International Journal of Industiral Engineering & Producion Research
http://ijiepr.iust.ac.ir
International Journal of Industrial Engineering & Production Research  Journal articles for year 2008, Volume 19, Number 1
Yektaweb Collection  https://yektaweb.com
en
2008/3/11

NORMAL 6VALENT CLAYEY GRAPHS OF ABELIAN GROUPS
http://ijiepr.iust.ac.ir/browse.php?a_id=161&sid=1&slc_lang=en
<span style="FONTSIZE: 11pt FONTFAMILY: "Times New Roman" msofareastfontfamily: 'MS Mincho' msoansilanguage: ENUS msofareastlanguage: ENUS msobidilanguage: ARSA">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 nonnormal Clayey graphs of finite abelian group with valency 6.</span><span style="FONTSIZE: 11pt FONTFAMILY: "Times New Roman" msofareastfontfamily: 'MS Mincho' msoansilanguage: ENUS msofareastlanguage: JA msobidilanguage: ARSA msobidifontsize: 12.0pt"> </span>
Mehdi Alaeiyan

MAXIMAL INDEPENDENT SETS FOR THE PIXEL EXPANSION OF GRAPH ACCESS STRUCTURE
http://ijiepr.iust.ac.ir/browse.php?a_id=162&sid=1&slc_lang=en
<a name="OLE_LINK2"></a><a name="OLE_LINK1"><span><span style="msobookmark: OLE_LINK2"><font face="Times New Roman" size="3">Given a graph G, a visual cryptography scheme based on the graph G is a</font></span><span> 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 </span><span>(G), moreover we present a the lower bound on the pixel expansion of basis matrices C<sub>5</sub> and Peterson graph access structure</span></span></a>
Massoud Hadian Dehkordi

SEMIRADICALS OF SUB MODULES IN MODULES
http://ijiepr.iust.ac.ir/browse.php?a_id=164&sid=1&slc_lang=en
<span style="FONTSIZE: 11pt FONTFAMILY: "Times New Roman" msofareastfontfamily: 'Times New Roman' msoansilanguage: ENUS msofareastlanguage: ENUS msobidilanguage: FA">Let<span style="POSITION: relative TOP: 2pt msotextraise: 2.0pt"><shapetype id="_x0000_t75" stroked="f" filled="f" path="m@4@5l@4@11@9@11@9@5xe" o:preferrelative="t" o:spt="75" coordsize="21600,21600"><font size="3"><font face="Times New Roman"> <stroke joinstyle="miter" ><formulas><f eqn="if lineDrawn pixelLineWidth 0" ><f eqn="sum @0 1 0" ><f eqn="sum 0 0 @1" ><f eqn="prod @2 1 2" ><f eqn="prod @3 21600 pixelWidth" ><f eqn="prod @3 21600 pixelHeight" ><f eqn="sum @0 0 1" ><f eqn="prod @6 1 2" ><f eqn="prod @7 21600 pixelWidth" ><f eqn="sum @8 21600 0" ><f eqn="prod @7 21600 pixelHeight" ><f eqn="sum @10 21600 0" ></formulas><p ath o:connecttype="rect" gradientshapeok="t" o:extrusionok="f" ><lock aspectratio="t" v:ext="edit" ></font></font></shapetype><shape id="_x0000_i1025" style="WIDTH: 12.75pt HEIGHT: 12pt" type="#_x0000_t75"><imagedata src="file:///C:DOCUME~1GEIGI2~1LOCALS~1Tempmsohtml11clip_image001.wmz" ></shape></span><font face="Times New Roman"><span style="msospacerun: yes"><font size="3"> </font></span>be a commutative ring and </font><span style="POSITION: relative TOP: 2pt msotextraise: 2.0pt"><shape id="_x0000_i1026" style="WIDTH: 15.75pt HEIGHT: 12pt" type="#_x0000_t75"><imagedata src="file:///C:DOCUME~1GEIGI2~1LOCALS~1Tempmsohtml11clip_image003.wmz" ></shape></span><font face="Times New Roman"><span style="msospacerun: yes"><font size="3"> </font></span>be a unitary </font><span style="POSITION: relative TOP: 2pt msotextraise: 2.0pt"><shape id="_x0000_i1027" style="WIDTH: 12pt HEIGHT: 12pt" type="#_x0000_t75"><imagedata src="file:///C:DOCUME~1GEIGI2~1LOCALS~1Tempmsohtml11clip_image005.wmz" ></shape></span><font face="Times New Roman"><span style="msospacerun: yes"><font size="3"> </font></span>module. We define a semi prime sub module of a module and consider various properties of it. Also we define semiradical of a sub module of a module and give a number of its properties. We define modules which satisfy the semiradical formula </font></span><span style="FONTSIZE: 11pt FONTFAMILY: "Times New Roman" msofareastfontfamily: 'Times New Roman' msoansilanguage: ENUS msofareastlanguage: ENUS msobidilanguage: ARSA"><span style="POSITION: relative TOP: 6pt msotextraise: 6.0pt"><shape id="_x0000_i1028" style="WIDTH: 54pt HEIGHT: 17.25pt" type="#_x0000_t75"><imagedata src="file:///C:DOCUME~1GEIGI2~1LOCALS~1Tempmsohtml11clip_image007.wmz" ></shape></span></span><span style="FONTSIZE: 11pt FONTFAMILY: "Times New Roman" msofareastfontfamily: 'Times New Roman' msoansilanguage: ENUS msofareastlanguage: ENUS msobidilanguage: FA">and present the existence of such a module.</span>
Hamid Tavallaee

IMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL
http://ijiepr.iust.ac.ir/browse.php?a_id=165&sid=1&slc_lang=en
<p> <a name="OLE_LINK2"></a><a name="OLE_LINK1"> 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 EMalgorithm. </a></p><p> 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 EMalgorithm 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. </p>
Rahman Farnoosh

