REVISTA INVESTIGACIÓN OPERACIONAL V ol. 29, No. 2,,


 Melvyn Mitchell
 2 years ago
 Views:
Transcription
1 REVISTA INVESTIGACIÓN OPERACIONAL V ol. 29, No. 2,, GENETIC OPERATORS FOR THE MULTIOBJECTIVE FLOWSHOW PROBLEM Magdalena Bandala*, María A. OorioLama** School of Computer Science, Univeridad Autónoma de Puebla Ciudad Univeritaria, Puebla, Pue , México * RESUMEN: Uno de lo problema má importante en lo Algoritmo Genético, e la elección correcta de lo operadore de cruza y mutación. Lo operadore genético on má importante para lo cromooma no binario debido a u impacto en lo reultado. Ete trabajo preenta un análii comparativo de diferente operadore de cruza y mutación aplicado a un algoritmo genético para el problema multiobjetivo de calendarización de proceo con tranferencia cero. El algoritmo utilizado etá adaptado de un método de partición propueto por Tagami et al [7] y contruye una frontera de Pareto, minimizando la duración y el tiempo promedio de proceo. ABSTRACT: One of the mut important iue in Genetic Algorithm i the right election of croover and mutation operator. Genetic Operator are even more important for non binary chromoome due to their high impact on the reult. Thi work preent a comparative analyi of different croover and mutation operator applied to a genetic algorithm for the multiobjective flowhop problem. The algorithm ued i adapted from the partition method propoed by Tagami et al[8] and build a Pareto frontier. We minimize the makepan and the mean flowtime. Key Word: mutation operator, partition method, Parteto frontier MSC: 90C29 1. INTRODUCTION In the flowhop problem, we have a et of tak that mut be proceed in everal machine. Not all the tak have to ue all the machine, each tak i proceed in a ubet of tage. It can be aumed tranference with zero time or a continuou flow between the tage in the proce. The flowhop equencing i characterized by unidirectional flow and i NPhard. The flowhop conit of m machine and n different job to be optimally equenced through thee machine. The common aumption ued in modeling the flowhop problem are: All n job are available for proceing at time zero and each job follow identical routing through the machine. Job are independent and available in time 0. Unlimited torage exit between the machine. Each job require m operation and each operation require a different machine. Every machine procee only one job at one time and every job i proceed on one machine at one time. Setup time for the operation are equenceindependent and are included in proceing time. The machine are continuouly available. Individual operation cannot be preempted. * 95
2 In theory, integer programming and the branch and bound technique can be ued to olve the flowhop problem optimally. However, thee method are not viable on large problem. Mot cheduling problem including flowhop problem belong to the NPhard cla for which the computational effort increae exponentially with problem ize. To remedy thi, reearcher have continually focued on developing heuritic and other method. Heuritic method typically do not guarantee optimality of the final olution, but a final olution i reached quickly ad i acceptable for practical ue. Several good heuritic method have appeared in the pat three decade to help minimize makepan for flowhop. Recent advance in metaheuritic earch method that help conduct directed intelligent earch of the olution pace have brought yet new poibilitie to rapidly find good chedule, even if they are not optimal. 2. MIP FORMULATION The Mixed Integer Formulation (MIP) for the flowhop problem conider a et of contraint that avoid colliion in every tage in the proce. The mono objective problem only addree the minimization of total time in the proce, the makepan while the multiobjective may conider the flowtime and the tardine Contraint The model ha two type of contraint. The firt et guarantee that the total time, named makepan (T) exceed the total time of every job in the ytem. The econd type, avoid colliion between each pair of the job in the firt tage, that coincide in the ytem. k T t i + t ij i I k km m J m j j J t + t t + t + My km m J m j i im ( k ) m J ( i ) i m< j t + t t + t + My im ( k ) m J ( i ) m< j ik ik j Cik, i,k I,i k t 0, i I, y = 0,1 i, k I,i k Where t ij i the time that job i tay in tage j and t i i the initial time for job i Objective i The objective i to find a tage equence according to an optimal criteria. The claical three cheduling objective are (1) makepan, (2) mean flowtime and (3) mean tardine. In thi reearch, we only worked with makepan and flowtime Makepan. In cheduling literature, makepan i defined a the imum completion time of all job, or the time taken to complete the lat job on the lat machine in the chedule auming that the proceing of the firt job began at time 0. Makepan i denoted by T and computed a: T = {F j } ik 96
3 i j n Where F j i the flowtime for job j, i.e., the total time taken by job j from the intant of it releae to the hop to the time it i proceed by the lat machine Mean Flowtime. The mean flowtime meaure the average repone of the chedule to individual demand of job for ervice. Mathematically, mean flow time i the average of the flow time of all job. It i uually repreented by F and expreed a: F (x)= n j= 1 2 /JOBS f j 3. MULTIOBJECTIVE FLOWSHOP In equencing job in a flowhop we are likely to confront everal different (and often conflicting) management objective. Conequently, a chedule may have to be evaluated by different type of performance meaure. Some of thee meaure may give importance to completion time (e.g. makepan), ome to due date (e.g. mean tardine, imum tardine), and ome other to peed with which the job flow (e.g. mean flow time). The imultaneou conideration of thee objective i a multiobjective optimization problem. But, even for a ingle objective, flowhop equencing i NPhard. 