Robust Design of Public Storage Warehouses. Yeming (Yale) Gong EMLYON Business School

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Robust Design of Public Storage Warehouses. Yeming (Yale) Gong EMLYON Business School"

Transcription

1 Robust Desgn of Publc Storage Warehouses Yemng (Yale) Gong EMLYON Busness School Rene de Koster Rotterdam school of management, Erasmus Unversty Abstract We apply robust optmzaton and revenue management n publc storage warehouses. We optmze the expected revenue of publc storage warehouses aganst the worst cases wth a max-mn revenue objectve, and the decson varables are manly the number of storage unts for each storage type. Wth the robust desgn, we can observe worst-case revenue mprovement. Introducton Publc storage warehousng s a mature ndustry n the U.S. and a fast-growng busness n Europe and Asa Pacfc. As the SSA (Self Storage Assocaton, the offcal assocaton of ths ndustry n the U.S.) reported, the number of such facltes has clmbed from only 6,60 facltes at year-end 984 to over 46,500 at year-end 200. In the year of 200, the total revenue of ths ndustry n the U.S. was $22 bllon (USD). Ths busness has become one of the fastest-growng sectors of the Unted States commercal real estate ndustry over the last 35 years (see []). The ndustry n Europe had boomed after a breathtakng growng from 2006 to Accordng to the 2008 ndustry annual report of the SSA UK (Self Storage Assocaton of the UK, the offcal assocaton of ths ndustry n the UK), the number of publc storage warehouse facltes n Europe had sharply ncreased from 2007 to 2008, partcularly n those emergng markets. For example, the ndustry had ncreased by 200% n Poland, 7% n Swtzerland, 64% n Denmark (see [2]). Publc storage warehousng usually operates as self-storage mode and thereby ts cost s low and stable. For publc storage warehouses wth an objectve to

2 maxmze the expected revenue and wth a stable cost level, revenue management rather than cost control s crtcal. A typcal publc storage warehouse contans storage spaces of dfferent storage types, each type wth a specfc number of storage unts. A customer rents one or multple storage unts of an approprate type for several months. However, the exstng storage types or the avalable number of storage unts for each type may not ft the needs of the market. The number of avalable storage unts of some types may be nsuffcent, whle other types are abundant. Ths results n ether lost customers and revenue, or neffcent utlzaton of capacty of one type, whch also may brng potental loss n another type. The man faclty desgn queston s to provde a faclty desgn mprovng the expected revenue that s wth a better ft between storage desgn (types and numbers) and market demand. Based on data of warehouses n Amerca, Europe and Asa, Gong et al.[3] propose a faclty desgn approach for publc storage warehouses wth three dfferent cases: an overflow customer rejecton model, and two models wth customer upgrade possbltes: one wth reservaton and another wthout reservaton. They analyze models for real warehouse cases manly by stochastc models and dynamc programmng (see [3], [4]). Whle these methods assume a probablty dstrbuton or a specfc stochastc process of demands, t s dffcult to accurately estmate the probablty dstrbuton of the total arrvng storage requests or assocated revenue n some warehouses. To provde a method to solve the practcal problem, we present robust optmzaton models to handle problems wth uncertan marketng data and parameters whch cannot be accurately estmated. Robust optmzaton s a developng lterature body recently, whch plays an mportant role n revenue management (see [5]). Robust optmzaton s a method for modelng optmzaton problems under uncertanty, to fnd optmal decsons for the worst-case realzaton of the uncertantes wthn a gven set. Typcally, the orgnal uncertan optmzaton problem s converted nto an equvalent determnstc form (called the robust counterpart) usng strong dualty arguments and then solved usng standard optmzaton algorthms. Soyster [6], Ben-Tal and Nemrovsk [7], and Bertsmas and Sm [8] descrbe the method for constructng robust counterparts for uncertanty sets of varous structures. Robust optmzaton has been appled n varous felds ncludng supply chan management, fnance, and logstcs. Goh and Sm [8] develop ROME, an algebrac modelng toolbox desgned to solve a class of robust optmzaton problems n the MATLAB envronment, to solve applcaton problems. AIMMS provdes robust optmzaton add-on, wth the ablty of handlng wth mult-perods optmzaton problems wthout fallng nto the trap of the curse of dmensonalty. Revenue management (see [0]) s a man concern of publc storage warehouses. Robust optmzaton plays an mportant role n revenue management

3 (see [4]). We optmze the expected revenue of publc storage warehouses aganst the worst cases wth a max-mn revenue objectve, and the decson varables are manly the number of storage unts for each storage type. Ths research s manly n the feld of faclty desgn (see []). Rosenblat and Lee[2] propose a robust approach to faclty desgn. Ths paper makes contrbuton to faclty plannng (see [] ) and proposes a faclty desgn method to handle rsks and uncertantes and wth an objectve to ncrease revenue. 2 Models The man research problem s to determne how many storage type unts should be constructed to ft demand. There are m dfferent storage type unts wth a specfc nteger storage sze area c, m. We assume that the random arrval processes of customers requestng a storage type unt, mare ndependent Posson processes wth arrval rates λ and that the occupaton tmes of a storage type unt are ndependent and dentcally dstrbuted random varables wth expected occupaton tme β. Customers requestng a storage type unt are called type customers. When a type customer arrves and storage type unts are not fully occuped, the customer wll be accepted at a prce of r per perod for a storage type unt. When all storage type unts are occuped, customers who ntally are nterested n a storage type unt, may accept wth probablty p to pay a prce of r + for a storage type + unt. A customer wllng to accept ths s called an upscaled customer. If a storage type + unt s avalable, an upscaled customer wll be served, otherwse the customer s lost. Let x, mbe the number of storage type unts bult for type customers at level and y, m the type + unts reserved for type customers upscaled to level +. A type customer who fnds upon arrval all the x storage type unts occuped may choose to be upscaled and use one of the y reserved unts, f one s avalable. If all y unts are occuped, the customer s lost. Customers of type m fndng upon arrval all xm unts busy are drectly lost. A customer of type + s not allowed to occupy one of the y unts reserved for upgraded type customers. Each rejected type customer s wllng to upscale wth probablty p.

4 To analyze ths model we frst look at the process followed by each type of customers separately. The long-run average revenue obtaned from type customers can be splt n the long-run average revenue obtaned from the x and y unts. The number of occuped storage type unts among the avalable x can agan be modeled by a queueng loss system. Due to our assumpton of exponentally dstrbuted occupaton tmes ths s an M / M / x / x loss queue. The long-run average revenue obtaned from the x unts s gven by long-run average revenue obtaned from the y unts s m rη+ ( x) β( Pr( x, y) ), where η ( x ) p (, ) λ B x ρ = m rρ ( B( x, ρ )). The = + = and r(, ) P x y the rejecton probablty that an upscaled type customer wll fnd all the reserved y unts occuped. Let Λ be the set of demand data for all storage types of D quarters n the examnng years, we calculate the worst performance as m m mn λ rρ ( B( x, ρ )) + rη ( x ) β P x, y ( ( )) d Δ, d=,..., D + r = = Gven the prce r for each storage type unt per unt of tme, the motvaton of robust desgn s to mnmze the loss n revenue due to varaton n demand, and to decde how many unts of each type to buld (and reserve) such that we can fnd the best one among the worst performances, and we present a robust model as follows, m m max mn λ,,..., ( (, )) ( ),... d Δ d= D rρ B x ρ + rη+ x β Pr x y R = = m st.. ( cx + c y) + c x C = x, y Z + m m + ( ( )) ( ) 3 Analyss We apply the followng random search robust optmzaton algorthm to calculate the desgn and solve model (R).

5 Algorthm: robust optmzaton for PS warehouses (I) For t = : T. (I.) Generate a sample constrant m cx C. = t x from (, ) U x x wth consderaton of the capacty (I.2)(A) For d = : D, compute the objectve value R ( x t ) from the pattern. (I.2)(B)Compute the worst performance of ths generated desgn t t patterns R mn ( Rd ( x ), d : D) (II)Compute R = max ( R t, t = : T) = =. (III) Return x correspondng to R as the robust desgn. d th d demand t x n D demand We take the S.P. Rotterdam warehouse as an example, to llustrate how to provde a robust desgn, such that a warehouse can acheve the best revenue performance among the worst revenues from demand data of D quarters. Table : Parameters of S.P. Rotterdam warehouse Items Class Class 2 Class 3 Class 4 Class 5 Class 6 Class 7 Class 8 Types(m 2 ) Prce (Euro) Old desgn We use optmal results from Table 3 n [3], whch s by a senstvty analyss of S.P. Rotterdam warehouse desgn based on data of 8 quarters, to construct the search range ( x, y),( x, y ) for problem (R). For teratons t = : T, we generate τ t samples ( x, y ), τ =,...,t from the search range whle consderng the capacty constrant. For each of the samples ( x, y) τ we compute ts revenues for D dfferent demand patterns, and obtan the worst value τ R = mn { R (( ), ),,..., d x, y d d D} τ λ = for ths desgn sample. The optmal revenue correspondng to the robust desgn s now Rt ( ) = max { R τ, τ =,..., t} for teraton t. We can then return the ( x, y) value correspondng to R( t) as the robust desgn for teraton t. The theoretcal optmal robust revenue s R = lm Rt ( ). t

6 We present the searchng process { R(), t t : T} = n Fg.-. By settng the convergence crteron as Rt ( + ) Rt ( ) Rt ( ) 0.3%, we observe the search converges n the last 500 teratons. We therefore calculate the average value from the last 500 values { Rt ( ), t= 2500 : 3000}, and obtan the robust optmal revenue value as 4392, whch s a sgnfcant mprovement compared wth the current worst revenue value based on the current desgn. We use the desgn obtaned after 3000 teratons {50(6), 9(5), 9(6), 46(0), 2(5), 25(5), 9(4), 9(0)} as the robust desgn. Fgure : Robust optmzaton for S.P. Rotterdam warehouse 4 Concludng remarks Ths paper presents a robust desgn method for publc storage warehouses by applyng robust optmzaton and revenue management. In regard to avalable nformaton, t needs to further consder two stuatons: () Some warehouses mantan ncomplete market data. They cannot accurately estmate customer demand, ncludng average order arrval rates. But they can roughly provde a scope of customer demand by hstory data. Ths happens n small and mddle faclty provders and some large-scale companes wth relatvely unqualfed nformaton management. (2) Some warehouses can provde more market data. They can estmate customer demand nformaton, ncludng average order arrval rates. They cannot accurately specfy some parameters of ther market demand models; nstead

7 they can roughly provde a scope of parameters by hstorcal data. Ths happens n some world-class faclty provders (lke Shurgard) and some mddle-scale companes wth good nformaton management systems. Methods and technologes of robust optmzaton need mprovement n the further research. The lmtaton of random search robust optmzaton algorthm s n searchng speed and accuracy. It s nterestng to explore better algorthms for robust desgn of large scale publc storage warehouses. Acknowledgements Yemng Gong acknowledges the support from NSFC ( ). References [] Self Storage Assocaton. Self Storage Assocaton Fact Sheet, Alexandra, VA, USA (20). [2] Self Storage Assocaton UK, Annual Industry Report, Self Storage Assocaton of Unted Kngdom, Cheshre, UK (2008). [3] Gong, Y. de Koster, R., Gabor, A., Frenk, J.B.G., Increasng revenue of selfstorage warehouses by faclty desgn, EMLYON workng paper (20). [4]Gong Y. Stochastc modellng and analyss of warehouse operatons, Erasmus Unversty, Rotterdam, the Netherlands (2009). [5]Brbl, S.I, Frenk, J.B.G., Gromcho, J.A.S., Zhang, S. The role of robust optmzaton n sngle-leg arlne revenue management. Management Scence, 55,, (2008). [6]Soyster, A. L. Convex programmng wth set-nclusve constrants and applcatons to nexact lnear programmng. Operatons Research, 2, 5, 54 57(973). [7]Ben-Tal, A. and Nemrovsk. A. Robust convex optmzaton. Mathematcs of Operatons Research, 23, 4, (998). [8]Bertsmas, D. and Sm M.. The prce of robustness. Operatons Research, 52,, 35 53(2004).

8 [9]Goh J. and Sm M.. Robust Optmzaton Made Easy wth ROME. Operatons Research, 59(4), (20). [0]Tallur, K.T., Van Ryzn, G.J. The theory and practce of revenue management. Kluwer, Boston (2004). []Tompkns, J.A., Whte, J.A., Bozer, Y.A., Tanchoco, J.M.A. Facltes plannng. John Wley & Sons, USA (2003). [2]Rosenblat, M.J. and Lee H.L. A robust approach to faclty desgn. Internatonal Journal of Producton Research, 25(4), (987).

Power-of-Two Policies for Single- Warehouse Multi-Retailer Inventory Systems with Order Frequency Discounts

Power-of-Two Policies for Single- Warehouse Multi-Retailer Inventory Systems with Order Frequency Discounts Power-of-wo Polces for Sngle- Warehouse Mult-Retaler Inventory Systems wth Order Frequency Dscounts José A. Ventura Pennsylvana State Unversty (USA) Yale. Herer echnon Israel Insttute of echnology (Israel)

More information

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of

More information

Fault tolerance in cloud technologies presented as a service

Fault tolerance in cloud technologies presented as a service Internatonal Scentfc Conference Computer Scence 2015 Pavel Dzhunev, PhD student Fault tolerance n cloud technologes presented as a servce INTRODUCTION Improvements n technques for vrtualzaton and performance

More information

Optimal Bidding Strategies for Generation Companies in a Day-Ahead Electricity Market with Risk Management Taken into Account

Optimal Bidding Strategies for Generation Companies in a Day-Ahead Electricity Market with Risk Management Taken into Account Amercan J. of Engneerng and Appled Scences (): 8-6, 009 ISSN 94-700 009 Scence Publcatons Optmal Bddng Strateges for Generaton Companes n a Day-Ahead Electrcty Market wth Rsk Management Taken nto Account

More information

Project Networks With Mixed-Time Constraints

Project Networks With Mixed-Time Constraints Project Networs Wth Mxed-Tme Constrants L Caccetta and B Wattananon Western Australan Centre of Excellence n Industral Optmsaton (WACEIO) Curtn Unversty of Technology GPO Box U1987 Perth Western Australa

More information

Credit Limit Optimization (CLO) for Credit Cards

Credit Limit Optimization (CLO) for Credit Cards Credt Lmt Optmzaton (CLO) for Credt Cards Vay S. Desa CSCC IX, Ednburgh September 8, 2005 Copyrght 2003, SAS Insttute Inc. All rghts reserved. SAS Propretary Agenda Background Tradtonal approaches to credt

More information

A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION. Michael E. Kuhl Radhamés A. Tolentino-Peña

A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION. Michael E. Kuhl Radhamés A. Tolentino-Peña Proceedngs of the 2008 Wnter Smulaton Conference S. J. Mason, R. R. Hll, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION

More information

SIX WAYS TO SOLVE A SIMPLE PROBLEM: FITTING A STRAIGHT LINE TO MEASUREMENT DATA

SIX WAYS TO SOLVE A SIMPLE PROBLEM: FITTING A STRAIGHT LINE TO MEASUREMENT DATA SIX WAYS TO SOLVE A SIMPLE PROBLEM: FITTING A STRAIGHT LINE TO MEASUREMENT DATA E. LAGENDIJK Department of Appled Physcs, Delft Unversty of Technology Lorentzweg 1, 68 CJ, The Netherlands E-mal: e.lagendjk@tnw.tudelft.nl

More information

Marginal Revenue-Based Capacity Management Models and Benchmark 1

Marginal Revenue-Based Capacity Management Models and Benchmark 1 Margnal Revenue-Based Capacty Management Models and Benchmark 1 Qwen Wang 2 Guanghua School of Management, Pekng Unversty Sherry Xaoyun Sun 3 Ctgroup ABSTRACT To effcently meet customer requrements, a

More information

Solutions to the exam in SF2862, June 2009

Solutions to the exam in SF2862, June 2009 Solutons to the exam n SF86, June 009 Exercse 1. Ths s a determnstc perodc-revew nventory model. Let n = the number of consdered wees,.e. n = 4 n ths exercse, and r = the demand at wee,.e. r 1 = r = r

More information

1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP)

1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP) 6.3 / -- Communcaton Networks II (Görg) SS20 -- www.comnets.un-bremen.de Communcaton Networks II Contents. Fundamentals of probablty theory 2. Emergence of communcaton traffc 3. Stochastc & Markovan Processes

More information

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange

More information

The OC Curve of Attribute Acceptance Plans

The OC Curve of Attribute Acceptance Plans The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4

More information

A method for a robust optimization of joint product and supply chain design

A method for a robust optimization of joint product and supply chain design DOI 10.1007/s10845-014-0908-5 A method for a robust optmzaton of jont product and supply chan desgn Bertrand Baud-Lavgne Samuel Bassetto Bruno Agard Receved: 10 September 2013 / Accepted: 21 March 2014

More information

The Greedy Method. Introduction. 0/1 Knapsack Problem

The Greedy Method. Introduction. 0/1 Knapsack Problem The Greedy Method Introducton We have completed data structures. We now are gong to look at algorthm desgn methods. Often we are lookng at optmzaton problems whose performance s exponental. For an optmzaton

More information

Simulation and optimization of supply chains: alternative or complementary approaches?

Simulation and optimization of supply chains: alternative or complementary approaches? Smulaton and optmzaton of supply chans: alternatve or complementary approaches? Chrstan Almeder Margaretha Preusser Rchard F. Hartl Orgnally publshed n: OR Spectrum (2009) 31:95 119 DOI 10.1007/s00291-007-0118-z

More information

A hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm

A hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(7):1884-1889 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 A hybrd global optmzaton algorthm based on parallel

More information

Performance Analysis of Energy Consumption of Smartphone Running Mobile Hotspot Application

Performance Analysis of Energy Consumption of Smartphone Running Mobile Hotspot Application Internatonal Journal of mart Grd and lean Energy Performance Analyss of Energy onsumpton of martphone Runnng Moble Hotspot Applcaton Yun on hung a chool of Electronc Engneerng, oongsl Unversty, 511 angdo-dong,

More information

Communication Networks II Contents

Communication Networks II Contents 8 / 1 -- Communcaton Networs II (Görg) -- www.comnets.un-bremen.de Communcaton Networs II Contents 1 Fundamentals of probablty theory 2 Traffc n communcaton networs 3 Stochastc & Marovan Processes (SP

More information

Portfolio Loss Distribution

Portfolio Loss Distribution Portfolo Loss Dstrbuton Rsky assets n loan ortfolo hghly llqud assets hold-to-maturty n the bank s balance sheet Outstandngs The orton of the bank asset that has already been extended to borrowers. Commtment

More information

Risk-based Fatigue Estimate of Deep Water Risers -- Course Project for EM388F: Fracture Mechanics, Spring 2008

Risk-based Fatigue Estimate of Deep Water Risers -- Course Project for EM388F: Fracture Mechanics, Spring 2008 Rsk-based Fatgue Estmate of Deep Water Rsers -- Course Project for EM388F: Fracture Mechancs, Sprng 2008 Chen Sh Department of Cvl, Archtectural, and Envronmental Engneerng The Unversty of Texas at Austn

More information

Multivariate EWMA Control Chart

Multivariate EWMA Control Chart Multvarate EWMA Control Chart Summary The Multvarate EWMA Control Chart procedure creates control charts for two or more numerc varables. Examnng the varables n a multvarate sense s extremely mportant

More information

9.1 The Cumulative Sum Control Chart

9.1 The Cumulative Sum Control Chart Learnng Objectves 9.1 The Cumulatve Sum Control Chart 9.1.1 Basc Prncples: Cusum Control Chart for Montorng the Process Mean If s the target for the process mean, then the cumulatve sum control chart s

More information

J. Parallel Distrib. Comput.

J. Parallel Distrib. Comput. J. Parallel Dstrb. Comput. 71 (2011) 62 76 Contents lsts avalable at ScenceDrect J. Parallel Dstrb. Comput. journal homepage: www.elsever.com/locate/jpdc Optmzng server placement n dstrbuted systems n

More information

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..

More information

Recurrence. 1 Definitions and main statements

Recurrence. 1 Definitions and main statements Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.

More information

An Analysis of Central Processor Scheduling in Multiprogrammed Computer Systems

An Analysis of Central Processor Scheduling in Multiprogrammed Computer Systems STAN-CS-73-355 I SU-SE-73-013 An Analyss of Central Processor Schedulng n Multprogrammed Computer Systems (Dgest Edton) by Thomas G. Prce October 1972 Techncal Report No. 57 Reproducton n whole or n part

More information

Allocating Time and Resources in Project Management Under Uncertainty

Allocating Time and Resources in Project Management Under Uncertainty Proceedngs of the 36th Hawa Internatonal Conference on System Scences - 23 Allocatng Tme and Resources n Project Management Under Uncertanty Mark A. Turnqust School of Cvl and Envronmental Eng. Cornell

More information

Risk Model of Long-Term Production Scheduling in Open Pit Gold Mining

Risk Model of Long-Term Production Scheduling in Open Pit Gold Mining Rsk Model of Long-Term Producton Schedulng n Open Pt Gold Mnng R Halatchev 1 and P Lever 2 ABSTRACT Open pt gold mnng s an mportant sector of the Australan mnng ndustry. It uses large amounts of nvestments,

More information

Applications of Nonlinear Models in Contribution Margin and Profit Analysis

Applications of Nonlinear Models in Contribution Margin and Profit Analysis Applcatons of Nonlnear Models n Contrbuton Margn and Proft Analyss ABSTRACT D Wu Calforna State Unversty-Bakersfeld J L Calforna State Unversty-Bakersfeld Management of a company often needs to make busness

More information

On the Optimal Control of a Cascade of Hydro-Electric Power Stations

On the Optimal Control of a Cascade of Hydro-Electric Power Stations On the Optmal Control of a Cascade of Hydro-Electrc Power Statons M.C.M. Guedes a, A.F. Rbero a, G.V. Smrnov b and S. Vlela c a Department of Mathematcs, School of Scences, Unversty of Porto, Portugal;

More information

The covariance is the two variable analog to the variance. The formula for the covariance between two variables is

The covariance is the two variable analog to the variance. The formula for the covariance between two variables is Regresson Lectures So far we have talked only about statstcs that descrbe one varable. What we are gong to be dscussng for much of the remander of the course s relatonshps between two or more varables.

More information

An Alternative Way to Measure Private Equity Performance

An Alternative Way to Measure Private Equity Performance An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate

More information

Dynamic Pricing for Smart Grid with Reinforcement Learning

Dynamic Pricing for Smart Grid with Reinforcement Learning Dynamc Prcng for Smart Grd wth Renforcement Learnng Byung-Gook Km, Yu Zhang, Mhaela van der Schaar, and Jang-Won Lee Samsung Electroncs, Suwon, Korea Department of Electrcal Engneerng, UCLA, Los Angeles,

More information

Cross-Selling in a Call Center with a Heterogeneous Customer Population

Cross-Selling in a Call Center with a Heterogeneous Customer Population OPERATIONS RESEARCH Vol. 57, No. 2, March Aprl 2009, pp. 299 313 ssn 0030-364X essn 1526-5463 09 5702 0299 nforms do 10.1287/opre.1080.0568 2009 INFORMS Cross-Sellng n a Call Center wth a Heterogeneous

More information

Support Vector Machines

Support Vector Machines Support Vector Machnes Max Wellng Department of Computer Scence Unversty of Toronto 10 Kng s College Road Toronto, M5S 3G5 Canada wellng@cs.toronto.edu Abstract Ths s a note to explan support vector machnes.

More information

Preventive Maintenance and Replacement Scheduling: Models and Algorithms

Preventive Maintenance and Replacement Scheduling: Models and Algorithms Preventve Mantenance and Replacement Schedulng: Models and Algorthms By Kamran S. Moghaddam B.S. Unversty of Tehran 200 M.S. Tehran Polytechnc 2003 A Dssertaton Proposal Submtted to the Faculty of the

More information

Optimization under uncertainty. Antonio J. Conejo The Ohio State University 2014

Optimization under uncertainty. Antonio J. Conejo The Ohio State University 2014 Optmzaton under uncertant Antono J. Conejo The Oho State Unverst 2014 Contents Stochastc programmng (SP) Robust optmzaton (RO) Power sstem applcatons A. J. Conejo The Oho State Unverst 2 Stochastc Programmng

More information

Research Article Enhanced Two-Step Method via Relaxed Order of α-satisfactory Degrees for Fuzzy Multiobjective Optimization

Research Article Enhanced Two-Step Method via Relaxed Order of α-satisfactory Degrees for Fuzzy Multiobjective Optimization Hndaw Publshng Corporaton Mathematcal Problems n Engneerng Artcle ID 867836 pages http://dxdoorg/055/204/867836 Research Artcle Enhanced Two-Step Method va Relaxed Order of α-satsfactory Degrees for Fuzzy

More information

An MILP model for planning of batch plants operating in a campaign-mode

An MILP model for planning of batch plants operating in a campaign-mode An MILP model for plannng of batch plants operatng n a campagn-mode Yanna Fumero Insttuto de Desarrollo y Dseño CONICET UTN yfumero@santafe-concet.gov.ar Gabrela Corsano Insttuto de Desarrollo y Dseño

More information

Efficient Project Portfolio as a tool for Enterprise Risk Management

Efficient Project Portfolio as a tool for Enterprise Risk Management Effcent Proect Portfolo as a tool for Enterprse Rsk Management Valentn O. Nkonov Ural State Techncal Unversty Growth Traectory Consultng Company January 5, 27 Effcent Proect Portfolo as a tool for Enterprse

More information

A Computer Technique for Solving LP Problems with Bounded Variables

A Computer Technique for Solving LP Problems with Bounded Variables Dhaka Unv. J. Sc. 60(2): 163-168, 2012 (July) A Computer Technque for Solvng LP Problems wth Bounded Varables S. M. Atqur Rahman Chowdhury * and Sanwar Uddn Ahmad Department of Mathematcs; Unversty of

More information

Research Article A Time Scheduling Model of Logistics Service Supply Chain with Mass Customized Logistics Service

Research Article A Time Scheduling Model of Logistics Service Supply Chain with Mass Customized Logistics Service Hndaw Publshng Corporaton Dscrete Dynamcs n Nature and Socety Volume 01, Artcle ID 48978, 18 pages do:10.1155/01/48978 Research Artcle A Tme Schedulng Model of Logstcs Servce Supply Chan wth Mass Customzed

More information

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.

More information

2008/8. An integrated model for warehouse and inventory planning. Géraldine Strack and Yves Pochet

2008/8. An integrated model for warehouse and inventory planning. Géraldine Strack and Yves Pochet 2008/8 An ntegrated model for warehouse and nventory plannng Géraldne Strack and Yves Pochet CORE Voe du Roman Pays 34 B-1348 Louvan-la-Neuve, Belgum. Tel (32 10) 47 43 04 Fax (32 10) 47 43 01 E-mal: corestat-lbrary@uclouvan.be

More information

Capacity Reservation for Time-Sensitive Service Providers: An Application in Seaport Management

Capacity Reservation for Time-Sensitive Service Providers: An Application in Seaport Management Capacty Reservaton for Tme-Senstve Servce Provders: An Applcaton n Seaport Management L. Jeff Hong Department of Industral Engneerng and Logstcs Management The Hong Kong Unversty of Scence and Technology

More information

Cross-Selling in a Call Center with a Heterogeneous Customer Population

Cross-Selling in a Call Center with a Heterogeneous Customer Population OPERATIONS RESEARCH Vol. 57, No. 2, March Aprl 29, pp. 299 313 ssn 3-364X essn 1526-5463 9 572 299 nforms do 1.1287/opre.18.568 29 INFORMS Cross-Sellng n a Call Center wth a Heterogeneous Customer Populaton

More information

Nasdaq Iceland Bond Indices 01 April 2015

Nasdaq Iceland Bond Indices 01 April 2015 Nasdaq Iceland Bond Indces 01 Aprl 2015 -Fxed duraton Indces Introducton Nasdaq Iceland (the Exchange) began calculatng ts current bond ndces n the begnnng of 2005. They were a response to recent changes

More information

Number of Levels Cumulative Annual operating Income per year construction costs costs ($) ($) ($) 1 600,000 35,000 100,000 2 2,200,000 60,000 350,000

Number of Levels Cumulative Annual operating Income per year construction costs costs ($) ($) ($) 1 600,000 35,000 100,000 2 2,200,000 60,000 350,000 Problem Set 5 Solutons 1 MIT s consderng buldng a new car park near Kendall Square. o unversty funds are avalable (overhead rates are under pressure and the new faclty would have to pay for tself from

More information

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ). REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or

More information

Forecasting the Direction and Strength of Stock Market Movement

Forecasting the Direction and Strength of Stock Market Movement Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract - Stock market s one of the most complcated systems

More information

Case Study: Load Balancing

Case Study: Load Balancing Case Study: Load Balancng Thursday, 01 June 2006 Bertol Marco A.A. 2005/2006 Dmensonamento degl mpant Informatc LoadBal - 1 Introducton Optmze the utlzaton of resources to reduce the user response tme

More information

The Application of Fractional Brownian Motion in Option Pricing

The Application of Fractional Brownian Motion in Option Pricing Vol. 0, No. (05), pp. 73-8 http://dx.do.org/0.457/jmue.05.0..6 The Applcaton of Fractonal Brownan Moton n Opton Prcng Qng-xn Zhou School of Basc Scence,arbn Unversty of Commerce,arbn zhouqngxn98@6.com

More information

Chapter 7. Random-Variate Generation 7.1. Prof. Dr. Mesut Güneş Ch. 7 Random-Variate Generation

Chapter 7. Random-Variate Generation 7.1. Prof. Dr. Mesut Güneş Ch. 7 Random-Variate Generation Chapter 7 Random-Varate Generaton 7. Contents Inverse-transform Technque Acceptance-Rejecton Technque Specal Propertes 7. