are forming from interactive relationships between humans. Environmental pollutants, such as solid waste, are by-products of the different activities of these systems. Sound solid waste management is regarded as a problematic issue in this new era. Every day, a great volume of waste is generated in different forms, especially in metropolitan cities, which endangers the lives of mankind and other living creatures. Facing up to this problem in all countries, such as Iran, with a large volume and various compositions of municipal waste is very complicated and challengeable. The complexity of urban systems, including solid waste management systems and interactions and nonlinear relations between its elements, increases the difficulty of solving this problem. Although the solid waste management problem for Tehran city has been reviewed in different articles and studies from environmental and managerial aspects, modeling its system with a macro viewpoint in dynamic software is a novelty. In this research, the dynamic modeling of Tehran citys municipal solid waste management system has been performed based on literature reviews and system approaches. The dynamic simulation model of the system has been illustrated in Vensim PLE software with 20 level variables and 83 mathematical equations. Based on results from the simulation run, the behavior of key variables has been forecaste for a 14-year time horizon. After verifying the model by validation tests, such as structure verification, boundary adequacy, dimensional consistency and extreme conditions, it has been used to propose system policy building. After obtaining the valid model using policy and sensitivity analysis improvement, a scenario has been compiled. Finally, an improvement scenario for managing solid waste in Tehran city has been proposed; a motion according to a comprehensive waste management hierarchy pyramid based on parameters of an optimistic scenario.
https://sjie.journals.sharif.edu/article_5222_e81a5278a31eb153d53006a9cbdc3ea2.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
28
2
2012
12
21
OPTIMIZING OF THE RESPONSE VARIABLE IN AN ORDINAL REGRESSION MODEL USING HEURISTIC AND META-HEURISTIC ALGORITHMS (A CASE STUDY: STUDENTS SATISFACTION EVALUATI
OPTIMIZING OF THE RESPONSE VARIABLE IN AN ORDINAL REGRESSION MODEL USING HEURISTIC AND META-HEURISTIC ALGORITHMS (A CASE STUDY: STUDENTS SATISFACTION EVALUATI
79
92
5223
FA
M.
Bashiri
Dept. of Industrial Engineering\r\nUniversity of Shahed
R.
Kamranrad
Dept. of Industrial Engineering\r\nUniversity of Shahed
H.
Karimi
Dept. of Industrial Engineering\r\nUniversity of Shahed
Journal Article
2010
10
02
Discussion on the quality of services and products has been an important issue during the last decades and usually is measured by ordinal scales. The ordinal logistic regression model is a method which can analyze and predict ordinal variables. This method can be applied to lots of cases in biology, engineering, criminology and etc. It is difficult to collect all experimentation data, so the optimum situations are not completely clear and hard to be known. Hence, optimization of the response of an ordinal logistic regression model is necessary to find the best set of controllable factors. The purpose of the optimization is to find the component of predictable factors with the best possible probability for the best rank of the response. The best possible probability must have the maximum probability considering other ranks calculated probabilities. We proposed heuristic and meta-heuristic algorithms for this purpose. The proposed meta-heuristic algorithm for optimizing ordinal logistic regression is entitled; OL-SA. This algorithm is based on simulated annealing, which is a well known meta-heuristic method. Simulated annealing is a popular local search, which is applied to solve discrete and continuous optimization problems. This method can escape from the local solution like other meta-heuristic methods. The proposed meta-heuristic method is implemented in the case study of a university. Student satisfaction of computer site efficiency is evaluated, and the results are analyzed. Moreover, two numerical analyses have been performed for the proposed method. The first problem, with heuristic and meta-heuristic methods, is executed, and the results are compared with the exact solution. It is recognized that the innovative meta-heuristic approach has a higher efficiency and accuracy. So, in light of the second problem, the proposed meta-heuristic is used for optimal solutions (optimal values of the control variables) and the predicted probability of the responses that have been put in rank. This meta-heuristic could be applied to some practical situations, such as predicting earthquake resistance constructions at their verification stage by the designer.