3.1 Pareto Optimality A common difficulty with multiobjective optimization i the appearance of objective conflict (Han, 1988): none of the feaible olution achieve imultaneou optimization of makepan, mean flow time, mean tardine, machine utilization, etc. in a flowhop, for example. In other word, the individual optimal olution of each objective are uually different. Thu a mot favored olution would be one that would caue minimum objective conflict. Such olution may be viewed a point in a olution pace. The general multiobjective optimization problem contain a number of objective while the olution (2) mut alo atify a number of inequality and equality contraint. Mathematically, the problem may be tated a follow. Minimize F(x) = (F 1(x), F2 (x)) F 1 (x)= T (Makepan) F (x)= 2 n j= 1 f j / JOBS (Flowtime) Here the deciion parameter x i a pdimenional vector made up of p deciion variable. Solution to a multiobjective optimization problem are mathematically expreed in term of nondominated point. The nondomination property of olution may be explained a follow. In a minimization problem, a olution vector x(1) i partially dominated by another vector x(2) (written a x(1) x(2)), when no component value of x(2) i le than x(1) and at leat one component of x(2) i trictly greater than x(1). In a minimization problem if x(1) i partially le than x(2), we ay that the olution x(1) dominate x(2) or the olution x(2) i inferior to x(1). Any ember of uch vector that i not dominated by any other member i aid to be nondominated or noninferior. Thi reearch get the Pareto frontier for olution that minimize the makepan and the mean flowtime. 3.3 Multiobjective Genetic Algorithm 97
4 There are two type of evolutive algorithm for mulitobjective optimization. Algorithm that do not incorporate the Pareto optimality concept for the election proce and algorithm that hierarchize the population according to Pareto domination. For the algorithm that conider Pareto optimization there are two generation. The firt generation of algorithm utilize a ranking for the bet individual. The econd generation ue the concept of elitim for the individual election and for the election of econdary population of individual. Figure. 1 A Pareto Frontier 4. GENETIC OPERATORS 4.1 Croover The genetic algorithm wa implemented in C. The individual ue a non binary repreentation for the chromoome uing the job equence a in Kobayahi et al [1995]. The election of chromoome for croover i made by the creation of an elite ubet. From the original population, the algorithm elect the individual with better qualification ( 8) and then randomly elect the individual that will perform the croover. The elected individual can have different.thi type of election doe not ue the croover probability becaue the element in the elite ubet will perform the croover with the other element in the ubet until the population generated reache the ame ize than the original population. Due to the nonbinary chromoome, we ued two croover: 1) a one point croover with a reparation algorithm propoed by Jayaram [16], that force olution to be feaible, and 2) the ubequence exchange croover preented by Kobayahi et al [1995] One Point Croover It randomly choice a point in the chromoome choen to be the parent and from thi point, it interchange the information from both chromoome to generate new individual. Thi croover method can generate invalid chromoome and require a reparation method that retore feaibility in the chromoome. The algorithm ha the following tep: 1. It randomly elect a cutting point in the parent chromoome. 2. It copie in the new individual, the chain before the cutting point, in the chromoome parent 3. In the firt individual generated, the empty poition will be filled with element from the econd parent that do not exit in the firt individual, in the ame order that they appear in the econd father. f i f i 98
5 4. In the econd individual generated, the empty poition will be filled with element from the firt parent that do not exit in the econd individual, in the ame order that they appear in the firt father. 5. FIRST Author Characteritic Year GENERATION VEGA Schaffer Divide population according to the number of objective, give priority to only one MOGA Foneca and Fleming, Hierarchize the population according to Pareto dominance with niche NSGA Sriniva y Deb Hierarchize the population by lide. Keep diverity NPGA SECOND GENERATION Elitim + Niche Horn, Nafplioti and Goldberg Make tournament between individual. Hierarchize the population according to the199 tournament. 4 Author Characteritic Year SPEA Zitzler and Thiele Ue a population to help nondominated individual. Hierarchize the external population PAES Knowleand and Ue an auxiliar population for the Corne nondominated individual. 199 Divide the objective pace in grid. Make9 election by elitim and Pareto. NSGA II Deb, Agrawal, Pratap Hierarchize population according to NSGA:. and Meyarivan PESA Corne, Knowle and Ue auxiliary population for the nondominated Oate individual Divide the objective pace in grid and ue a0 election criterion baed on elitim and Pareto. NSGAII Ded and Goel Control elitim SPEA2 Zitzler, Laumann and Thiele Figure. 2 Multiobjective algorithm evolution Subequence Exchange Croover Baed on Pareto dominance. Keep diverity Thi method interchange ubequence that have the ame element in each chromoome parent an interchange the chain. It originate valid chromoome and doe not need a reparation algorithm. 99
6 4.2 Mutation Figure. 3 Subequence Exchange Croover Mutation i made by interchanging adjacent job in the chromoome. The job ued for mutation are choen randomly. During all experiment, the population ize i et i et a 25, the mutation rate a 0.1 with only one interchange in the chromoome. In the Pareto partitioning method, the number of partitioning region i et a the GENETIC ALGORITHM UTILIZED The genetic algorithm propoed i baed in the Pareto partitioning algorithm propoed by Takanori Tagami and Tohru Kawabe [1998]. The Pareto partitioning algorithm conit in dividing the olution pace in pecific region, a a grid where every objective function ha a certain number of pecific region and the objective i to put olution in the Pareto frontier. The evaluation function (fitne) for each individual p i i defined a f i = 1/n i. The value of n i denote the number of olution nondominated in the region p i. There are algorithm that work with a very imilar method and divide the olution pace in cubic ubpace that contitute a grid where the objective i to locate individual that are nondominated in an uniform and ditributed way in the Pareto frontier. Thoe algorithm are: PAES (Pareto Archived Evolution Strategy), Knowle and Corne, (1999), PESA (Pareto Envelopebaed Selection Algorithm), Knowle and Corne, (2000), PESA II Corne, Jerram, Knowle and Oate (2001). The Pareto partitioning algorithm partition can be tated a follow: Algorithm Step 1 Generate a random initial population with M individual. Step 2 Calculate the makepan and the flowtime for each individual in the initial population. Step 3 Calculate the fitne of each olution in the current population according to the multobjective ranking. i) Aign a rank R i to every individual in the population. The rank will correpond to the number of member in the population that dominate the individual. 100
7 Step 4 ii) Aign the fitne f i to all the nondominated individual. The evaluation function f i of every individual p i i defined a follow: f = (f f (R )) i i 1 f 1 = (f M 1 f min where M denote the population ize, f and f min denote the imun and the minimum fitne value. Generate a new population from the initial population, uing the croover operator Step 5 Apply mutation to the new population. Step 6 Apply the evaluation function (Fitne) to both population. Step 7 Select M individual from all population on the bai of their fitne. Step 8 6. COMPUTATIONAL RESULTS If a terminal condition i atified, top and return the bet individual. Otherwie go to Step 2. ) We teted everal ize for the numerical example preented by Bagchi in [1999]. The algorithm i implemented in C (gcc Linux/3.2.2) V 5.1 under Linux. We teted problem that range from 15 to 25 job and from 10 to 15 machine. The parameter conidered are: population ize ( p ), mutation probability 2 ( p m ), number of region in the Pareto partition ( p ) and imum number of generation ( t ). The value for the parameter are reported in Table 1. Parameter Value p 50, 70, ( p ) ( 50), (70), (100) p 0.1 m t 100, 300,400,500 Table 1 Figure 4 how the graph obtained with the etting that yield the bet reult for an example with 20 job and 10 machine. Figure 5 correpond to an example with 25 job and 15 machine. The Pareto frontier can be een in both graph. 101
8 Figure. 4 Example with 20 job and 10 machine Execution time in econd of CPU, were obtained with the clock function in language C for unix. We teted intance with 25 job and 15 machine and with 20 job and 10 machine and reported the execution time. Figure. 6 Example with 25 job and 15 machine Table 2 and 3 how the execution average time in econd for different population ize ( ), and with generation ranging from 100 to Table 4 how that with a bigger of job and machine, the average execution time increae with the ize of the population and the number of generation, although the time i le for p =50 and t =100. For our tet the bet reult were obtained with a ize of p =100 and t =500 with the ame execution time for both example. Figure 7 how the average computational time and the population ize for different number of generation. It can be een that the computational time increae for bigger intance p 102
9 p t (100,1000) Execution Average Time 50 (0.01, ) (0.02,0.04) 100 (0.04,0.05) Table 2 Execution Time (ec) for n =20 and m =10 5. CONCLUSIONS The implemented election produce diverity in the population, becaue it doe not reproduce children equal to their parent. In addition, 65% to 70% of the individual are apt for croover. From both croover method, the ubequence interchange ha more computational work, becaue the number of comparion neceary to find the ubequence with equal element. When the chain contain greater number of element (20 to 25), the reultant chain do not preent a great change among them and it correponding parent. Thi ituation limit the exploration, ince it generate very imilar or equal chain ometime. p t (100, 1000) Execution Average Time 50 (0.02, ) 70 (0.03, 0.04) (0.04, 0.06) Table 3 Execution Time (ec) for n =25 and m =15 It i difficult to find ubequence of more than 5 character with ignificant change. If the chain correpond to mall ubequence, they may be different between them but very imilar to their parent, and a mutation procedure i neceary. For thi reaon, the method of croover with chromoome reparation i ued. For more than 10 work, the method of croover with chromoome reparation offer a good exploration in a maller computational time. N m p =50 p =0.1 m t (100,1000) p =70 p =0.1 m t (100,1000) Table 4 Execution Time (ec) In the mutation cae, tet were done interchanging adjacent and random work. Better chain are obtained with a randomly mutation. Bet reult are obtained with a population of 100 individual and a probability of 0.1 in 500 generation, a hown in table
10 Parámeter Bet Value p ( p ) ( 100) p m 0.1 t 500 Table 5 Bet Parameter 0.06 Tiempo en egundo n=20,m=10 n=25,m= Tamaño de población Figure 7 Time v. Population ize Received April 2006 Revied March 2007 REFERENCES BAGCHI, T. (1999) Multiobjetive Scheduling by Genetic Algorithm, Kluwer Academic Publiher, Boton/Dordrecht/London. COELLO, C.A.; D. A. VELDHUIZEN, and G.B. LAMONT, (2002) Evolutionary Algorithm for Solving Multiobjective Problem. Kluwer Academic Publiher, Boton/Dordrecht/London. 104
