NEWSVENDOR PROBLEM WITH PRICING: PROPERTIES, ALGORITHMS, AND SIMULATION

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "NEWSVENDOR PROBLEM WITH PRICING: PROPERTIES, ALGORITHMS, AND SIMULATION"

Transcription

1 Proceedings of the 2005 Winter Simulation Conference M. E. Kuhl, N. M. Steiger, F. B. rmstrong, and J.. Joines, eds. NEWSVENDOR PROBLEM WITH PRICING: PROPERTIES, LGORITHMS, ND SIMULTION Roger L. Zhan ISE Deartment University of Florida Gainesville, FL , U.S.. Zuo-Jun Max Shen IEOR Deartment University of California Berkeley, C , U.S.. BSTRCT When a newsvendor faces stochastic rice-sensitive demands, he/she has to make the ricing and inventory decisions before the demand is realied. In the literature, this roblem is tyically solved by reducing it to an otimiation roblem over a single variable. In contrast, we treat this roblem as a nonlinear system of two variables and rovide some solution roerties (e.g. existence, uniqueness) of the system. We also develo an iterative algorithm and a simulation based algorithm for this roblem. INTRODTION The classic newsvendor roblem is to make a single-eriod rocurement decision of a single roduct under stochastic demands. This roblem, because of its simle but elegant structure as well as its rich managerial insights, is a crucial building block of the stochastic inventory theory. It has been extensively studied over decades with extensions including different objectives and utility functions, multile roducts with substitution, multile locations, and different ricing strategies. For extensive reviews see Khouja (999) and Porteus (990). In this aer, we study the extension that incororates ricing decisions into the classic newsvendor roblem. In this scenario, the newsvendor faces stochastic demands which are influenced by the rice. The roblem is to determine the rocurement and ricing strategies that maximie the exected rofit over a single eriod. We call this roblem the Newsvendor Problem with Pricing (NPP). The research on NPP started in 950s with the work of Whitin (955) and Mills (959). Petrui and Dada (999) rovide a good review on this roblem. Recent studies incororate more market features such as rice markdown and inventory sharing among multile retailers (e.g., Sošić 2004 and Karakul and Chan 2004). To the best of our knowledge, literature on NPP mostly focuses on transforming this roblem of two decision variables (rice and order quantity) to a roblem of a single variable, either rice () or quantity (Q), then solving the roblem by using the first order conditions or an exhaustive search method. In this aer, we aroach the roblem by directly dealing with a system of two equations that are derived from the first order conditions. Using this aroach, roerties of the solutions to the roblem, as well as a good geometric exlanation of the roblem, can be obtained. Furthermore, solution algorithms are develoed based on this aroach. In articular, we roose an efficient iterative algorithm and a simulation based algorithm. In the simulation based algorithm, random samles of demands are generated and the Samle verage roximation (S) scheme and IP (Infinitesimal Perturbation nalysis) gradients are used. The simulation based algorithm can be extended to more general cases, which involve multile newsvendors. The rest of the aer is organied as follows. Section 2 resents the model and the existence and uniqueness roerties of the solution. Section 3 rooses the algorithms for the NPP roblem. Finally, Section 4 summaries the results and discusses future work. 2 MODEL ND ITS PROPERTIES 2. Notation = retail rice. Q = order quantity. v, s, c = er unit salvage value, shortage cost, and urchase cost, resectively. D(, ɛ) = a b + ɛ. ɛ = stochastic term defined on the range [, B] with mean μ. f( ), F ( ) = df and cdf of the distribution of ɛ. = Q (a b). (, ) = exected rofit function., = otimal solution. 0 = a+bc+μ. 743

2 2.2 Model In this aer, the stochastic rice-sensitive demand D is modeled in an additive demand form, i.e., D(, ɛ) = a b + ɛ(a > 0,b > 0). ɛ is a random variable defined on [, B] with mean μ, cumulative distribution function (cdf) F( ) and robability density function (df) f( ). Before the stochastic term ɛ is realied, the newsvendor determines simultaneously an order quantity, Q, and a retail rice,, to maximie the exected rofit. We also assume that v<c to avoid the trivial solution. Denote x + = max{x,0}, the newsvendor s rofit can be exressed as the difference between the revenue and the total cost: π(q, ) = min{q, D(, ɛ)} cq + v(q D(, ɛ)) + s(d(,ɛ) Q) +. () This rofit function can be rewritten by substituting D(, ɛ) with a b + ɛ and defining = Q (a b): π(,) = (a b + min{, ɛ}) c(a b + ) +v( ɛ) + s(ɛ ) +. (2) This variable transformation simlifies the comutations and its economic meaning is rovided in Petrui and Dada (999). Noticing that (x y) + = x min{x,y}, the rofit function can be further exressed as: π(,) = ( c)(a b) (c v) sɛ +( + s v)min{, ɛ}, (3) with the exected value (, ) = E[π(,)]=( c)(a b) (c v) sμ + ( + s v)e(min{, ɛ}). (4) The objective of the newsvendor is to maximie the exected rofit (, ) by choosing and. However, the otimal solution is not necessarily an interior solution, in articular, the value of can be on the boundary, i.e., or B. In the next subsection, we exlore the conditions that guarantee the existence of the interior otimal solution. The geometric exlanation of these condition is also rovided. 2.3 Solution Proerties Define () = B (u )f (u)du. Notice that B E(min{, ɛ}) = uf (u)du + f (u)du = μ (), and (E(min{, ɛ})) = f () + (( F ())) The first artial derivatives of (, ) are (, ) (, ) = F(). = (c v) + ( + s v)[ F()], (5) = a + bc + μ () = ( 0 ) (). (6) Equations (5) and (6) imly the relationshi between and as following: () = F ( + s c ), (7) + s v () = 0 (). (8) Equation (7) is simly the solution formulation for the classic newsvendor roblem. It naturally requires that c s. nd Equation (8) imlies that 0. Thus, the boundary condition for is c s 0. In the literature, researchers usually utilie Equation (8) to reduce the original roblem to an otimiation roblem over a single variable. Our aroach, however, works on these two first order conditions directly, and rovides more insights into the roblem. Let r() = f() F(),wehave () () = r()( + s v), (9) = F(). (0) Equations (9) and (0) imly that the functions () and () increase in and, resectively. nd the second artial derivatives are dr() d 2 () r() 2 = + r() ( + s v) 2 r 2 (), () 2 () 2 = f() < 0. (2) Thus function () is concave in and based on Equation () the following lemma is established. Lemma If F( ) is a distribution function satisfying the condition r 2 () + dr() d > 0 for [, B], () is concave in. This condition is satisfied by all nondecreasing haard rate distributions, which include PF 2 distributions and the 744