Discussion on the quality of services and products has been an important issue during the last decades and usually is measured by ordinal scales. The ordinal logistic regression model is a method which can analyze and predict ordinal variables. This method can be applied to lots of cases in biology, engineering, criminology and etc. It is difficult to collect all experimentation data, so the optimum situations are not completely clear and hard to be known. Hence, optimization of the response of an ordinal logistic regression model is necessary to find the best set of controllable factors. The purpose of the optimization is to find the component of predictable factors with the best possible probability for the best rank of the response. The best possible probability must have the maximum probability considering other ranks calculated probabilities. We proposed heuristic and meta-heuristic algorithms for this purpose. The proposed meta-heuristic algorithm for optimizing ordinal logistic regression is entitled; OL-SA. This algorithm is based on simulated annealing, which is a well known meta-heuristic method. Simulated annealing is a popular local search, which is applied to solve discrete and continuous optimization problems. This method can escape from the local solution like other meta-heuristic methods. The proposed meta-heuristic method is implemented in the case study of a university. Student satisfaction of computer site efficiency is evaluated, and the results are analyzed. Moreover, two numerical analyses have been performed for the proposed method. The first problem, with heuristic and meta-heuristic methods, is executed, and the results are compared with the exact solution. It is recognized that the innovative meta-heuristic approach has a higher efficiency and accuracy. So, in light of the second problem, the proposed meta-heuristic is used for optimal solutions (optimal values of the control variables) and the predicted probability of the responses that have been put in rank. This meta-heuristic could be applied to some practical situations, such as predicting earthquake resistance constructions at their verification stage by the designer.
https://sjie.journals.sharif.edu/article_5223_9c7486b319978025302c67fefaeac1be.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
28
2
2012
12
21
PRESENTING A CONCEPTUAL MODEL FOR IMPLEMENTATION OF EXPERT SYSTEMS FOR PLANNING CONSTRUCTION PROJECTS AS A TOOL FOR KNOWLEDGE MANAGEMENT IN A PROJECT EXECUTOR OR
PRESENTING A CONCEPTUAL MODEL FOR IMPLEMENTATION OF EXPERT SYSTEMS FOR PLANNING CONSTRUCTION PROJECTS AS A TOOL FOR KNOWLEDGE MANAGEMENT IN A PROJECT EXECUTOR OR
93
102
5224
FA
A.A.
Yazdanpanah
Institute for Research & Planning in Higher Education،Ministry of Science, Resear?
M.
Hoseinali Pour
Dept. of Architecture and Urban \r\nPlanning Shahid Beheshti University
R.
Sadri
Dept. of Architecture and Urban \r\nPlanning Shahid Beheshti University
Journal Article
2010
05
02
The present research is the presentation of a model for implementation of expert systems for planning construction projects. The main idea of this study is that by using expert systems as a subsystem of the project knowledge management system, it could make these systems operational in planning organizations and also capable of saving and retrieving part of the planning knowledge of construction projects, in order for managers and planners of organizations to make decisions. The technical construction planning expert system model is a prototype model that can memorize and present construction project planning knowledge in three stages: feasibility, details, and workshop planning phases. According to the main goal of this research, designing the model has been done as a knowledge management sub-system in the project based organizations, considering the specifications of construction planning knowledge, which includes planning processes, planning rules, planning auxiliary documents and the technical know-how of construction stages of construction projects, in order to solve the problems of expert systems development. For this reason, the processes of expert system implementation in the organization have been prepared using knowledge management models. Due to expert system model development, it has been amended and completed using specialties tailored by the special features of construction planning companies and construction planning experts of the case study company, and also by considering the opinions of planning experts in the NOSAZAN Company; one of the greatest leading companies of state construction management. To prepare this model, first, the planning process, knowledge titles and information fields needed for executing each knowledge component process are identified. Then, these components are classified into six groups of knowledge categories. In the next step, appropriate systems are specified for each category. With a combination of these systems, the final technical model of the construction planning expert system is designed. By methodologies obtained from valid models and results extracted from questionnaires, the expert system implementation model was evaluated and recommended .