Image Segmentation using Gaussian Mixture Model
http://ijiepr.iust.ac.ir/browse.php?a_id=19&sid=1&slc_lang=en
<p><strong><i><font face="times new roman,times,serif" size="2">Abstract: </font></i></strong><i><font face="times new roman,times,serif" size="2">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 EMalgorithm. </font></i></p><p align="justify"><font face="times new roman,times,serif" size="2"> </font><i><font face="times new roman,times,serif" size="2"> 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 EMalgorithm 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.</font> </i></p>
R. Farnoosh

A Threshold Accepting Algorithm for Partitioning Machines in a Tandem Automated Guided Vehicle
http://ijiepr.iust.ac.ir/browse.php?a_id=20&sid=1&slc_lang=en
<p><font face="times new roman,times,serif"><font size="2"><strong><i>Abstract </i></strong><i>: </i><i>A tandem automated guided vehicle (AGV) system deals with grouping workstations into some nonoverlapping zones </i><i>, and assigning exactly one AGV to each zone. This paper presents a new nonlinear integer mathematical model to group n machines into N loops that minimizes both inter and intraloop 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 branchandbound (B/B) method. The related computational results show that our proposed TA dominates the exact algorithm when the size of instances grows. </i></font></font></p><p> <strong> </strong></p>
R. TavakkoliMoghaddam

TOWARD EVOLUTIONARY INNOVATION THEORY
http://ijiepr.iust.ac.ir/browse.php?a_id=167&sid=1&slc_lang=en
<p class="StyleHeading1Complex14pt" style="MARGIN: 0in 49.55pt 0pt 45pt TEXTALIGN: justify"><a name="OLE_LINK2"></a><a name="OLE_LINK1"><span style="msobookmark: OLE_LINK2"><span style="FONTWEIGHT: normal"><font face="Times New Roman" size="3">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, ChainLinked Model and National Innovation Systems (</font></span></span></a><font size="3"><font face="Times New Roman"><city w:st="on"><place w:st="on"><span style="msobookmark: OLE_LINK2"><span style="msobookmark: OLE_LINK1"><span style="FONTWEIGHT: normal">NIS</span></span></span></place></city><span style="msobookmark: OLE_LINK2"><span style="msobookmark: OLE_LINK1"><span style="FONTWEIGHT: normal">) 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.<p></p></span></span></span></font></font></p>
.A. Seifoddin

Toward Evolutionary Innovation Theory
http://ijiepr.iust.ac.ir/browse.php?a_id=21&sid=1&slc_lang=en
<p><font face="times new roman,times,serif"><font size="2"><i><strong>Abstract</strong></i><i>: 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, ChainLinked 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. </i></font></font></p><p> <i> </i></p>
A. Seifoddin

NEW CONCEPT IN LEANNESS DEVELOPMENT AND ASSESSMENT IN PLANT LIFE CYCLE (PLC)
http://ijiepr.iust.ac.ir/browse.php?a_id=168&sid=1&slc_lang=en
<p class="StyleHeading1Complex14pt" style="MARGIN: 0in 49.55pt 0pt 45pt"><a name="OLE_LINK2"></a><a name="OLE_LINK1"><span style="msobookmark: OLE_LINK2"><font face="Times New Roman"><font size="3">At the last decade of the 20<sup>th</sup> 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<span style="msospacerun: yes"> </span></font><font size="3">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.</font></font></span></a></p>
Seyed M. Seyed Hosseini

New Concept in Leanness Development and Assessment in Plant Life Cycle (PLC)
http://ijiepr.iust.ac.ir/browse.php?a_id=22&sid=1&slc_lang=en
<p><font face="times new roman,times,serif"><font size="2"><i><strong>Abstract</strong>: At the last decade of the 20<sup>th</sup> 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.</i></font></font></p><p> <i /></p>
S.M. Seyedhoseini

A BILEVEL LINEAR MULTIOBJECTIVE DECISION MAKING MODEL WITH INTERVAL COEFFICIENTS FOR SUPPLY CHAIN COORDINATION
http://ijiepr.iust.ac.ir/browse.php?a_id=169&sid=1&slc_lang=en
<p> <a name="OLE_LINK2"></a><a name="OLE_LINK1"> Bilevel 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 bilevel programming. By extending this idea it is possible to define multilevel 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. </a></p><p> Enterprisewide supply chain planning problems naturally exhibit a multilevel decision network structure, where for example, one level may correspond to a local plant control/scheduling/planning problem and another level to a corresponding plantwide planning/network problem. Such a multilevel decision network structure can be mathematically represented by using “multilevel programming” principles. This paper studies a “bilevel linear multiobjective 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 multiobjective multilevel optimization when all parameters are imprecise and interval <em>.</em> </p><p> </p>
M.B. Aryanezhad

A bilevel linear multiobjective decision making model with interval coefficients for supply chain coordination
http://ijiepr.iust.ac.ir/browse.php?a_id=23&sid=1&slc_lang=en
<p><font face="times new roman,times,serif"><strong><i>Abstract: </i></strong><i>Bilevel 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 bilevel programming. By extending this idea it is possible to define multilevel 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. </i><font size="2"><i>Enterprisewide supply chain planning problems naturally exhibit a multilevel decision network structure, where for example, one level may correspond to a local plant control/scheduling/planning problem and another level to a corresponding plantwide planning/network problem. Such a multilevel decision network structure can be mathematically represented by using “multilevel programming” principles. This paper studies a “bilevel linear multiobjective 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 multiobjective multilevel optimization when all parameters are imprecise and interval . </i></font></font></p><p> <i> </i></p>
M.B Aryanezhad