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http://ijiepr.iust.ac.ir/browse.php?mag_id=3&slc_lang=en&sid=1
International Journal of Industrial Engineering & Production Research
IJIEPR
2008-4889
2345-363X
10.22068/ijiepr
2008
19
1
NORMAL 6-VALENT CLAYEY GRAPHS OF ABELIAN GROUPS
Mehdi
Alaeiyan
alaeiyan@iust.ac.ir
We call a Clayey graph Γ = Cay(G, S) normal for G, if the right regular representation R(G) of G is normal in the full automorphism group of Aunt(Γ). in this paper, we give a classification of all non-normal Clayey graphs of finite abelian group with valency 6.
Clayey graph
normal Clayey graph
automorphism group
2008
3
01
1
11
International Journal of Industrial Engineering & Production Research
IJIEPR
2008-4889
2345-363X
10.22068/ijiepr
2008
19
1
MAXIMAL INDEPENDENT SETS FOR THE PIXEL EXPANSION OF GRAPH ACCESS STRUCTURE
Massoud
Hadian Dehkordi
Cheraghi
Cheraghi
a_cheraghi@iust.ac.ir
Given a graph G, a visual cryptography scheme based on the graph G is a method to distribute a secret image among the vertices of G, the participants, so that a subset of participants can recover the secret image if they contain an edge of G, by stacking their shares, otherwise they can obtain no information regarding the secret image. In this paper we apply maximal independent sets of the graph G to propose a lower bound on the pixel expansion of visual cryptography schemes with graph access structure (G), moreover we present a the lower bound on the pixel expansion of basis matrices C5 and Peterson graph access structure
Sharing Schemes
Visual Cryptography
Graph Access Structure
2008
3
01
13
16
International Journal of Industrial Engineering & Production Research
IJIEPR
2008-4889
2345-363X
10.22068/ijiepr
2008
19
1
SEMI-RADICALS OF SUB MODULES IN MODULES
Hamid
Tavallaee
tavallaee@iust.ac.ir,
Rezvan
Hamid A. Tavallaee and Rezvan. Varmazyar
varmazyar@ iust.ac.ir
Let be a commutative ring and be a unitary module. We define a semi prime sub module of a module and consider various properties of it. Also we define semi-radical of a sub module of a module and give a number of its properties. We define modules which satisfy the semi-radical formula and present the existence of such a module.
Prime sub module
semi prime sub module
radical and semi- radical of a module
modules satisfying the semi-radical formula
2008
3
01
21
27
International Journal of Industrial Engineering & Production Research
IJIEPR
2008-4889
2345-363X
10.22068/ijiepr
2008
19
1
IMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL
Rahman
Farnoosh
Behnam
Zarpak
zarpak@iust.ac.ir
Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, we introduce a new numerically method of finding maximum a posterior estimation by using EM-algorithm and Gaussians mixture distribution. In this algorithm, we have made a sequence of priors, posteriors and they converge to a posterior probability that is called the reference posterior probability. Maximum a posterior estimated can determine by the reference posterior probability that will make labeled image. This labeled image shows our segmented image with reduced noises. We show this method in several experiments.
Bayesian Rule
Gaussian Mixture Model (GMM)
Maximum a Posterior (MAP)
Expectation- Maximization (EM) Algorithm
Reference Analysis.
2008
3
01
29
32
International Journal of Industrial Engineering & Production Research
IJIEPR
2008-4889
2345-363X
10.22068/ijiepr
2008
19
1
Image Segmentation using Gaussian Mixture Model
R.
Farnoosh
B.
Zarpak
zarpak@iust.ac.ir
Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact, a new numerically method was introduced for finding the maximum a posterior estimation by using EM-algorithm and Gaussians mixture distribution. In this algorithm, we were made a sequence of priors, posteriors were made and then converged to a posterior probability that is called the reference posterior probability. Maximum a posterior estimated can determine by the reference posterior probability which can make labeled image. This labeled image shows our segmented image with reduced noises. We presented this method in several experiments.
Keywords : Bayesian Rule
Gaussian Mixture Model (GMM)
Maximum a Posterior (MAP)
Expectation- Maximization (EM) Algorithm
Reference Analysis
2008
3
01
29
32
International Journal of Industrial Engineering & Production Research
IJIEPR
2008-4889
2345-363X
10.22068/ijiepr
2008
19
1
A Threshold Accepting Algorithm for Partitioning Machines in a Tandem Automated Guided Vehicle
R.
Tavakkoli-Moghaddam
tavakoli@ut.ac.ir
M.
Aryanezhad
H.
Kazemipoor
hkazemipoor@yahoo.com
A.