The present research is the presentation of a model for implementation of expert systems for planning construction projects. The main idea of this study is that by using expert systems as a subsystem of the project knowledge management system, it could make these systems operational in planning organizations and also capable of saving and retrieving part of the planning knowledge of construction projects, in order for managers and planners of organizations to make decisions. The technical construction planning expert system model is a prototype model that can memorize and present construction project planning knowledge in three stages: feasibility, details, and workshop planning phases. According to the main goal of this research, designing the model has been done as a knowledge management sub-system in the project based organizations, considering the specifications of construction planning knowledge, which includes planning processes, planning rules, planning auxiliary documents and the technical know-how of construction stages of construction projects, in order to solve the problems of expert systems development. For this reason, the processes of expert system implementation in the organization have been prepared using knowledge management models. Due to expert system model development, it has been amended and completed using specialties tailored by the special features of construction planning companies and construction planning experts of the case study company, and also by considering the opinions of planning experts in the NOSAZAN Company; one of the greatest leading companies of state construction management. To prepare this model, first, the planning process, knowledge titles and information fields needed for executing each knowledge component process are identified. Then, these components are classified into six groups of knowledge categories. In the next step, appropriate systems are specified for each category. With a combination of these systems, the final technical model of the construction planning expert system is designed. By methodologies obtained from valid models and results extracted from questionnaires, the expert system implementation model was evaluated and recommended .
https://sjie.journals.sharif.edu/article_5224_3814eb29391f3367fd6038bd5f064f59.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
28
2
2012
12
21
SOLVING TIME COST AND QUALITY TRADE OFF PROJECT PROBLEM USING MULTI OBJECTIVE STOCHASTIC PROGRAMMING
SOLVING TIME COST AND QUALITY TRADE OFF PROJECT PROBLEM USING MULTI OBJECTIVE STOCHASTIC PROGRAMMING
103
111
5225
FA
E.
Mehdizadeh
Dept. of Industrial Engineering\r\nQazvin Branch,Islamic Azad University
O.
Mohsenian
Dept. of Industrial Engineering\r\nQazvin Branch,Islamic Azad University
Journal Article
2010
04
24
The crashing of project times is not a new topic in the project management area and its history returns to CPM and PERT network development. New views and methods have been presented about the time-cost trade off in recent years. Generally, the completion time of projects could be shortened by reviewing project network logic or crashing the activities through expending more cost. Note that any reduction in the duration of activities could affect their quality. In other words, crashing could decrease the closeness rate of project deliverables from the employer or customer expectation levels. Ambiguity is an unavoidable factor in most activity duration estimates, and uncertainty is another problem that always goes along with project scheduling. By reviewing the literature, it was found that researchers often try to optimize a specific objective function, while, in real world problems, a project might have several objectives simultaneously. In this paper, we try to study the project scheduling problem in a multi-objective stochastic state. On the other hand, it might be an unpredictable lag that occurs for each activity which affects successor scheduling and the delivery date of the project, finally. This fact increases the importance of scheduling lags and the necessity of compensation or their avoidance. When project managers decide to shorten activities by expending cost, this problem becomes more important. So, it is necessary to consider the stochastic lags of each activity, which might occur after crashing, in project scheduling and the time-cost trade off problem. Since the duration time of these lags is not determined, we can consider them stochastic. Hence, applying stochastic objective functions in these problems is another innovation of this study, which could help to realize better than ever results . In this paper, the time-cost trade off problem, with quality (TCQTP) as a key element of the projects, is discussed, while crashing with stochastic lags is occurred in activities. First, a stochastic time-cost-quality model has been developed, and then, we use Chance Constrained Compromise Programming (CCCP) to solve it. Eventually, the ability of the proposed model is illustrated through a real case study in a Liquid Gas Tank Establishment (LGTE) project. The results demonstrate the applicability of our model in real world problems.
The crashing of project times is not a new topic in the project management area and its history returns to CPM and PERT network development. New views and methods have been presented about the time-cost trade off in recent years. Generally, the completion time of projects could be shortened by reviewing project network logic or crashing the activities through expending more cost. Note that any reduction in the duration of activities could affect their quality. In other words, crashing could decrease the closeness rate of project deliverables from the employer or customer expectation levels. Ambiguity is an unavoidable factor in most activity duration estimates, and uncertainty is another problem that always goes along with project scheduling. By reviewing the literature, it was found that researchers often try to optimize a specific objective function, while, in real world problems, a project might have several objectives simultaneously. In this paper, we try to study the project scheduling problem in a multi-objective stochastic state. On the other hand, it might be an unpredictable lag that occurs for each activity which affects successor scheduling and the delivery date of the project, finally. This fact increases the importance of scheduling lags and the necessity of compensation or their avoidance. When project managers decide to shorten activities by expending cost, this problem becomes more important. So, it is necessary to consider the stochastic lags of each activity, which might occur after crashing, in project scheduling and the time-cost trade off problem. Since the duration time of these lags is not determined, we can consider them stochastic. Hence, applying stochastic objective functions in these problems is another innovation of this study, which could help to realize better than ever results . In this paper, the time-cost trade off problem, with quality (TCQTP) as a key element of the projects, is discussed, while crashing with stochastic lags is occurred in activities. First, a stochastic time-cost-quality model has been developed, and then, we use Chance Constrained Compromise Programming (CCCP) to solve it. Eventually, the ability of the proposed model is illustrated through a real case study in a Liquid Gas Tank Establishment (LGTE) project. The results demonstrate the applicability of our model in real world problems.
https://sjie.journals.sharif.edu/article_5225_13346971f38add4f385fd63c7e50019d.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
28
2
2012
12
21
RISK ANALYSIS OF TRAFFIC CONFINED CONTROL SYSTEM BASED ON RFID
RISK ANALYSIS OF TRAFFIC CONFINED CONTROL SYSTEM BASED ON RFID
113
130
5226
FA
B.
Lak
Faculy of Science Administrative and Support Police University
N.
Nahavandi
Dept. of Industrial Engineering\r\nTarbiat Modares University
Journal Article
2010
05
26
The paper analyzes the possibilities of using a relative new technology, RFID, in an information system. The main features of this technology, the components of the system, the way the system functions and the stages of the implementing process are also presented. One of the main uses of RFID technology is the Traffic Confined Control System based on RFID. Development of this system has a large amount of risk. Reviewing the related literature, the authors did not find any suitable framework for identification and classification of IT project risks in Iran, and no research concerning identification of Traffic Confined Control System risks has so far been carried out. In this paper, in order to analyze the risks of a vehicle movement control system based on RFID, research is carried out in two stages: In the first step, in addition to introducing the Risk Breakdown Structure (RBS) as the best method for identification of IT project risk, a framework is proposed for classification of current risks in the Traffic Confine Control System, based on RFID, and then, a list of current risks is provided that can be used in all IT projects. At this stage, the approach of the research is qualitative, and the data was collected by means of interviews, focus groups and library studies. The validation of results is also reported. At the second stage, identified risks of the noun system are analyzed by qualitative approaches. The approach of this stage is quantitative and the data is collected using questionnaires. The values of the parameters probability and impact are identified for the risks. In addition, qualitative and quantitative risk analysis was extracted. Data analysis will provide the final list of top ten risks and five important resources, so that they can be addressed in other risk management cycles. The research results show that dysfunction of the supply chain needed and resource management (partial resources) are considered the most important risks and the most significant sources, respectively, which should be taken into consideration by the IT manager.
The paper analyzes the possibilities of using a relative new technology, RFID, in an information system. The main features of this technology, the components of the system, the way the system functions and the stages of the implementing process are also presented. One of the main uses of RFID technology is the Traffic Confined Control System based on RFID. Development of this system has a large amount of risk. Reviewing the related literature, the authors did not find any suitable framework for identification and classification of IT project risks in Iran, and no research concerning identification of Traffic Confined Control System risks has so far been carried out. In this paper, in order to analyze the risks of a vehicle movement control system based on RFID, research is carried out in two stages: In the first step, in addition to introducing the Risk Breakdown Structure (RBS) as the best method for identification of IT project risk, a framework is proposed for classification of current risks in the Traffic Confine Control System, based on RFID, and then, a list of current risks is provided that can be used in all IT projects. At this stage, the approach of the research is qualitative, and the data was collected by means of interviews, focus groups and library studies. The validation of results is also reported. At the second stage, identified risks of the noun system are analyzed by qualitative approaches. The approach of this stage is quantitative and the data is collected using questionnaires. The values of the parameters probability and impact are identified for the risks. In addition, qualitative and quantitative risk analysis was extracted. Data analysis will provide the final list of top ten risks and five important resources, so that they can be addressed in other risk management cycles. The research results show that dysfunction of the supply chain needed and resource management (partial resources) are considered the most important risks and the most significant sources, respectively, which should be taken into consideration by the IT manager.
https://sjie.journals.sharif.edu/article_5226_d07cc0e1121c2e879de11f63f4747035.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
28
2
2012
12
21
APPLICATION OF RCM TO ENHANCE THE EFFICIENCY AND EFFECTIVENESS OF MAINTENANCE PROCESS (CASE STUDY: OIL AND GAS PRODUCTION, SOUTH ZAGROS COMPANY OF IRAN
APPLICATION OF RCM TO ENHANCE THE EFFICIENCY AND EFFECTIVENESS OF MAINTENANCE PROCESS (CASE STUDY: OIL AND GAS PRODUCTION, SOUTH ZAGROS COMPANY OF IRAN
133
140
5227
FA
H.