3 log-normal distribution (Barlow and Proschan 975). In articular, uniform, normal, logistic, chi-squared and exonential distributions all belong to this category (Bagnoli and Bergstrom 989). The next result follows from Lemma and the fact that if functions () and () are increasing and concave in and, resectively, then the system of the equations = () and = () has at most two solutions. Lemma 2 If the condition for Lemma is satisfied, then the system of Equations (7) and (8) has at most two solutions. Geometrically, the lemma states that the two curves in Figure have at most two intersections. We denote the uer intersection as " and the lower intersection as ". Zhan and Shen B () () (a+bc+)/() c s 0 Figure 3: Two Solutions B () () 0 Figure : Two Solutions By considering the boundary conditions, B and c s 0, roerties of the NPP roblem can be established. Proosition If the condition for Lemma is satisfied and a b(c 2s) + >0, then equations (7) and (8) have a unique solution, which is also otimal for the roblem. The condition a b(c 2s)+ >0, i.e., a+bc+ >c s is to ensure there is only one intersection of the two curves in the feasible region. This case is geometrically shown in Figure 2. B () () Figure 4: No Solution If the condition a b(c 2s)+ >0 is not satisfied, then there are two scenarios. The first scenario is where Line in Figure 2 moves lower than oint ", and the system has two solutions (see Figure 3); the other scenario is where Line moves higher than oint ", and the system then has no solution, which imlies NPP roblem has no interior solution (see Figure 4). The following roositions characterie these two cases. Proosition 2 If the condition for Lemma is satisfied, a b(c 2s)+ <0 and f () < a+b(c+2s 2v)+, then equations (7) and (8) have two solutions. The one with the larger value is the otimal solution for the NPP roblem. Proosition 3 If the condition for Lemma is satisfied, a b(c 2s)+ <0 and f () > a+b(c+2s 2v)+, then equations (7) and (8) have no solution. The NPP roblem has the otimal solution on the boundary. ll the above roerties are derived under the condition that Lemma holds. If Lemma does not hold, an exhaustive search method is not avoidable. In the next section, we develo the algorithms that are based on the roerties derived in this section. c s (a+bc+)/() 0 Figure 2: Unique Solution 745

4 3 LGORITHMS Proositions and 2 state the conditions for the NPP roblem to have an interior solution. Past research usually limited the analysis on theoretical results, identified some simle cases that can be solved analytically and did not resent algorithms that could be used to solve more general roblems. In contrast, we develo an iterative algorithm, as well as a simulation based algorithm, to solve the system of Equations (7) and (8), which leads to the solution for the NPP roblem under the conditions given by Proositions and Iterative lgorithm Recall the relationshi between and : () = F ( + s c + s v ), () = 0 (). s shown later in Proosition 4 of this section, the above system can be solved iteratively and the solution converges to the otimal one. The algorithm works in this fashion: Start with an initial rice, 0, the value of can be calculated using Equation (7), and with Equation (8), a new rice can be uniquely determined. This new rice can then be used to udate in the next iteration. The algorithm reeats this rocess till the otimal solution is found. The rocedure is summaried in a seudo-code format in the following, where δ reresents the recision that is used in the stoing criterion. In ractice, we can also simly secify the number of the iterations as the stoing criterion. Iterative lgorithm Set δ; Set 0 0 ; begin loo Calculate using equation (7); Calculate using equation (8), denote the value by ; if 0 <δthen sto the loo; else set 0, continue the loo; end if; end loo If the initial rice starts from 0, the above algorithm finds the solution to the system of Equations (7) and (8), which has a larger value of if the system has two solutions as shown in Proosition 2. The following roosition shows that the algorithm converges to the otimal solution, and. Proosition 4 Starting from 0, the iterative algorithm converges to the otimal solution, and. Proof. From Equation (8) and () 0, we know that 0. To rove the convergence result, we examine the two sequences: 0 = 0, = ( 0 ), 2 = ( )... and 0 = ( 0 ), = ( ), 2 = ( 2 )..., where ( ) and ( ) are the functions given in Equations (7) and (8). Since 0 = 0, due to the monotonicity of the function ( ), 0. Similarly, due to the monotonicity of the function ( ), we get 0. The same analysis carries on while the algorithm kees roducing the elements in the sequences. That is, we have and Therefor these two sequence are monotonically decreasing and bounded. Thus they will converge to and resectively. 3.2 Simulation Based lgorithm The iterative algorithm will work well and converge to the otimal solution very fast, if it is not difficult to solve Equation (7) analytically and easy to comute () in Equation (8). To byass these two otential difficulties, we need to resort to a different aroach. Therefore, arallel to the iterative algorithm, we roose a simulation based algorithm to solve the NPP roblem, which will work articularly well if the random instance can be easily generated. The simulation based algorithm is more romising if we want to extend the algorithm to deal with more comlicated cases which may involve multile newsvendors. From Equation (4), the first order conditions can be written as (, ) (, ) = (c v) +( + s v) E(min(ɛ, )), (3) = + bc + a + E(min(ɛ, )). (4) The information of Equation (3) can be used to udate the value of by the gradient search method. ( However, ) we need to estimate E(min(ɛ,)) by E (min(ɛ,)). This estimation is justified by the unbiased IP estimator for the gradient because min(ɛ, ) is almost surely continuous with resect to. detailed discussion of the alication of IP can be found in Fu (994). Furthermore, the exectation is aroximated by the samle average value, which is usually called the Samle verage roximation (S) scheme. The rocedure is summaried in a seudo-code format in the following, where U reresents the number of regenerative cycles, and a k reresents the ste sie at iteration k, and δ reresents the recision that is used in the stoing criterion. In ractice, we can use other stoing criteria such as a reset number of iterations. 746

5 Simulation Based lgorithm Set δ; Set U; Set 0 0 ; begin loo Set u ; begin loo i. Generate an instance of the random variable ɛ; ii. ccumulate the desired gradients, dmin; iii. u u + ; end loo until u = U; Calculate the desired gradient, dmin/u; Udate the value, + a k (dmin/u); k k + ; Find using the new, denote the value by ; if 0 <δthen sto the loo; else set 0, continue the loo; end if; end loo In ste (ii) of the algorithm, we use IP to comute the gradient. In the comuter imlementation, it works as follows: if the instance of ɛ is greater than, then dmin = dmin++s c; otherwise dmin = dmin c+v. These equalities are derived from Equation (3). Starting with dmin = 0 and dividing dmin by U yield the derivative estimation. 3.3 Numerical Results In this section, we use two small numerical examles to illustrate the quality of the solutions from the the simulation based algorithm. Examle In this examle, the arameters are set as v = 0.5,s = c =,a = 200,b = 35, and ɛ follows the normal distribution with mean of ero and standard deviation of 20. Starting from 0 = 3.357, the iterative algorithm gives the otimal solution = and = We set U = 00 in the simulation based algorithm. The rogram is coded in Matlab and results from 0 runs are reorted in Table. Examle 2 In this examle, the arameters are set as v = 0.5,s = c =,a = 200,b = 35, and ɛ is exonentially distributed with mean value of 0. The iterative algorithm gives the otimal solution = and = The simulation solutions are shown in Table 2. Tables and 2 clearly indicate that we can find the high quality solutions that are very close to the otimal solutions using the simulation based algorithm. lthough this aer mainly considers the single newsvendor roblem, we believe the simulation based algorithm would have more advantage over the analytical algorithms in scenarios with multile newsvendors. 4 CONCLUSIONS Table : Simulation lgorithm: Normal Distribution Run No Mean Std. Dev Otimal Table 2: Simulation lgorithm: Exonential Distribution Run No Mean Std. Dev Otimal In this aer, we analye, from a different ersective, the solution roerties of the newsvendor roblem with ricing. The analysis focuses on the system of the two equations derived from the first order conditions. The derived roerties guide us to develo both the iterative algorithm and the simulation based algorithms. There are several extensions to this aer. The simulation based algorithm can be generalied to deal with more comlicated scenarios which may involve multile roducts and multile newsvendors. The model analyed in this aer uses the additive demand form. Extending the model to other demand forms, such as multilicative demand form, is also an interesting toic for future research. 747