Salehipour
Abstract : A tandem automated guided vehicle (AGV) system deals with grouping workstations into some non-overlapping zones , and assigning exactly one AGV to each zone. This paper presents a new non-linear integer mathematical model to group n machines into N loops that minimizes both inter and intra-loop flows simultaneously. Due to computational difficulties of exact methods in solving our proposed model, a threshold accepting (TA) algorithm is proposed. To show its efficiency, a number of instances generated randomly are solved by this proposed TA and then compared with the LINGO solver package employing the branch-and-bound (B/B) method. The related computational results show that our proposed TA dominates the exact algorithm when the size of instances grows.
Keywords : Tandem AGV
Machine Grouping
Mathematical Model
Threshold Accepting Algorithm.
2008
3
01
33
42
International Journal of Industrial Engineering & Production Research
IJIEPR
2008-4889
2345-363X
10.22068/ijiepr
2008
19
1
TOWARD EVOLUTIONARY INNOVATION THEORY
.A.
Seifoddin
seifd@tco.ir
M. H.
Salimi
M. M.
Syed Esfahani
Innovations, commercialized by new or old established firms, located at the core of industrial renewal process. The innovation concept has suffered transformations, along with the evolution of the models that try to explain and understand the innovation process. The innovative process corresponds to all activities that generate technological changes and the dynamic interaction between them, not necessarily being novelties. Linier model, Chain-Linked Model and National Innovation Systems (NIS) Approach, are three models that have developed for innovation process. Innovation process can be viewed as evolutionary process. One can recognize some mechanism for innovation evolution. These are grouped into two classes those that increase configurations variation and those that decrease it. Emergence of knowledge, knowledge flow and recombination are the mechanism to increase variation of configuration. Internal and external selections are the mechanism to selecting. Innovation operators are evolutionary operators that create new combinations of configuration and increase variation. This paper develops an evolutionary cycle in innovation process and extends evolutionary mechanisms of innovation.
innovation
evolutionary theory of innovation
innovation presses
evolution
selection
fitness
variation
innovation-operators
2008
3
01
43
55
International Journal of Industrial Engineering & Production Research
IJIEPR
2008-4889
2345-363X
10.22068/ijiepr
2008
19
1
Toward Evolutionary Innovation Theory
A.
Seifoddin
seifd@tco.ir
H.A.
Salimi
A.
Seyed Esfahani
Abstract: Innovations, commercialized by new or old established firms, located at the core of industrial renewal process. The innovation concept has suffered transformations, along with the evolution of the models that try to explain and understand the innovation process. The innovative process corresponds to all activities that generate technological changes and the dynamic interaction between them, not necessarily being novelties. Linier model, Chain-Linked Model and National Innovation Systems (NIS) Approach, are three models that have developed for innovation process. Innovation process can be viewed as evolutionary process. One can recognize some mechanism for innovation evolution. These are grouped into two classes those that increase configurations variation and those that decrease it. Emergence of knowledge, knowledge flow and recombination are the mechanism to increase variation of configuration. Internal and external selections are the mechanism to selecting. Innovation operators are evolutionary operators that create new combinations of configuration and increase variation. This paper develops an evolutionary cycle in innovation process and extends evolutionary mechanisms of innovation.
Keywords: innovation
evolutionary theory of innovation
innovation presses
evolution
selection
fitness
variation
innovation-operators
2008
3
01
43
55
International Journal of Industrial Engineering & Production Research
IJIEPR
2008-4889
2345-363X
10.22068/ijiepr
2008
19
1
NEW CONCEPT IN LEANNESS DEVELOPMENT AND ASSESSMENT IN PLANT LIFE CYCLE (PLC)
Farzad R
Sanati
Seyed M.
Seyed Hosseini
seyedhosseini@iust.ac.ir
At the last decade of the 20th century, Womack et. Al introduced Lean concept to the industrial world. Since 1990 up to now, existed studies mostly have focused on lean production in the step of manufacturing, but in this research leanness concept has developed in the plant life cycle. In this paper leanness concept will be described as elimination of wastes in the phases of investment, plant design & construction(hardware), organization & systems design (software) and these three steps will be added to, elimination of previously described seven wastes in production step. For this purpose at first, the types of wastes in the above mentioned phases are defined by using Axiomatic Design methodology. After defining the types of wastes, a model for assessment of leanness is submitted. In this quantitative model, amount of leanness in each phase will be determined and combined to make a unique measure for total leanness. Dimensions of leanness are shown for quick understanding, by using a spider diagram. In the last section of the paper, the results of an example of the application of this model in fan industry are brought. This example shows the simplicity and powerfully of the model to determine the leanness in before production phases. © 2008 Authors all rights reserved.
Leanness
Plant Life Cycle
Assessment
Lean Investment
Lean Manufacturing
2008
3
01
57
65
International Journal of Industrial Engineering & Production Research
IJIEPR
2008-4889
2345-363X
10.22068/ijiepr
2008
19
1
New Concept in Leanness Development and Assessment in Plant Life Cycle (PLC)
F.
Sanati
S.M.
Seyedhoseini
seyedhoseini@yahoo.com
Abstract: At the last decade of the 20th century, Womack et. Al introduced Lean concept to the industrial world. Since 1990 up to now, existed studies mostly have focused on lean production in the step of manufacturing, but in this research leanness concept has developed in the plant life cycle. In this paper leanness concept will be described as elimination of wastes in the phases of investment, plant design & construction(hardware), organization & systems design (software) and these three steps will be added to, elimination of previously described seven wastes in production step. For this purpose at first, the types of wastes in the above mentioned phases are defined by using Axiomatic Design methodology. After defining the types of wastes, a model for assessment of leanness is submitted. In this quantitative model, amount of leanness in each phase will be determined and combined to make a unique measure for total leanness. Dimensions of leanness are shown for quick understanding, by using a spider diagram. In the last section of the paper, the results of an example of the application of this model in fan industry are brought. This example shows the simplicity and powerfully of the model to determine the leanness in before production phases. © 2008 Authors all rights reserved.
Keywords: Leanness
Plant Life Cycle
Assessment
Lean Investment
Lean Manufacturing
2008
3
01
57
65
International Journal of Industrial Engineering & Production Research
IJIEPR
2008-4889
2345-363X
10.22068/ijiepr
2008
19
1
A BI-LEVEL LINEAR MULTI-OBJECTIVE DECISION MAKING MODEL WITH INTERVAL COEFFICIENTS FOR SUPPLY CHAIN COORDINATION
M.B.
Aryanezhad
E.
M.B.Aryanezhad & E.Roghanian
E-roghanian@iustarak.ac.ir
Bi-level programming, a tool for modeling decentralized decisions, consists of the objective(s) of the leader at its first level and that is of the follower at the second level. Three level programming results when second level is itself a bi-level programming. By extending this idea it is possible to define multi-level programs with any number of levels. Supply chain planning problems are concerned with synchronizing and optimizing multiple activities involved in the enterprise, from the start of the process, such as procurement of the raw materials, through a series of process operations, to the end, such as distribution of the final product to customers. Enterprise-wide supply chain planning problems naturally exhibit a multi-level decision network structure, where for example, one level may correspond to a local plant control/scheduling/planning problem and another level to a corresponding plant-wide planning/network problem. Such a multi-level decision network structure can be mathematically represented by using “multi-level programming” principles. This paper studies a “bi-level linear multi-objective decision making” model in with “interval” parameters and presents a solution method for solving it this method uses the concepts of tolerance membership function and multi-objective multi-level optimization when all parameters are imprecise and interval .
Multi-level programming; Multi-objective decision-making; Multi-level multi-objective decision-making; Fuzzy decision-approach; Linear- programming with interval coefficients.
2008
3
01
67
74
International Journal of Industrial Engineering & Production Research
IJIEPR
2008-4889
2345-363X
10.22068/ijiepr
2008
19
1
A bi-level linear multi-objective decision making model with interval coefficients for supply chain coordination
M.B
Aryanezhad
Mirarya@iust.ac.ir
A.
Roghanian
E-roghanian@iustarak.ac.ir
Abstract: Bi-level programming, a tool for modeling decentralized decisions, consists of the objective(s) of the leader at its first level and that is of the follower at the second level. Three level programming results when second level is itself a bi-level programming. By extending this idea it is possible to define multi-level programs with any number of levels. Supply chain planning problems are concerned with synchronizing and optimizing multiple activities involved in the enterprise, from the start of the process, such as procurement of the raw materials, through a series of process operations, to the end, such as distribution of the final product to customers. Enterprise-wide supply chain planning problems naturally exhibit a multi-level decision network structure, where for example, one level may correspond to a local plant control/scheduling/planning problem and another level to a corresponding plant-wide planning/network problem. Such a multi-level decision network structure can be mathematically represented by using “multi-level programming” principles. This paper studies a “bi-level linear multi-objective decision making” model in with “interval” parameters and presents a solution method for solving it this method uses the concepts of tolerance membership function and multi-objective multi-level optimization when all parameters are imprecise and interval .
Keywords : Multi-level programming; Multi-objective decision-making; Multi-level multi-objective decision-making; Fuzzy decision-approach; Linear- programming with interval coefficients
2008
3
01
67
74