Rashedi
Dept. of Industrial Engineering\r\nYazd University, Yazd, IRAN
M.S.
Owlia
Dept. of Industrial Engineering\r\nYazd University, Yazd, IRAN
H.
Hoseininasab
Dept. of Industrial Engineering\r\nYazd University, Yazd, IRAN
Y.
Zaremehrjerdi
Dept. of Industrial Engineering\r\nYazd University, Yazd, IRAN
Journal Article
2010
04
17
Although Reliability Centered Maintenance (RCM) has been successfully implemented in many industries in developed countries, the same record is not clear when it comes to developing countries, like Iran. The excessive specialist tendency to focus on technical aspects, and the negligence of basic principles to maintain available infrastructures, is found to be a significant factor hindering the effective implementation of RCM. This paper reports on a project in an oil and gas company using three key objectives of RCM, i.e. improving the reliability of the system, reducing the consequences of the failures, and minimizing maintenance costs. A variety of techniques was used in an integrated approach. In line with the first principle, the FMEA technique was applied as a sound quantitative evaluation of all critical functions. It helps us to allocate necessary recourses to the most critical functions and equipment. Then, it is necessary to distinguish the possible failures of the equipment to specify their consequences. At this point, the cause and effect diagram was applied concerning the second principle of RCM; to prevent or mitigate the consequences of failures, not to prevent the failures themselves. At the final step, and in line with the third RCM principle, a simulation model was developed to check how it would be possible to reduce maintenance costs by avoiding or removing maintenance actions that are not strictly necessary. The model was applied to a chemical injection pump with a series of operational failures. Failures were characterized by an empirically derived function via the Weibull distribution. Furthermore, the impact of preventive maintenance time cycles was tested in the simulated model to improve system availability, combined with cost benefit analysis. The model was implemented in the case of the South Zagros Oil and Gas Production Company to evaluate performance capabilities. The final results indicated a simultaneous increase in effectiveness and efficiency in the maintenance process through maximizing reliability indicators .
Although Reliability Centered Maintenance (RCM) has been successfully implemented in many industries in developed countries, the same record is not clear when it comes to developing countries, like Iran. The excessive specialist tendency to focus on technical aspects, and the negligence of basic principles to maintain available infrastructures, is found to be a significant factor hindering the effective implementation of RCM. This paper reports on a project in an oil and gas company using three key objectives of RCM, i.e. improving the reliability of the system, reducing the consequences of the failures, and minimizing maintenance costs. A variety of techniques was used in an integrated approach. In line with the first principle, the FMEA technique was applied as a sound quantitative evaluation of all critical functions. It helps us to allocate necessary recourses to the most critical functions and equipment. Then, it is necessary to distinguish the possible failures of the equipment to specify their consequences. At this point, the cause and effect diagram was applied concerning the second principle of RCM; to prevent or mitigate the consequences of failures, not to prevent the failures themselves. At the final step, and in line with the third RCM principle, a simulation model was developed to check how it would be possible to reduce maintenance costs by avoiding or removing maintenance actions that are not strictly necessary. The model was applied to a chemical injection pump with a series of operational failures. Failures were characterized by an empirically derived function via the Weibull distribution. Furthermore, the impact of preventive maintenance time cycles was tested in the simulated model to improve system availability, combined with cost benefit analysis. The model was implemented in the case of the South Zagros Oil and Gas Production Company to evaluate performance capabilities. The final results indicated a simultaneous increase in effectiveness and efficiency in the maintenance process through maximizing reliability indicators .
https://sjie.journals.sharif.edu/article_5227_5301422bf304458b7e39b6c7a131a61f.pdf
Sharif University of Technology
Sharif Journal of Industrial Engineering & Management
2676-4741
28
2
2012
12
21
DEVELOPMENT OF A STRUCTURAL MODEL FOR THE PERFORMANCE OF AIRLINE AGENCY SYSTEMS USING QUEUING THEORY
DEVELOPMENT OF A STRUCTURAL MODEL FOR THE PERFORMANCE OF AIRLINE AGENCY SYSTEMS USING QUEUING THEORY
141
146
5228
FA
S.M.T.