6 REFERENCES Bagnoli, M., and T. Bergstrom Log-concave robability and its alications. University of Michigan. Working Paer. vailable at <htt:// ucsb.edu/ tedb/theory/delta.df>. Barlow, R. E., and F. Proschan Statistical theory of reliability and life testing: Probability models. New York: Holt, Rinehart, and Winston. Fu, M. C Otimiation via simulation: review. nnals of Oerations Research 53: Karakul, M., and L. M.. Chan Newsvendor roblem of a monoolist with clearance markets. YEM vailable at <htt://yaem2004. cukurova.edu.tr/>. Khouja, M The single-eriod (news-vendor) roblem: literature review and suggestions for future research. Omega, the International Journal of Management Science 27: Mills, E. S Uncertainty and rice theory. Quarterly Journal of Economics 73:6 30. Petrui, N. C., and M. Dada Pricing and the newsvendor roblem: a review with extensions. Oerations Research 47(2): Porteus, E. L Stochastic inventory theory. In Handooks in OR & MS, ed. D. P. Heyman and M. J. Sobel, Elsevier, North-Holland. Sošić, G Price-setting newsvendors: Effects of rice markdowns and inventory sharing. University of Southern California. Working aer. vailable at <htt://www-rcf.usc.edu/ sosic/ ricesetting.df>. Whitin, T. M Inventory control and rice theory. Management Science 2:6 68. and his web age is <htt:// shen/>. UTHOR BIOGRPHIES ROGER LEZHOU ZHN is a Ph.D. candidate in the Deartment of Industrial and Systems Engineering at the University of Florida. His research interests include auction design, decisions under uncertainty and global otimiation. He is a member of IIE, INFORMS, and SIM. His address is ZUO-JUN MX SHEN is an assistant rofessor in the Deartment of Industrial Engineering and Oerations Research at University of California-Berkeley. He obtained his Ph.D. in Industrial Engineering and Management Sciences from Northwestern University. Before joining Berkeley, he taught in the Deartment of Industrial and Systems Engineering at the University of Florida. Dr. Shen s rimary research interests are in the general area of integrated suly chain design and management, and ractical mechanism design. His address is 748

Jena Research Papers in Business and Economics

Jena Research Papers in Business and Economics Jena Research Paers in Business and Economics A newsvendor model with service and loss constraints Werner Jammernegg und Peter Kischka 21/2008 Jenaer Schriften zur Wirtschaftswissenschaft Working and Discussion

More information

An important observation in supply chain management, known as the bullwhip effect,

An important observation in supply chain management, known as the bullwhip effect, Quantifying the Bullwhi Effect in a Simle Suly Chain: The Imact of Forecasting, Lead Times, and Information Frank Chen Zvi Drezner Jennifer K. Ryan David Simchi-Levi Decision Sciences Deartment, National

More information

Confidence Intervals for Capture-Recapture Data With Matching

Confidence Intervals for Capture-Recapture Data With Matching Confidence Intervals for Cature-Recature Data With Matching Executive summary Cature-recature data is often used to estimate oulations The classical alication for animal oulations is to take two samles

More information

An optimal batch size for a JIT manufacturing system

An optimal batch size for a JIT manufacturing system Comuters & Industrial Engineering 4 (00) 17±136 www.elsevier.com/locate/dsw n otimal batch size for a JIT manufacturing system Lutfar R. Khan a, *, Ruhul. Sarker b a School of Communications and Informatics,

More information

A Simple Model of Pricing, Markups and Market. Power Under Demand Fluctuations

A Simple Model of Pricing, Markups and Market. Power Under Demand Fluctuations A Simle Model of Pricing, Markus and Market Power Under Demand Fluctuations Stanley S. Reynolds Deartment of Economics; University of Arizona; Tucson, AZ 85721 Bart J. Wilson Economic Science Laboratory;

More information

1 Gambler s Ruin Problem

1 Gambler s Ruin Problem Coyright c 2009 by Karl Sigman 1 Gambler s Ruin Problem Let N 2 be an integer and let 1 i N 1. Consider a gambler who starts with an initial fortune of $i and then on each successive gamble either wins

More information

A MOST PROBABLE POINT-BASED METHOD FOR RELIABILITY ANALYSIS, SENSITIVITY ANALYSIS AND DESIGN OPTIMIZATION

A MOST PROBABLE POINT-BASED METHOD FOR RELIABILITY ANALYSIS, SENSITIVITY ANALYSIS AND DESIGN OPTIMIZATION 9 th ASCE Secialty Conference on Probabilistic Mechanics and Structural Reliability PMC2004 Abstract A MOST PROBABLE POINT-BASED METHOD FOR RELIABILITY ANALYSIS, SENSITIVITY ANALYSIS AND DESIGN OPTIMIZATION

More information

Piracy and Network Externality An Analysis for the Monopolized Software Industry

Piracy and Network Externality An Analysis for the Monopolized Software Industry Piracy and Network Externality An Analysis for the Monoolized Software Industry Ming Chung Chang Deartment of Economics and Graduate Institute of Industrial Economics mcchang@mgt.ncu.edu.tw Chiu Fen Lin

More information

Risk in Revenue Management and Dynamic Pricing

Risk in Revenue Management and Dynamic Pricing OPERATIONS RESEARCH Vol. 56, No. 2, March Aril 2008,. 326 343 issn 0030-364X eissn 1526-5463 08 5602 0326 informs doi 10.1287/ore.1070.0438 2008 INFORMS Risk in Revenue Management and Dynamic Pricing Yuri

More information

Stochastic Derivation of an Integral Equation for Probability Generating Functions

Stochastic Derivation of an Integral Equation for Probability Generating Functions Journal of Informatics and Mathematical Sciences Volume 5 (2013), Number 3,. 157 163 RGN Publications htt://www.rgnublications.com Stochastic Derivation of an Integral Equation for Probability Generating