11 CORNE, D.W; J. D. KNOWLES, and M. J. OATE(2000), The ParetoEnvelope baed Selection Algorithm for Multiobjetive Optimization. Parallel Problem Solving from NaturePPSN VI, Springer Lecture Note in Computer Science, Springer, Berlin. CORNE, D.W. N. R. JERRAM, J. D. KNOWLES and M. J. OATES (2001) PESA11: Regionbaed Selection in Evolutionary Multiobjetive Optimization. Proceeding of the Genetic and Evolutionary Computation Conference (GECCO2001), Morgan Kaufmann Publiher, Illinoi FINK, A. and V.B.STEFAN, (2001) Solving the Continuo FlowShop Scheduling Problem by Metaheuritic. Technical Univerity of Braunchweig, Germany. FONSECA C. m. AND P. J. FLEMING. (1993) Genetic Algorithm for Multiobjective Optimization: Formulation, Dicuion and Generalization. In Stephanie Forret, editor, Proceeding of the Fifth International Conference on Genetic Algorithm, , San Mateo, California, Univerity of Illinoi at Urbana Champaign, Morgan Kauffman Publiher, Illinoi. GREINER, D. (2000) A ummarized overview of evolutionary multiobjetive algorithm and new approache CEANI, USA,. ISHIBUCHI, H., T. YOSHIDA, and T. MURATA, (2000) Balance between Genetic Search and Local Search in Memetic Algorithm for Multiobjective Permutation Flowhop Scheduling. Deparment of Indutrial Engineering, Oaka Prefecture Univerity, Japan; Deparment of Informatic, Faculty of Informatic, Kanai Univerity, Japan. JAYARAM,K. (1998) Multiobjetive Production Scheduling, M Tech Thei, Deparment of indutrial and Management Engineering, Indian Intitute of Technology Kanpur. KNOWLES, J. D. and D. W. CORNE Approximating the Nondominated Front uing the Pareto Archived Evolution Strategy. Evolutionary Computation, 8(2) pp Maachuett Intitute of Technology, KOBAYASHI, S; I. ONO, and M. YAMAMURA, (1995), An Efficient Genetic Algorithm for Job Shop Scheduling Problem, Proceeding of the Sixth International Conference on Genetic Algorithm Univerity of Pittburgh, KURI A and, J. GALAVIZ (2002), Algoritmo Genético, Intituto Politécnico Nacional, Univeridad Nacional Autónoma de México, Fondo de Cultura Económica, México PÉREZ, B y M.A. OSORIO,(2003) Análii Comparativo de Heurítica para Problema de Calendarización de Trabajo con Tranferencia Cero Memoria del Primer Congreo Mexicano de Computación Evolutiva, CIMAT, RÍOS, R. Z. y J. F. BARD (1999) Heurítica para Secuenciamiento de Tarea en Línea de Flujo. Reporte Técnico, Univeridad Autónoma de Nuevo León. RUIZ, R. y C. MORATO (2003) Evaluación de Heurítica para el problema del taller de flujo. Reporte Técnico, Departamento de etadítica e Invetigación Operativa Aplicada y Calidad. Univeridad Politécnica de Valencia, Epaña/. TAGAMI, T AND T. KAWABE (1998) Genetic Algorithm with a Pareto Partitioning Method for Multiobjetive Flowhop Scheduling, Technical Report, Kagawa Junior College, Univerity of Tukuba, Japan,. 105
12 . 106
Group Mutual Exclusion Based on Priorities
Group Mutual Excluion Baed on Prioritie Karina M. Cenci Laboratorio de Invetigación en Sitema Ditribuido Univeridad Nacional del Sur Bahía Blanca, Argentina kmc@c.un.edu.ar and Jorge R. Ardenghi Laboratorio
More informationQueueing systems with scheduled arrivals, i.e., appointment systems, are typical for frontal service systems,
MANAGEMENT SCIENCE Vol. 54, No. 3, March 28, pp. 565 572 in 25199 ein 1526551 8 543 565 inform doi 1.1287/mnc.17.82 28 INFORMS Scheduling Arrival to Queue: A SingleServer Model with NoShow INFORMS
More informationFEDERATION OF ARAB SCIENTIFIC RESEARCH COUNCILS
Aignment Report RP/98983/5/0./03 Etablihment of cientific and technological information ervice for economic and ocial development FOR INTERNAL UE NOT FOR GENERAL DITRIBUTION FEDERATION OF ARAB CIENTIFIC
More informationScheduling of Jobs and Maintenance Activities on Parallel Machines
Scheduling of Job and Maintenance Activitie on Parallel Machine ChungYee Lee* Department of Indutrial Engineering Texa A&M Univerity College Station, TX 778433131 cylee@ac.tamu.edu ZhiLong Chen** Department
More informationDISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENTMATCHING INTRUSION DETECTION SYSTEMS
DISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENTMATCHING INTRUSION DETECTION SYSTEMS Chritopher V. Kopek Department of Computer Science Wake Foret Univerity WintonSalem, NC, 2709 Email: kopekcv@gmail.com
More informationDISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENTMATCHING INTRUSION DETECTION SYSTEMS. G. Chapman J. Cleese E. Idle
DISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENTMATCHING INTRUSION DETECTION SYSTEMS G. Chapman J. Cleee E. Idle ABSTRACT Content matching i a neceary component of any ignaturebaed network Intruion Detection
More informationSCM integration: organiational, managerial and technological iue M. Caridi 1 and A. Sianei 2 Dipartimento di Economia e Produzione, Politecnico di Milano, Italy Email: maria.caridi@polimi.it Itituto
More informationPartial optimal labeling search for a NPhard subclass of (max,+) problems
Partial optimal labeling earch for a NPhard ubcla of (max,+) problem Ivan Kovtun International Reearch and Training Center of Information Technologie and Sytem, Kiev, Uraine, ovtun@image.iev.ua Dreden
More informationA Resolution Approach to a Hierarchical Multiobjective Routing Model for MPLS Networks
A Reolution Approach to a Hierarchical Multiobjective Routing Model for MPLS Networ Joé Craveirinha a,c, Rita GirãoSilva a,c, João Clímaco b,c, Lúcia Martin a,c a b c DEECFCTUC FEUC INESCCoimbra International
More informationBiObjective Optimization for the Clinical Trial Supply Chain Management
Ian David Lockhart Bogle and Michael Fairweather (Editor), Proceeding of the 22nd European Sympoium on Computer Aided Proce Engineering, 1720 June 2012, London. 2012 Elevier B.V. All right reerved. BiObjective
More informationProject Management Basics
Project Management Baic A Guide to undertanding the baic component of effective project management and the key to ucce 1 Content 1.0 Who hould read thi Guide... 3 1.1 Overview... 3 1.2 Project Management
More informationA Spam Message Filtering Method: focus on run time
, pp.2933 http://dx.doi.org/10.14257/atl.2014.76.08 A Spam Meage Filtering Method: focu on run time SinEon Kim 1, JungTae Jo 2, SangHyun Choi 3 1 Department of Information Security Management 2 Department
More informationControl of Wireless Networks with Flow Level Dynamics under Constant Time Scheduling
Control of Wirele Network with Flow Level Dynamic under Contant Time Scheduling Long Le and Ravi R. Mazumdar Department of Electrical and Computer Engineering Univerity of Waterloo,Waterloo, ON, Canada
More informationOptical Illusion. Sara Bolouki, Roger Grosse, Honglak Lee, Andrew Ng
Optical Illuion Sara Bolouki, Roger Groe, Honglak Lee, Andrew Ng. Introduction The goal of thi proect i to explain ome of the illuory phenomena uing pare coding and whitening model. Intead of the pare
More informationA technical guide to 2014 key stage 2 to key stage 4 value added measures
A technical guide to 2014 key tage 2 to key tage 4 value added meaure CONTENTS Introduction: PAGE NO. What i value added? 2 Change to value added methodology in 2014 4 Interpretation: Interpreting chool
More informationNETWORK TRAFFIC ENGINEERING WITH VARIED LEVELS OF PROTECTION IN THE NEXT GENERATION INTERNET
Chapter 1 NETWORK TRAFFIC ENGINEERING WITH VARIED LEVELS OF PROTECTION IN THE NEXT GENERATION INTERNET S. Srivatava Univerity of Miouri Kana City, USA hekhar@conrel.ice.umkc.edu S. R. Thirumalaetty now
More informationA note on profit maximization and monotonicity for inbound call centers
A note on profit maximization and monotonicity for inbound call center Ger Koole & Aue Pot Department of Mathematic, Vrije Univeriteit Amterdam, The Netherland 23rd December 2005 Abtract We conider an
More informationRisk Management for a Global Supply Chain Planning under Uncertainty: Models and Algorithms
Rik Management for a Global Supply Chain Planning under Uncertainty: Model and Algorithm Fengqi You 1, John M. Waick 2, Ignacio E. Gromann 1* 1 Dept. of Chemical Engineering, Carnegie Mellon Univerity,
More informationCHARACTERISTICS OF WAITING LINE MODELS THE INDICATORS OF THE CUSTOMER FLOW MANAGEMENT SYSTEMS EFFICIENCY
Annale Univeritati Apuleni Serie Oeconomica, 2(2), 200 CHARACTERISTICS OF WAITING LINE MODELS THE INDICATORS OF THE CUSTOMER FLOW MANAGEMENT SYSTEMS EFFICIENCY Sidonia Otilia Cernea Mihaela Jaradat 2 Mohammad
More informationTwo Dimensional FEM Simulation of Ultrasonic Wave Propagation in Isotropic Solid Media using COMSOL
Excerpt from the Proceeding of the COMSO Conference 0 India Two Dimenional FEM Simulation of Ultraonic Wave Propagation in Iotropic Solid Media uing COMSO Bikah Ghoe *, Krihnan Balaubramaniam *, C V Krihnamurthy
More informationName: SID: Instructions
CS168 Fall 2014 Homework 1 Aigned: Wedneday, 10 September 2014 Due: Monday, 22 September 2014 Name: SID: Dicuion Section (Day/Time): Intruction  Submit thi homework uing Pandagrader/GradeScope(http://www.gradecope.com/
More informationA Note on Profit Maximization and Monotonicity for Inbound Call Centers
OPERATIONS RESEARCH Vol. 59, No. 5, September October 2011, pp. 1304 1308 in 0030364X ein 15265463 11 5905 1304 http://dx.doi.org/10.1287/opre.1110.0990 2011 INFORMS TECHNICAL NOTE INFORMS hold copyright
More informationQueueing Models for Multiclass Call Centers with RealTime Anticipated Delays
Queueing Model for Multicla Call Center with RealTime Anticipated Delay Oualid Jouini Yve Dallery Zeynep Akşin Ecole Centrale Pari Koç Univerity Laboratoire Génie Indutriel College of Adminitrative Science
More informationProfitability of Loyalty Programs in the Presence of Uncertainty in Customers Valuations
Proceeding of the 0 Indutrial Engineering Reearch Conference T. Doolen and E. Van Aken, ed. Profitability of Loyalty Program in the Preence of Uncertainty in Cutomer Valuation Amir Gandomi and Saeed Zolfaghari
More informationResearch Article An (s, S) Production Inventory Controlled SelfService Queuing System
Probability and Statitic Volume 5, Article ID 558, 8 page http://dxdoiorg/55/5/558 Reearch Article An (, S) Production Inventory Controlled SelfService Queuing Sytem Anoop N Nair and M J Jacob Department
More informationTRADING rules are widely used in financial market as
Complex Stock Trading Strategy Baed on Particle Swarm Optimization Fei Wang, Philip L.H. Yu and David W. Cheung Abtract Trading rule have been utilized in the tock market to make profit for more than a
More informationINFORMATION Technology (IT) infrastructure management
IEEE TRANSACTIONS ON CLOUD COMPUTING, VOL. 2, NO. 1, MAY 214 1 BuineDriven Longterm Capacity Planning for SaaS Application David Candeia, Ricardo Araújo Santo and Raquel Lope Abtract Capacity Planning
More informationAssessing the Discriminatory Power of Credit Scores
Aeing the Dicriminatory Power of Credit Score Holger Kraft 1, Gerald Kroiandt 1, Marlene Müller 1,2 1 Fraunhofer Intitut für Techno und Wirtchaftmathematik (ITWM) GottliebDaimlerStr. 49, 67663 Kaierlautern,
More informationMANAGING DATA REPLICATION IN MOBILE AD HOC NETWORK DATABASES (Invited Paper) *
MANAGING DATA REPLICATION IN MOBILE AD HOC NETWORK DATABASES (Invited Paper) * Praanna Padmanabhan School of Computer Science The Univerity of Oklahoma Norman OK, USA praannap@yahooinc.com Dr. Le Gruenwald
More informationAccelerationDisplacement Crash Pulse Optimisation A New Methodology to Optimise Vehicle Response for Multiple Impact Speeds
AccelerationDiplacement Crah Pule Optimiation A New Methodology to Optimie Vehicle Repone for Multiple Impact Speed D. Gildfind 1 and D. Ree 2 1 RMIT Univerity, Department of Aeropace Engineering 2 Holden
More informationTurbulent Mixing and Chemical Reaction in Stirred Tanks
Turbulent Mixing and Chemical Reaction in Stirred Tank André Bakker Julian B. Faano Blend time and chemical product ditribution in turbulent agitated veel can be predicted with the aid of Computational
More informationPerformance of a BrowserBased JavaScript Bandwidth Test
Performance of a BrowerBaed JavaScript Bandwidth Tet David A. Cohen II May 7, 2013 CP SC 491/H495 Abtract An exiting browerbaed bandwidth tet written in JavaScript wa modified for the purpoe of further
More informationAlgorithms for Advance Bandwidth Reservation in Media Production Networks
Algorithm for Advance Bandwidth Reervation in Media Production Network Maryam Barhan 1, Hendrik Moen 1, Jeroen Famaey 2, Filip De Turck 1 1 Department of Information Technology, Ghent Univerity imind Gaton
More informationAssigning Tasks for Efficiency in Hadoop
Aigning Tak for Efficiency in Hadoop [Extended Abtract] Michael J. Ficher Computer Science Yale Univerity P.O. Box 208285 New Haven, CT, USA michael.ficher@yale.edu Xueyuan Su Computer Science Yale Univerity
More informationMixed Method of Model Reduction for Uncertain Systems
SERBIAN JOURNAL OF ELECTRICAL ENGINEERING Vol 4 No June Mixed Method of Model Reduction for Uncertain Sytem N Selvaganean Abtract: A mixed method for reducing a higher order uncertain ytem to a table reduced
More informationAvailability of WDM Multi Ring Networks
Paper Availability of WDM Multi Ring Network Ivan Rado and Katarina Rado H d.o.o. Motar, Motar, Bonia and Herzegovina Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Univerity
More informationThus far. Inferences When Comparing Two Means. Testing differences between two means or proportions
Inference When Comparing Two Mean Dr. Tom Ilvento FREC 48 Thu far We have made an inference from a ingle ample mean and proportion to a population, uing The ample mean (or proportion) The ample tandard
More informationReturn on Investment and Effort Expenditure in the Software Development Environment
International Journal of Applied Information ytem (IJAI) IN : 22490868 Return on Invetment and Effort Expenditure in the oftware Development Environment Dineh Kumar aini Faculty of Computing and IT, ohar
More informationCost Models for Selecting Materialized Views in Public Clouds
International Journal of Data Warehouing and Mining, 0(4), 25, OctoberDecember 204 Cot Model for Selecting Materialized View in ublic Cloud Romain erriot, Clermont Univerité, Univerité Blaie acal, Aubière
More informationRedesigning Ratings: Assessing the Discriminatory Power of Credit Scores under Censoring
Redeigning Rating: Aeing the Dicriminatory Power of Credit Score under Cenoring Holger Kraft, Gerald Kroiandt, Marlene Müller Fraunhofer Intitut für Techno und Wirtchaftmathematik (ITWM) Thi verion: June
More informationQUANTIFYING THE BULLWHIP EFFECT IN THE SUPPLY CHAIN OF SMALLSIZED COMPANIES
Sixth LACCEI International Latin American and Caribbean Conference for Engineering and Technology (LACCEI 2008) Partnering to Succe: Engineering, Education, Reearch and Development June 4 June 6 2008,
More informationPerformance of Multiple TFRC in Heterogeneous Wireless Networks
Performance of Multiple TFRC in Heterogeneou Wirele Network 1 HyeonJin Jeong, 2 SeongSik Choi 1, Firt Author Computer Engineering Department, Incheon National Univerity, oaihjj@incheon.ac.kr *2,Correponding
More informationHealth Insurance and Social Welfare. Run Liang. China Center for Economic Research, Peking University, Beijing 100871, China,
Health Inurance and Social Welfare Run Liang China Center for Economic Reearch, Peking Univerity, Beijing 100871, China, Email: rliang@ccer.edu.cn and Hao Wang China Center for Economic Reearch, Peking
More informationComparison of Scheduling Algorithms for a MultiProduct BatchChemical Plant with a Generalized Serial Network
Comparion of Scheduling Algorithm for a MultiProduct BatchChemical Plant with a Generalized Serial Network NiemTrung L. Tra Thei ubmitted to the faculty of the Virginia Polytechnic Intitute and State
More informationResource Allocation for RealTime Tasks using Cloud Computing
Reource Allocation for RealTime Tak uing Cloud Computing Karthik Kumar, Jing Feng, Yamini Nimmagadda, and YungHiang Lu School of Electrical and Computer Engineering, Purdue Univerity, Wet Lafayette,
More informationApigee Edge: Apigee Cloud vs. Private Cloud. Evaluating deployment models for API management
Apigee Edge: Apigee Cloud v. Private Cloud Evaluating deployment model for API management Table of Content Introduction 1 Time to ucce 2 Total cot of ownerhip 2 Performance 3 Security 4 Data privacy 4
More informationA Review On Software Testing In SDlC And Testing Tools
www.ijec.in International Journal Of Engineering And Computer Science ISSN:23197242 Volume  3 Iue 9 September, 2014 Page No. 81888197 A Review On Software Teting In SDlC And Teting Tool T.Amruthavalli*,
More informationUnit 11 Using Linear Regression to Describe Relationships
Unit 11 Uing Linear Regreion to Decribe Relationhip Objective: To obtain and interpret the lope and intercept of the leat quare line for predicting a quantitative repone variable from a quantitative explanatory
More informationMobile Network Configuration for Largescale Multimedia Delivery on a Single WLAN
Mobile Network Configuration for Largecale Multimedia Delivery on a Single WLAN Huigwang Je, Dongwoo Kwon, Hyeonwoo Kim, and Hongtaek Ju Dept. of Computer Engineering Keimyung Univerity Daegu, Republic
More informationREDUCTION OF TOTAL SUPPLY CHAIN CYCLE TIME IN INTERNAL BUSINESS PROCESS OF REAMER USING DOE AND TAGUCHI METHODOLOGY. Abstract. 1.
International Journal of Advanced Technology & Engineering Reearch (IJATER) REDUCTION OF TOTAL SUPPLY CHAIN CYCLE TIME IN INTERNAL BUSINESS PROCESS OF REAMER USING DOE AND Abtract TAGUCHI METHODOLOGY Mr.
More informationA New Multiobjective Evolutionary Optimisation Algorithm: The TwoArchive Algorithm
A New Multiobjective Evolutionary Optimisation Algorithm: The TwoArchive Algorithm Kata Praditwong 1 and Xin Yao 2 The Centre of Excellence for Research in Computational Intelligence and Applications(CERCIA),
More informationSector Concentration in Loan Portfolios and Economic Capital. Abstract
Sector Concentration in Loan Portfolio and Economic Capital Klau Düllmann and Nancy Machelein 2 Thi verion: September 2006 Abtract The purpoe of thi paper i to meaure the potential impact of buineector
More informationResearch in Economics
Reearch in Economic 64 (2010) 137 145 Content lit available at ScienceDirect Reearch in Economic journal homepage: www.elevier.com/locate/rie Health inurance: Medical treatment v diability payment Geir
More informationGrowth and Sustainability of Managed Security Services Networks: An Economic Perspective
Growth and Sutainability of Managed Security Service etwork: An Economic Perpective Alok Gupta Dmitry Zhdanov Department of Information and Deciion Science Univerity of Minneota Minneapoli, M 55455 (agupta,
More information1 Introduction. Reza Shokri* Privacy Games: Optimal UserCentric Data Obfuscation
Proceeding on Privacy Enhancing Technologie 2015; 2015 (2):1 17 Reza Shokri* Privacy Game: Optimal UerCentric Data Obfucation Abtract: Conider uer who hare their data (e.g., location) with an untruted
More informationLinear Momentum and Collisions
Chapter 7 Linear Momentum and Colliion 7.1 The Important Stuff 7.1.1 Linear Momentum The linear momentum of a particle with ma m moving with velocity v i defined a p = mv (7.1) Linear momentum i a vector.
More informationTowards ControlRelevant Forecasting in Supply Chain Management
25 American Control Conference June 81, 25. Portland, OR, USA WeA7.1 Toward ControlRelevant Forecating in Supply Chain Management Jay D. Schwartz, Daniel E. Rivera 1, and Karl G. Kempf Control Sytem
More informationSenior Thesis. Horse Play. Optimal Wagers and the Kelly Criterion. Author: Courtney Kempton. Supervisor: Professor Jim Morrow
Senior Thei Hore Play Optimal Wager and the Kelly Criterion Author: Courtney Kempton Supervior: Profeor Jim Morrow June 7, 20 Introduction The fundamental problem in gambling i to find betting opportunitie
More informationA Life Contingency Approach for Physical Assets: Create Volatility to Create Value
A Life Contingency Approach for Phyical Aet: Create Volatility to Create Value homa Emil Wendling 2011 Enterprie Rik Management Sympoium Society of Actuarie March 1416, 2011 Copyright 2011 by the Society
More informationIn this paper, we investigate toll setting as a policy tool to regulate the use of roads for dangerous goods
Vol. 43, No. 2, May 2009, pp. 228 243 in 00411655 ein 15265447 09 4302 0228 inform doi 10.1287/trc.1080.0236 2009 INFORMS Toll Policie for Mitigating Hazardou Material Tranport Rik Patrice Marcotte,
More informationTap Into Smartphone Demand: Mobileizing Enterprise Websites by Using Flexible, Open Source Platforms
Tap Into Smartphone Demand: Mobileizing Enterprie Webite by Uing Flexible, Open Source Platform acquia.com 888.922.7842 1.781.238.8600 25 Corporate Drive, Burlington, MA 01803 Tap Into Smartphone Demand:
More information2. METHOD DATA COLLECTION
Key to learning in pecific ubject area of engineering education an example from electrical engineering AnnaKarin Cartenen,, and Jonte Bernhard, School of Engineering, Jönköping Univerity, S Jönköping,
More informationCASE STUDY BRIDGE. www.futureprocessing.com
CASE STUDY BRIDGE TABLE OF CONTENTS #1 ABOUT THE CLIENT 3 #2 ABOUT THE PROJECT 4 #3 OUR ROLE 5 #4 RESULT OF OUR COLLABORATION 67 #5 THE BUSINESS PROBLEM THAT WE SOLVED 8 #6 CHALLENGES 9 #7 VISUAL IDENTIFICATION
More informationExposure Metering Relating Subject Lighting to Film Exposure
Expoure Metering Relating Subject Lighting to Film Expoure By Jeff Conrad A photographic expoure meter meaure ubject lighting and indicate camera etting that nominally reult in the bet expoure of the film.
More informationGrowth and Sustainability of Managed Security Services Networks: An Economic Perspective
Growth and Sutainability of Managed Security Service etwork: An Economic Perpective Alok Gupta Dmitry Zhdanov Department of Information and Deciion Science Univerity of Minneota Minneapoli, M 55455 (agupta,
More informationGrowing SelfOrganizing Maps for Surface Reconstruction from Unstructured Point Clouds
Growing SelfOrganizing Map for Surface Recontruction from Untructured Point Cloud Renata L. M. E. do Rêgo, Aluizio F. R. Araújo, and Fernando B.de Lima Neto Abtract Thi work introduce a new method for
More informationTIME SERIES ANALYSIS AND TRENDS BY USING SPSS PROGRAMME
TIME SERIES ANALYSIS AND TRENDS BY USING SPSS PROGRAMME RADMILA KOCURKOVÁ Sileian Univerity in Opava School of Buine Adminitration in Karviná Department of Mathematical Method in Economic Czech Republic
More informationBidding for Representative Allocations for Display Advertising
Bidding for Repreentative Allocation for Diplay Advertiing Arpita Ghoh, Preton McAfee, Kihore Papineni, and Sergei Vailvitkii Yahoo! Reearch. {arpita, mcafee, kpapi, ergei}@yahooinc.com Abtract. Diplay
More informationProceedings of Power Tech 2007, July 15, Lausanne
Second Order Stochatic Dominance Portfolio Optimization for an Electric Energy Company M.P. Cheong, Student Member, IEEE, G. B. Sheble, Fellow, IEEE, D. Berleant, Senior Member, IEEE and C.C. Teoh, Student
More informationMobility Improves Coverage of Sensor Networks
Mobility Improve Coverage of Senor Networ Benyuan Liu Dept. of Computer Science Univerity of MaachuettLowell Lowell, MA 1854 Peter Bra Dept. of Computer Science City College of New Yor New Yor, NY 131
More informationChapter 10 Stocks and Their Valuation ANSWERS TO ENDOFCHAPTER QUESTIONS
Chapter Stoc and Their Valuation ANSWERS TO ENOFCHAPTER QUESTIONS  a. A proxy i a document giving one peron the authority to act for another, typically the power to vote hare of common toc. If earning
More informationINTERACTIVE TOOL FOR ANALYSIS OF TIMEDELAY SYSTEMS WITH DEADTIME COMPENSATORS
INTERACTIVE TOOL FOR ANALYSIS OF TIMEDELAY SYSTEMS WITH DEADTIME COMPENSATORS Joé Lui Guzmán, Pedro García, Tore Hägglund, Sebatián Dormido, Pedro Alberto, Manuel Berenguel Dep. de Lenguaje y Computación,
More information1) Assume that the sample is an SRS. The problem state that the subjects were randomly selected.
12.1 Homework for t Hypothei Tet 1) Below are the etimate of the daily intake of calcium in milligram for 38 randomly elected women between the age of 18 and 24 year who agreed to participate in a tudy
More informationFinal Award. (exit route if applicable for Postgraduate Taught Programmes) N/A JACS Code. Fulltime. Length of Programme. Queen s University Belfast
Date of Reviion Date of Previou Reviion Programme Specification (201415) A programme pecification i required for any programme on which a tudent may be regitered. All programme of the Univerity are ubject
More informationDUE to the small size and low cost of a sensor node, a
1992 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 14, NO. 10, OCTOBER 2015 A Networ Coding Baed Energy Efficient Data Bacup in SurvivabilityHeterogeneou Senor Networ Jie Tian, Tan Yan, and Guiling Wang
More informationLaureate Network Products & Services Copyright 2013 Laureate Education, Inc.
Laureate Network Product & Service Copyright 2013 Laureate Education, Inc. KEY Coure Name Laureate Faculty Development...3 Laureate Englih Program...9 Language Laureate Signature Product...12 Length Laureate
More informationHow Enterprises Can Build Integrated Digital Marketing Experiences Using Drupal
How Enterprie Can Build Integrated Digital Marketing Experience Uing Drupal acquia.com 888.922.7842 1.781.238.8600 25 Corporate Drive, Burlington, MA 01803 How Enterprie Can Build Integrated Digital Marketing
More informationSocially Optimal Pricing of Cloud Computing Resources
Socially Optimal Pricing of Cloud Computing Reource Ihai Menache Microoft Reearch New England Cambridge, MA 02142 timena@microoft.com Auman Ozdaglar Laboratory for Information and Deciion Sytem Maachuett
More informationJanuary 21, 2015. Abstract
T S U I I E P : T R M C S J. R January 21, 2015 Abtract Thi paper evaluate the trategic behavior of a monopolit to influence environmental policy, either with taxe or with tandard, comparing two alternative
More informationTtest for dependent Samples. Difference Scores. The t Test for Dependent Samples. The t Test for Dependent Samples. s D
The t Tet for ependent Sample Ttet for dependent Sample (ak.a., Paired ample ttet, Correlated Group eign, Within Subject eign, Repeated Meaure,.. RepeatedMeaure eign When you have two et of core from
More informationInternational Journal of Heat and Mass Transfer
International Journal of Heat and Ma Tranfer 5 (9) 14 144 Content lit available at ScienceDirect International Journal of Heat and Ma Tranfer journal homepage: www.elevier.com/locate/ijhmt Technical Note
More informationReport 46681b 30.10.2010. Measurement report. Sylomer  field test
Report 46681b Meaurement report Sylomer  field tet Report 46681b 2(16) Contet 1 Introduction... 3 1.1 Cutomer... 3 1.2 The ite and purpoe of the meaurement... 3 2 Meaurement... 6 2.1 Attenuation of
More informationClusterAware Cache for Network Attached Storage *
CluterAware Cache for Network Attached Storage * Bin Cai, Changheng Xie, and Qiang Cao National Storage Sytem Laboratory, Department of Computer Science, Huazhong Univerity of Science and Technology,
More informationMulticlass Classification using Neural Networks and Interval Neutrosophic Sets
Proceeding of the 5th WSEAS Int. Conf. on COMPUTATIONAL INTELLIGENCE, MANMACHINE SYSTEMS AND CYBERNETICS, Venice, Italy, November 2022, 2006 123 Multicla Claification uing Neural Network and Interval
More informationv = x t = x 2 x 1 t 2 t 1 The average speed of the particle is absolute value of the average velocity and is given Distance travelled t
Chapter 2 Motion in One Dimenion 2.1 The Important Stuff 2.1.1 Poition, Time and Diplacement We begin our tudy of motion by conidering object which are very mall in comparion to the ize of their movement
More informationCHAPTER 5 BROADBAND CLASSE AMPLIFIER
CHAPTER 5 BROADBAND CLASSE AMPLIFIER 5.0 Introduction ClaE amplifier wa firt preented by Sokal in 1975. The application of cla E amplifier were limited to the VHF band. At thi range of frequency, clae
More informationAn Evolutionary Algorithm in Grid Scheduling by multiobjective Optimization using variants of NSGA
International Journal of Scientific and Research Publications, Volume 2, Issue 9, September 2012 1 An Evolutionary Algorithm in Grid Scheduling by multiobjective Optimization using variants of NSGA Shahista
More informationReview of Multiple Regression Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ Last revised January 13, 2015
Review of Multiple Regreion Richard William, Univerity of Notre Dame, http://www3.nd.edu/~rwilliam/ Lat revied January 13, 015 Aumption about prior nowledge. Thi handout attempt to ummarize and yntheize
More informationSupport Vector Machine Based Electricity Price Forecasting For Electricity Markets utilising Projected Assessment of System Adequacy Data.
The Sixth International Power Engineering Conference (IPEC23, 2729 November 23, Singapore Support Vector Machine Baed Electricity Price Forecating For Electricity Maret utiliing Projected Aement of Sytem
More informationAutomatic Generation Control of TwoArea System Considering NonLinearities
International Journal of Engineering and Technical Reearch (IJETR) ISSN: 2320869, Volume2, Iue5, May 204 Automatic Generation Control of TwoArea Sytem Conidering NonLinearitie Akul Sharma, Kamleh
More informationPlanning of Capacity and Inventory in a Manufacturing Supply Chain: Under Uncertain Demand. Acknowledgements. Outline
Planning of Capacity and Inventory in a Manufacturing Supply Chain: Under Uncertain Demand B. Dominguez Balletero,, C. Luca, G. Mitra,, C. Poojari Acknowledgement European Project SCHUMANN: Supply Chain
More informationOnline story scheduling in web advertising
Online tory cheduling in web advertiing Anirban Dagupta Arpita Ghoh Hamid Nazerzadeh Prabhakar Raghavan Abtract We tudy an online job cheduling problem motivated by toryboarding in web advertiing, where
More informationEfficient Pricing and Insurance Coverage in Pharmaceutical Industry when the Ability to Pay Matters
ömmföäfläafaäflaflafla fffffffffffffffffffffffffffffffffff Dicuion Paper Efficient Pricing and Inurance Coverage in Pharmaceutical Indutry when the Ability to Pay Matter Vea Kanniainen Univerity of Helinki,
More informationA New Optimum Jitter Protection for Conversational VoIP
Proc. Int. Conf. Wirele Commun., Signal Proceing (Nanjing, China), 5 pp., Nov. 2009 A New Optimum Jitter Protection for Converational VoIP Qipeng Gong, Peter Kabal Electrical & Computer Engineering, McGill
More informationDistributed, Secure Load Balancing with Skew, Heterogeneity, and Churn
Ditributed, Secure Load Balancing with Skew, Heterogeneity, and Churn Jonathan Ledlie and Margo Seltzer Diviion of Engineering and Applied Science Harvard Univerity Abtract Numerou propoal exit for load
More informationA Duality Model of TCP and Queue Management Algorithms
A Duality Model of TCP and Queue Management Algorithm Steven H. Low CS and EE Department California Intitute of Technology Paadena, CA 95 low@caltech.edu May 4, Abtract We propoe a duality model of endtoend
More informationMBA 570x Homework 1 Due 9/24/2014 Solution
MA 570x Homework 1 Due 9/24/2014 olution Individual work: 1. Quetion related to Chapter 11, T Why do you think i a fund of fund market for hedge fund, but not for mutual fund? Anwer: Invetor can inexpenively
More informationUtilityBased Flow Control for Sequential Imagery over Wireless Networks
UtilityBaed Flow Control for Sequential Imagery over Wirele Networ Tomer Kihoni, Sara Callaway, and Mar Byer Abtract Wirele enor networ provide a unique et of characteritic that mae them uitable for building
More informationSENSING IMAGES. School of Remote Sensing and Information Engineering, Wuhan University, 129# Luoyu Road, Wuhan, China,ych@whu.edu.
International Archive of the Photogrammetry, Remote Sening and Spatial Information Science, Volume X/W, 3 8th International Sympoium on Spatial Data Quality, 3 May  June 3, Hong Kong COUD DETECTION METHOD
More informationHUMAN CAPITAL AND THE FUTURE OF TRANSITION ECONOMIES * Michael Spagat Royal Holloway, University of London, CEPR and Davidson Institute.
HUMAN CAPITAL AND THE FUTURE OF TRANSITION ECONOMIES * By Michael Spagat Royal Holloway, Univerity of London, CEPR and Davidon Intitute Abtract Tranition economie have an initial condition of high human
More information