Fatemi Ghomi
Dept. of Industrial Engineerin\r\nAmirkabir University of Technology
M.
Asadi-Bakloi
Dept. of Industrial Engineerin\r\nAmirkabir University of echnology
Journal Article
2011
01
17
Many models have been proposed to develop airline companies and to schedule their flight lines. To develop these models, various approaches, such as mathematical planning, descriptive and heuristic methods, and a simulation approach, have been applied, with respect to the queuing theory. In this paper, using the queuing theory approach, a model of the queue simulation system has been presented to determine the capacity of airline companies. The model has been developed using SLAM software and it has been used to analyze an airline company. The results gained have been compared with those of Jacksons open network model, thus, the efficiency and validity of the model have been confirmed. Using the results of this model, the percentage of time the station is busy, the average number of passengers on the waiting list and the average time the passengers spend on the waiting list can be calculated. In comparison with the results gained from simulation by SLAM software, it is observed that the results of the proposed model and those of the open network of Jackson are very close to each other. The reason for the minor difference is in the number of simulation runs. As this number becomes higher, the Jackson model will have less difference with simulation. The modeling of capacity determination is the process of forecasting the measure of effectiveness, the volume of cargo in a system, and, also, designing an optimal method to perform this process. Capacity designing is a kind of process for forecasting the saturation time as much as possible and applying the most economical way for postponing congestion in the system. The process should include the capacity of the volume of work resulting from present and new plans and desirable service levels. Our goal is forecasting the response time for a situation in which many passengers have been requested simultaneously. In this paper, research has been done on the basis of a real and practical case, which is the agent of an airline company for selling tickets, and the results have been generalized to determine capacity using techniques of queuing and simulation models. This has facilitated reaching the goal. The information that is necessary to determine capacity in airline companies is divided into two internal and external environmental factors . The procedure used in this paper is based on regular and common methods of research, in such a way that, after achieving the goals and for warming up, a review of the background is given first. To consider and describe the queuing system, a travel agency, called Serat, has been selected as the case study. The process of system stages is then introduced and an analysis of it is given. Considering simplifying assumptions, a general queuing model of this agency is proposed .
Many models have been proposed to develop airline companies and to schedule their flight lines. To develop these models, various approaches, such as mathematical planning, descriptive and heuristic methods, and a simulation approach, have been applied, with respect to the queuing theory. In this paper, using the queuing theory approach, a model of the queue simulation system has been presented to determine the capacity of airline companies. The model has been developed using SLAM software and it has been used to analyze an airline company. The results gained have been compared with those of Jacksons open network model, thus, the efficiency and validity of the model have been confirmed. Using the results of this model, the percentage of time the station is busy, the average number of passengers on the waiting list and the average time the passengers spend on the waiting list can be calculated. In comparison with the results gained from simulation by SLAM software, it is observed that the results of the proposed model and those of the open network of Jackson are very close to each other. The reason for the minor difference is in the number of simulation runs. As this number becomes higher, the Jackson model will have less difference with simulation. The modeling of capacity determination is the process of forecasting the measure of effectiveness, the volume of cargo in a system, and, also, designing an optimal method to perform this process. Capacity designing is a kind of process for forecasting the saturation time as much as possible and applying the most economical way for postponing congestion in the system. The process should include the capacity of the volume of work resulting from present and new plans and desirable service levels. Our goal is forecasting the response time for a situation in which many passengers have been requested simultaneously. In this paper, research has been done on the basis of a real and practical case, which is the agent of an airline company for selling tickets, and the results have been generalized to determine capacity using techniques of queuing and simulation models. This has facilitated reaching the goal. The information that is necessary to determine capacity in airline companies is divided into two internal and external environmental factors . The procedure used in this paper is based on regular and common methods of research, in such a way that, after achieving the goals and for warming up, a review of the background is given first. To consider and describe the queuing system, a travel agency, called Serat, has been selected as the case study. The process of system stages is then introduced and an analysis of it is given. Considering simplifying assumptions, a general queuing model of this agency is proposed .
https://sjie.journals.sharif.edu/article_5228_6be6b295d46ad2540644e4dcfef68ed2.pdf