More information

Two-resource stochastic capacity planning employing a Bayesian methodology

Two-resource stochastic capacity planning employing a Bayesian methodology Journal of the Oerational Research Society (23) 54, 1198 128 r 23 Oerational Research Society Ltd. All rights reserved. 16-5682/3 $25. www.algrave-journals.com/jors Two-resource stochastic caacity lanning

More information

Discrete Stochastic Approximation with Application to Resource Allocation

Discrete Stochastic Approximation with Application to Resource Allocation Discrete Stochastic Aroximation with Alication to Resource Allocation Stacy D. Hill An otimization roblem involves fi nding the best value of an obective function or fi gure of merit the value that otimizes

More information

Service Network Design with Asset Management: Formulations and Comparative Analyzes

Service Network Design with Asset Management: Formulations and Comparative Analyzes Service Network Design with Asset Management: Formulations and Comarative Analyzes Jardar Andersen Teodor Gabriel Crainic Marielle Christiansen October 2007 CIRRELT-2007-40 Service Network Design with

More information

On Software Piracy when Piracy is Costly

On Software Piracy when Piracy is Costly Deartment of Economics Working aer No. 0309 htt://nt.fas.nus.edu.sg/ecs/ub/w/w0309.df n Software iracy when iracy is Costly Sougata oddar August 003 Abstract: The ervasiveness of the illegal coying of

More information

IEEM 101: Inventory control

IEEM 101: Inventory control IEEM 101: Inventory control Outline of this series of lectures: 1. Definition of inventory. Examles of where inventory can imrove things in a system 3. Deterministic Inventory Models 3.1. Continuous review:

More information

Joint Production and Financing Decisions: Modeling and Analysis

Joint Production and Financing Decisions: Modeling and Analysis Joint Production and Financing Decisions: Modeling and Analysis Xiaodong Xu John R. Birge Deartment of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208,

More information

Time-Cost Trade-Offs in Resource-Constraint Project Scheduling Problems with Overlapping Modes

Time-Cost Trade-Offs in Resource-Constraint Project Scheduling Problems with Overlapping Modes Time-Cost Trade-Offs in Resource-Constraint Proect Scheduling Problems with Overlaing Modes François Berthaut Robert Pellerin Nathalie Perrier Adnène Hai February 2011 CIRRELT-2011-10 Bureaux de Montréal

More information

Price Elasticity of Demand MATH 104 and MATH 184 Mark Mac Lean (with assistance from Patrick Chan) 2011W

Price Elasticity of Demand MATH 104 and MATH 184 Mark Mac Lean (with assistance from Patrick Chan) 2011W Price Elasticity of Demand MATH 104 and MATH 184 Mark Mac Lean (with assistance from Patrick Chan) 2011W The rice elasticity of demand (which is often shortened to demand elasticity) is defined to be the

More information

An inventory control system for spare parts at a refinery: An empirical comparison of different reorder point methods

An inventory control system for spare parts at a refinery: An empirical comparison of different reorder point methods An inventory control system for sare arts at a refinery: An emirical comarison of different reorder oint methods Eric Porras a*, Rommert Dekker b a Instituto Tecnológico y de Estudios Sueriores de Monterrey,

More information

F inding the optimal, or value-maximizing, capital

F inding the optimal, or value-maximizing, capital Estimating Risk-Adjusted Costs of Financial Distress by Heitor Almeida, University of Illinois at Urbana-Chamaign, and Thomas Philion, New York University 1 F inding the otimal, or value-maximizing, caital

More information

Universiteit-Utrecht. Department. of Mathematics. Optimal a priori error bounds for the. Rayleigh-Ritz method

Universiteit-Utrecht. Department. of Mathematics. Optimal a priori error bounds for the. Rayleigh-Ritz method Universiteit-Utrecht * Deartment of Mathematics Otimal a riori error bounds for the Rayleigh-Ritz method by Gerard L.G. Sleijen, Jaser van den Eshof, and Paul Smit Prerint nr. 1160 Setember, 2000 OPTIMAL

More information

On the predictive content of the PPI on CPI inflation: the case of Mexico

On the predictive content of the PPI on CPI inflation: the case of Mexico On the redictive content of the PPI on inflation: the case of Mexico José Sidaoui, Carlos Caistrán, Daniel Chiquiar and Manuel Ramos-Francia 1 1. Introduction It would be natural to exect that shocks to

More information

SQUARE GRID POINTS COVERAGED BY CONNECTED SOURCES WITH COVERAGE RADIUS OF ONE ON A TWO-DIMENSIONAL GRID

SQUARE GRID POINTS COVERAGED BY CONNECTED SOURCES WITH COVERAGE RADIUS OF ONE ON A TWO-DIMENSIONAL GRID International Journal of Comuter Science & Information Technology (IJCSIT) Vol 6, No 4, August 014 SQUARE GRID POINTS COVERAGED BY CONNECTED SOURCES WITH COVERAGE RADIUS OF ONE ON A TWO-DIMENSIONAL GRID

More information

Re-Dispatch Approach for Congestion Relief in Deregulated Power Systems

Re-Dispatch Approach for Congestion Relief in Deregulated Power Systems Re-Disatch Aroach for Congestion Relief in Deregulated ower Systems Ch. Naga Raja Kumari #1, M. Anitha 2 #1, 2 Assistant rofessor, Det. of Electrical Engineering RVR & JC College of Engineering, Guntur-522019,

More information

Presales, Leverage Decisions and Risk Shifting

Presales, Leverage Decisions and Risk Shifting Presales, Leverage Decisions and Risk Shifting Su Han Chan Professor Carey Business School Johns Hokins University, USA schan@jhu.edu Fang Fang AssociateProfessor School of Public Economics and Administration

More information

Load Balancing Mechanism in Agent-based Grid

Load Balancing Mechanism in Agent-based Grid Communications on Advanced Comutational Science with Alications 2016 No. 1 (2016) 57-62 Available online at www.isacs.com/cacsa Volume 2016, Issue 1, Year 2016 Article ID cacsa-00042, 6 Pages doi:10.5899/2016/cacsa-00042

More information

An Analysis of Reliable Classifiers through ROC Isometrics

An Analysis of Reliable Classifiers through ROC Isometrics An Analysis of Reliable Classifiers through ROC Isometrics Stijn Vanderlooy s.vanderlooy@cs.unimaas.nl Ida G. Srinkhuizen-Kuyer kuyer@cs.unimaas.nl Evgueni N. Smirnov smirnov@cs.unimaas.nl MICC-IKAT, Universiteit

More information

6.042/18.062J Mathematics for Computer Science December 12, 2006 Tom Leighton and Ronitt Rubinfeld. Random Walks

6.042/18.062J Mathematics for Computer Science December 12, 2006 Tom Leighton and Ronitt Rubinfeld. Random Walks 6.042/8.062J Mathematics for Comuter Science December 2, 2006 Tom Leighton and Ronitt Rubinfeld Lecture Notes Random Walks Gambler s Ruin Today we re going to talk about one-dimensional random walks. In

More information

The Graphical Method. Lecture 1

The Graphical Method. Lecture 1 References: Anderson, Sweeney, Williams: An Introduction to Management Science - quantitative aroaches to decision maing 7 th ed Hamdy A Taha: Oerations Research, An Introduction 5 th ed Daellenbach, George,

More information

The Online Freeze-tag Problem

The Online Freeze-tag Problem The Online Freeze-tag Problem Mikael Hammar, Bengt J. Nilsson, and Mia Persson Atus Technologies AB, IDEON, SE-3 70 Lund, Sweden mikael.hammar@atus.com School of Technology and Society, Malmö University,

More information

Multiperiod Portfolio Optimization with General Transaction Costs

Multiperiod Portfolio Optimization with General Transaction Costs Multieriod Portfolio Otimization with General Transaction Costs Victor DeMiguel Deartment of Management Science and Oerations, London Business School, London NW1 4SA, UK, avmiguel@london.edu Xiaoling Mei

More information

Point Location. Preprocess a planar, polygonal subdivision for point location queries. p = (18, 11)

Point Location. Preprocess a planar, polygonal subdivision for point location queries. p = (18, 11) Point Location Prerocess a lanar, olygonal subdivision for oint location ueries. = (18, 11) Inut is a subdivision S of comlexity n, say, number of edges. uild a data structure on S so that for a uery oint

More information

Asymmetric Information, Transaction Cost, and. Externalities in Competitive Insurance Markets *

Asymmetric Information, Transaction Cost, and. Externalities in Competitive Insurance Markets * Asymmetric Information, Transaction Cost, and Externalities in Cometitive Insurance Markets * Jerry W. iu Deartment of Finance, University of Notre Dame, Notre Dame, IN 46556-5646 wliu@nd.edu Mark J. Browne

More information

The fast Fourier transform method for the valuation of European style options in-the-money (ITM), at-the-money (ATM) and out-of-the-money (OTM)

The fast Fourier transform method for the valuation of European style options in-the-money (ITM), at-the-money (ATM) and out-of-the-money (OTM) Comutational and Alied Mathematics Journal 15; 1(1: 1-6 Published online January, 15 (htt://www.aascit.org/ournal/cam he fast Fourier transform method for the valuation of Euroean style otions in-the-money

More information

Service Network Design with Asset Management: Formulations and Comparative Analyzes

Service Network Design with Asset Management: Formulations and Comparative Analyzes Service Network Design with Asset Management: Formulations and Comarative Analyzes Jardar Andersen Teodor Gabriel Crainic Marielle Christiansen October 2007 CIRRELT-2007-40 Service Network Design with

More information

Effect Sizes Based on Means

Effect Sizes Based on Means CHAPTER 4 Effect Sizes Based on Means Introduction Raw (unstardized) mean difference D Stardized mean difference, d g Resonse ratios INTRODUCTION When the studies reort means stard deviations, the referred

More information

Title: Stochastic models of resource allocation for services

Title: Stochastic models of resource allocation for services Title: Stochastic models of resource allocation for services Author: Ralh Badinelli,Professor, Virginia Tech, Deartment of BIT (235), Virginia Tech, Blacksburg VA 2461, USA, ralhb@vt.edu Phone : (54) 231-7688,

More information

Large-Scale IP Traceback in High-Speed Internet: Practical Techniques and Theoretical Foundation

Large-Scale IP Traceback in High-Speed Internet: Practical Techniques and Theoretical Foundation Large-Scale IP Traceback in High-Seed Internet: Practical Techniques and Theoretical Foundation Jun Li Minho Sung Jun (Jim) Xu College of Comuting Georgia Institute of Technology {junli,mhsung,jx}@cc.gatech.edu

More information

Large firms and heterogeneity: the structure of trade and industry under oligopoly

Large firms and heterogeneity: the structure of trade and industry under oligopoly Large firms and heterogeneity: the structure of trade and industry under oligooly Eddy Bekkers University of Linz Joseh Francois University of Linz & CEPR (London) ABSTRACT: We develo a model of trade

More information

Branch-and-Price for Service Network Design with Asset Management Constraints

Branch-and-Price for Service Network Design with Asset Management Constraints Branch-and-Price for Servicee Network Design with Asset Management Constraints Jardar Andersen Roar Grønhaug Mariellee Christiansen Teodor Gabriel Crainic December 2007 CIRRELT-2007-55 Branch-and-Price

More information

The Optimal Sequenced Route Query

The Optimal Sequenced Route Query The Otimal Sequenced Route Query Mehdi Sharifzadeh, Mohammad Kolahdouzan, Cyrus Shahabi Comuter Science Deartment University of Southern California Los Angeles, CA 90089-0781 [sharifza, kolahdoz, shahabi]@usc.edu

More information

DAY-AHEAD ELECTRICITY PRICE FORECASTING BASED ON TIME SERIES MODELS: A COMPARISON

DAY-AHEAD ELECTRICITY PRICE FORECASTING BASED ON TIME SERIES MODELS: A COMPARISON DAY-AHEAD ELECTRICITY PRICE FORECASTING BASED ON TIME SERIES MODELS: A COMPARISON Rosario Esínola, Javier Contreras, Francisco J. Nogales and Antonio J. Conejo E.T.S. de Ingenieros Industriales, Universidad

More information

Risk and Return. Sample chapter. e r t u i o p a s d f CHAPTER CONTENTS LEARNING OBJECTIVES. Chapter 7

Risk and Return. Sample chapter. e r t u i o p a s d f CHAPTER CONTENTS LEARNING OBJECTIVES. Chapter 7 Chater 7 Risk and Return LEARNING OBJECTIVES After studying this chater you should be able to: e r t u i o a s d f understand how return and risk are defined and measured understand the concet of risk

More information

Penalty Interest Rates, Universal Default, and the Common Pool Problem of Credit Card Debt

Penalty Interest Rates, Universal Default, and the Common Pool Problem of Credit Card Debt Penalty Interest Rates, Universal Default, and the Common Pool Problem of Credit Card Debt Lawrence M. Ausubel and Amanda E. Dawsey * February 2009 Preliminary and Incomlete Introduction It is now reasonably

More information

Machine Learning with Operational Costs

Machine Learning with Operational Costs Journal of Machine Learning Research 14 (2013) 1989-2028 Submitted 12/11; Revised 8/12; Published 7/13 Machine Learning with Oerational Costs Theja Tulabandhula Deartment of Electrical Engineering and

More information

Beyond the F Test: Effect Size Confidence Intervals and Tests of Close Fit in the Analysis of Variance and Contrast Analysis

Beyond the F Test: Effect Size Confidence Intervals and Tests of Close Fit in the Analysis of Variance and Contrast Analysis Psychological Methods 004, Vol. 9, No., 164 18 Coyright 004 by the American Psychological Association 108-989X/04/$1.00 DOI: 10.1037/108-989X.9..164 Beyond the F Test: Effect Size Confidence Intervals

More information

On Multicast Capacity and Delay in Cognitive Radio Mobile Ad-hoc Networks

On Multicast Capacity and Delay in Cognitive Radio Mobile Ad-hoc Networks On Multicast Caacity and Delay in Cognitive Radio Mobile Ad-hoc Networks Jinbei Zhang, Yixuan Li, Zhuotao Liu, Fan Wu, Feng Yang, Xinbing Wang Det of Electronic Engineering Det of Comuter Science and Engineering

More information

Managing specific risk in property portfolios

Managing specific risk in property portfolios Managing secific risk in roerty ortfolios Andrew Baum, PhD University of Reading, UK Peter Struemell OPC, London, UK Contact author: Andrew Baum Deartment of Real Estate and Planning University of Reading

More information

Concurrent Program Synthesis Based on Supervisory Control

Concurrent Program Synthesis Based on Supervisory Control 010 American Control Conference Marriott Waterfront, Baltimore, MD, USA June 30-July 0, 010 ThB07.5 Concurrent Program Synthesis Based on Suervisory Control Marian V. Iordache and Panos J. Antsaklis Abstract

More information

Variations on the Gambler s Ruin Problem

Variations on the Gambler s Ruin Problem Variations on the Gambler s Ruin Problem Mat Willmott December 6, 2002 Abstract. This aer covers the history and solution to the Gambler s Ruin Problem, and then exlores the odds for each layer to win

More information

Large firms and heterogeneity: the structure of trade and industry under oligopoly

Large firms and heterogeneity: the structure of trade and industry under oligopoly Large firms and heterogeneity: the structure of trade and industry under oligooly Eddy Bekkers University of Linz Joseh Francois University of Linz & CEPR (London) ABSTRACT: We develo a model with endogeneity

More information

Drinking water systems are vulnerable to

Drinking water systems are vulnerable to 34 UNIVERSITIES COUNCIL ON WATER RESOURCES ISSUE 129 PAGES 34-4 OCTOBER 24 Use of Systems Analysis to Assess and Minimize Water Security Risks James Uber Regan Murray and Robert Janke U. S. Environmental

More information

THE REVISED CONSUMER PRICE INDEX IN ZAMBIA

THE REVISED CONSUMER PRICE INDEX IN ZAMBIA THE REVISED CONSUMER PRICE INDEX IN ZAMBIA Submitted by the Central Statistical office of Zambia Abstract This aer discusses the Revised Consumer Price Index (CPI) in Zambia, based on revised weights,

More information

Characterizing and Modeling Network Traffic Variability

Characterizing and Modeling Network Traffic Variability Characterizing and Modeling etwork Traffic Variability Sarat Pothuri, David W. Petr, Sohel Khan Information and Telecommunication Technology Center Electrical Engineering and Comuter Science Deartment,

More information

A Study of Active Queue Management for Congestion Control

A Study of Active Queue Management for Congestion Control In IEEE INFOCOM 2 A Study of Active Queue Management for Congestion Control Victor Firoiu Marty Borden 1 vfiroiu@nortelnetworks.com mborden@tollbridgetech.com Nortel Networks TollBridge Technologies 6

More information

COST CALCULATION IN COMPLEX TRANSPORT SYSTEMS

COST CALCULATION IN COMPLEX TRANSPORT SYSTEMS OST ALULATION IN OMLEX TRANSORT SYSTEMS Zoltán BOKOR 1 Introduction Determining the real oeration and service costs is essential if transort systems are to be lanned and controlled effectively. ost information

More information

X How to Schedule a Cascade in an Arbitrary Graph

X How to Schedule a Cascade in an Arbitrary Graph X How to Schedule a Cascade in an Arbitrary Grah Flavio Chierichetti, Cornell University Jon Kleinberg, Cornell University Alessandro Panconesi, Saienza University When individuals in a social network

More information

Measuring relative phase between two waveforms using an oscilloscope

Measuring relative phase between two waveforms using an oscilloscope Measuring relative hase between two waveforms using an oscilloscoe Overview There are a number of ways to measure the hase difference between two voltage waveforms using an oscilloscoe. This document covers

More information

Storage Basics Architecting the Storage Supplemental Handout

Storage Basics Architecting the Storage Supplemental Handout Storage Basics Architecting the Storage Sulemental Handout INTRODUCTION With digital data growing at an exonential rate it has become a requirement for the modern business to store data and analyze it

More information

TRANSCENDENTAL NUMBERS

TRANSCENDENTAL NUMBERS TRANSCENDENTAL NUMBERS JEREMY BOOHER. Introduction The Greeks tried unsuccessfully to square the circle with a comass and straightedge. In the 9th century, Lindemann showed that this is imossible by demonstrating

More information

Fluent Software Training TRN-99-003. Solver Settings. Fluent Inc. 2/23/01

Fluent Software Training TRN-99-003. Solver Settings. Fluent Inc. 2/23/01 Solver Settings E1 Using the Solver Setting Solver Parameters Convergence Definition Monitoring Stability Accelerating Convergence Accuracy Grid Indeendence Adation Aendix: Background Finite Volume Method

More information

The impact of metadata implementation on webpage visibility in search engine results (Part II) q

The impact of metadata implementation on webpage visibility in search engine results (Part II) q Information Processing and Management 41 (2005) 691 715 www.elsevier.com/locate/inforoman The imact of metadata imlementation on webage visibility in search engine results (Part II) q Jin Zhang *, Alexandra

More information

Module 5: Multiple Random Variables. Lecture 1: Joint Probability Distribution

Module 5: Multiple Random Variables. Lecture 1: Joint Probability Distribution Module 5: Multile Random Variables Lecture 1: Joint Probabilit Distribution 1. Introduction irst lecture o this module resents the joint distribution unctions o multile (both discrete and continuous) random

More information

lecture 25: Gaussian quadrature: nodes, weights; examples; extensions

lecture 25: Gaussian quadrature: nodes, weights; examples; extensions 38 lecture 25: Gaussian quadrature: nodes, weights; examles; extensions 3.5 Comuting Gaussian quadrature nodes and weights When first aroaching Gaussian quadrature, the comlicated characterization of the

More information

The Economics of the Cloud: Price Competition and Congestion

The Economics of the Cloud: Price Competition and Congestion Submitted to Oerations Research manuscrit The Economics of the Cloud: Price Cometition and Congestion Jonatha Anselmi Basque Center for Alied Mathematics, jonatha.anselmi@gmail.com Danilo Ardagna Di. di

More information

Frequentist vs. Bayesian Statistics

Frequentist vs. Bayesian Statistics Bayes Theorem Frequentist vs. Bayesian Statistics Common situation in science: We have some data and we want to know the true hysical law describing it. We want to come u with a model that fits the data.

More information

POISSON PROCESSES. Chapter 2. 2.1 Introduction. 2.1.1 Arrival processes

POISSON PROCESSES. Chapter 2. 2.1 Introduction. 2.1.1 Arrival processes Chater 2 POISSON PROCESSES 2.1 Introduction A Poisson rocess is a simle and widely used stochastic rocess for modeling the times at which arrivals enter a system. It is in many ways the continuous-time

More information

Web Application Scalability: A Model-Based Approach

Web Application Scalability: A Model-Based Approach Coyright 24, Software Engineering Research and Performance Engineering Services. All rights reserved. Web Alication Scalability: A Model-Based Aroach Lloyd G. Williams, Ph.D. Software Engineering Research

More information

An Investigation on LTE Mobility Management

An Investigation on LTE Mobility Management An Investigation on LTE Mobility Management Ren-Huang Liou, Yi-Bing Lin, Fellow, IEEE, and Shang-Chih Tsai Deartment of Comuter Science National Chiao Tung University {rhliou, liny, tsaisc}@csnctuedutw

More information

The Economics of the Cloud: Price Competition and Congestion

The Economics of the Cloud: Price Competition and Congestion Submitted to Oerations Research manuscrit (Please, rovide the manuscrit number!) Authors are encouraged to submit new aers to INFORMS journals by means of a style file temlate, which includes the journal

More information

A 60,000 DIGIT PRIME NUMBER OF THE FORM x 2 + x Introduction Stark-Heegner Theorem. Let d > 0 be a square-free integer then Q( d) has

A 60,000 DIGIT PRIME NUMBER OF THE FORM x 2 + x Introduction Stark-Heegner Theorem. Let d > 0 be a square-free integer then Q( d) has A 60,000 DIGIT PRIME NUMBER OF THE FORM x + x + 4. Introduction.. Euler s olynomial. Euler observed that f(x) = x + x + 4 takes on rime values for 0 x 39. Even after this oint f(x) takes on a high frequency

More information

HYPOTHESIS TESTING FOR THE PROCESS CAPABILITY RATIO. A thesis presented to. the faculty of

HYPOTHESIS TESTING FOR THE PROCESS CAPABILITY RATIO. A thesis presented to. the faculty of HYPOTHESIS TESTING FOR THE PROESS APABILITY RATIO A thesis resented to the faculty of the Russ ollege of Engineering and Technology of Ohio University In artial fulfillment of the requirement for the degree

More information

Softmax Model as Generalization upon Logistic Discrimination Suffers from Overfitting

Softmax Model as Generalization upon Logistic Discrimination Suffers from Overfitting Journal of Data Science 12(2014),563-574 Softmax Model as Generalization uon Logistic Discrimination Suffers from Overfitting F. Mohammadi Basatini 1 and Rahim Chiniardaz 2 1 Deartment of Statistics, Shoushtar

More information

Multiple-Project Financing with Informed Trading * Salvatore Cantale IMD International. Dmitry Lukin New Economic School. Abstract

Multiple-Project Financing with Informed Trading * Salvatore Cantale IMD International. Dmitry Lukin New Economic School. Abstract The ournal of Entrereneurial Finance Volume 6 Number ring 0-8 Coyright 0 cademy of Entrereneurial Finance nc. ll rights reserved. N: 55-9570 Multile-Project Financing with nformed Trading * alvatore Cantale

More information

Minimizing the Communication Cost for Continuous Skyline Maintenance

Minimizing the Communication Cost for Continuous Skyline Maintenance Minimizing the Communication Cost for Continuous Skyline Maintenance Zhenjie Zhang, Reynold Cheng, Dimitris Paadias, Anthony K.H. Tung School of Comuting National University of Singaore {zhenjie,atung}@com.nus.edu.sg

More information

Loglikelihood and Confidence Intervals

Loglikelihood and Confidence Intervals Stat 504, Lecture 3 Stat 504, Lecture 3 2 Review (contd.): Loglikelihood and Confidence Intervals The likelihood of the samle is the joint PDF (or PMF) L(θ) = f(x,.., x n; θ) = ny f(x i; θ) i= Review:

More information

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 29, NO. 4, APRIL 2011 757. Load-Balancing Spectrum Decision for Cognitive Radio Networks

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 29, NO. 4, APRIL 2011 757. Load-Balancing Spectrum Decision for Cognitive Radio Networks IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 29, NO. 4, APRIL 20 757 Load-Balancing Sectrum Decision for Cognitive Radio Networks Li-Chun Wang, Fellow, IEEE, Chung-Wei Wang, Student Member, IEEE,

More information

The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling

The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling The Fundamental Incomatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsamling Michael Betancourt Deartment of Statistics, University of Warwick, Coventry, UK CV4 7A BETANAPHA@GMAI.COM Abstract

More information

INFERRING APP DEMAND FROM PUBLICLY AVAILABLE DATA 1

INFERRING APP DEMAND FROM PUBLICLY AVAILABLE DATA 1 RESEARCH NOTE INFERRING APP DEMAND FROM PUBLICLY AVAILABLE DATA 1 Rajiv Garg McCombs School of Business, The University of Texas at Austin, Austin, TX 78712 U.S.A. {Rajiv.Garg@mccombs.utexas.edu} Rahul

More information

FREQUENCIES OF SUCCESSIVE PAIRS OF PRIME RESIDUES

FREQUENCIES OF SUCCESSIVE PAIRS OF PRIME RESIDUES FREQUENCIES OF SUCCESSIVE PAIRS OF PRIME RESIDUES AVNER ASH, LAURA BELTIS, ROBERT GROSS, AND WARREN SINNOTT Abstract. We consider statistical roerties of the sequence of ordered airs obtained by taking

More information

High Quality Offset Printing An Evolutionary Approach

High Quality Offset Printing An Evolutionary Approach High Quality Offset Printing An Evolutionary Aroach Ralf Joost Institute of Alied Microelectronics and omuter Engineering University of Rostock Rostock, 18051, Germany +49 381 498 7272 ralf.joost@uni-rostock.de

More information

Supplemental material for: Dynamic jump intensities and risk premiums: evidence from S&P500 returns and options

Supplemental material for: Dynamic jump intensities and risk premiums: evidence from S&P500 returns and options Sulemental material for: Dynamic jum intensities and risk remiums: evidence from S&P5 returns and otions Peter Christo ersen University of Toronto, CBS and CREATES Kris Jacobs University of Houston and

More information

Failure Behavior Analysis for Reliable Distributed Embedded Systems

Failure Behavior Analysis for Reliable Distributed Embedded Systems Failure Behavior Analysis for Reliable Distributed Embedded Systems Mario Tra, Bernd Schürmann, Torsten Tetteroo {tra schuerma tetteroo}@informatik.uni-kl.de Deartment of Comuter Science, University of

More information

Large-Scale Traffic Network Control Based on Hybrid System Modeling and Convex Analysis

Large-Scale Traffic Network Control Based on Hybrid System Modeling and Convex Analysis 48 5 11 5 6 JA Large-Scale Traffic Networ Control Based on Hybrid System Modeling and Convex Analysis Tatsuya KatoNagoya University),YoungWoo KimToyota Technological Institute),Shigeru OumaNagoya University)

More information

http://www.ualberta.ca/~mlipsett/engm541/engm541.htm

http://www.ualberta.ca/~mlipsett/engm541/engm541.htm ENGM 670 & MECE 758 Modeling and Simulation of Engineering Systems (Advanced Toics) Winter 011 Lecture 9: Extra Material M.G. Lisett University of Alberta htt://www.ualberta.ca/~mlisett/engm541/engm541.htm

More information

Synopsys RURAL ELECTRICATION PLANNING SOFTWARE (LAPER) Rainer Fronius Marc Gratton Electricité de France Research and Development FRANCE

Synopsys RURAL ELECTRICATION PLANNING SOFTWARE (LAPER) Rainer Fronius Marc Gratton Electricité de France Research and Development FRANCE RURAL ELECTRICATION PLANNING SOFTWARE (LAPER) Rainer Fronius Marc Gratton Electricité de France Research and Develoment FRANCE Synosys There is no doubt left about the benefit of electrication and subsequently

More information

Working paper No: 23/2011 May 2011 LSE Health. Sotiris Vandoros, Katherine Grace Carman. Demand and Pricing of Preventative Health Care

Working paper No: 23/2011 May 2011 LSE Health. Sotiris Vandoros, Katherine Grace Carman. Demand and Pricing of Preventative Health Care Working aer o: 3/0 May 0 LSE Health Sotiris Vandoros, Katherine Grace Carman Demand and Pricing of Preventative Health Care Demand and Pricing of Preventative Healthcare Sotiris Vandoros, Katherine Grace

More information

Design of A Knowledge Based Trouble Call System with Colored Petri Net Models

Design of A Knowledge Based Trouble Call System with Colored Petri Net Models 2005 IEEE/PES Transmission and Distribution Conference & Exhibition: Asia and Pacific Dalian, China Design of A Knowledge Based Trouble Call System with Colored Petri Net Models Hui-Jen Chuang, Chia-Hung

More information

A Modified Measure of Covert Network Performance

A Modified Measure of Covert Network Performance A Modified Measure of Covert Network Performance LYNNE L DOTY Marist College Deartment of Mathematics Poughkeesie, NY UNITED STATES lynnedoty@maristedu Abstract: In a covert network the need for secrecy

More information

where a, b, c, and d are constants with a 0, and x is measured in radians. (π radians =

where a, b, c, and d are constants with a 0, and x is measured in radians. (π radians = Introduction to Modeling 3.6-1 3.6 Sine and Cosine Functions The general form of a sine or cosine function is given by: f (x) = asin (bx + c) + d and f(x) = acos(bx + c) + d where a, b, c, and d are constants

More information

Pressure Drop in Air Piping Systems Series of Technical White Papers from Ohio Medical Corporation

Pressure Drop in Air Piping Systems Series of Technical White Papers from Ohio Medical Corporation Pressure Dro in Air Piing Systems Series of Technical White Paers from Ohio Medical Cororation Ohio Medical Cororation Lakeside Drive Gurnee, IL 600 Phone: (800) 448-0770 Fax: (847) 855-604 info@ohiomedical.com

More information

An Analysis Model of Botnet Tracking based on Ant Colony Optimization Algorithm

An Analysis Model of Botnet Tracking based on Ant Colony Optimization Algorithm An Analysis Model of Botnet Tracing based on Ant Colony Otimization Algorithm Ping Wang Tzu Chia Wang* Pu-Tsun Kuo Chin Pin Wang Deartment of Information Management Kun Shan University, Taiwan TEL: 886+6-05-0545

More information

Rejuvenating the Supply Chain by Benchmarking using Fuzzy Cross-Boundary Performance Evaluation Approach

Rejuvenating the Supply Chain by Benchmarking using Fuzzy Cross-Boundary Performance Evaluation Approach ICSI International Journal of Engineering and echnology, Vol.2, o.6, December 2 ISS: 793-8236 Rejuvenating the Suly Chain by Benchmarking using uzzy Cross-Boundary erformance Evaluation roach RU SUIL BIDU,

More information

Static and Dynamic Properties of Small-world Connection Topologies Based on Transit-stub Networks

Static and Dynamic Properties of Small-world Connection Topologies Based on Transit-stub Networks Static and Dynamic Proerties of Small-world Connection Toologies Based on Transit-stub Networks Carlos Aguirre Fernando Corbacho Ramón Huerta Comuter Engineering Deartment, Universidad Autónoma de Madrid,

More information

Automatic Search for Correlated Alarms

Automatic Search for Correlated Alarms Automatic Search for Correlated Alarms Klaus-Dieter Tuchs, Peter Tondl, Markus Radimirsch, Klaus Jobmann Institut für Allgemeine Nachrichtentechnik, Universität Hannover Aelstraße 9a, 0167 Hanover, Germany

More information

5 Elliptic Curves in Cryptography

5 Elliptic Curves in Cryptography Crytograhy (art 5): Ellitic Curves in Crytograhy (by Evan Dummit, 2016, v. 1.00) Contents 5 Ellitic Curves in Crytograhy 1 5.1 Ellitic Curves and the Addition Law................................... 1 5.1.1

More information

ENFORCING SAFETY PROPERTIES IN WEB APPLICATIONS USING PETRI NETS

ENFORCING SAFETY PROPERTIES IN WEB APPLICATIONS USING PETRI NETS ENFORCING SAFETY PROPERTIES IN WEB APPLICATIONS USING PETRI NETS Liviu Grigore Comuter Science Deartment University of Illinois at Chicago Chicago, IL, 60607 lgrigore@cs.uic.edu Ugo Buy Comuter Science

More information

Buffer Capacity Allocation: A method to QoS support on MPLS networks**

Buffer Capacity Allocation: A method to QoS support on MPLS networks** Buffer Caacity Allocation: A method to QoS suort on MPLS networks** M. K. Huerta * J. J. Padilla X. Hesselbach ϒ R. Fabregat O. Ravelo Abstract This aer describes an otimized model to suort QoS by mean

More information

A joint initiative of Ludwig-Maximilians University s Center for Economic Studies and the Ifo Institute for Economic Research

A joint initiative of Ludwig-Maximilians University s Center for Economic Studies and the Ifo Institute for Economic Research A joint initiative of Ludwig-Maximilians University s Center for Economic Studies and the Ifo Institute for Economic Research Area Conference on Alied Microeconomics - 2 March 20 CESifo Conference Centre,

More information

An Introduction to Risk Parity Hossein Kazemi

An Introduction to Risk Parity Hossein Kazemi An Introduction to Risk Parity Hossein Kazemi In the aftermath of the financial crisis, investors and asset allocators have started the usual ritual of rethinking the way they aroached asset allocation

More information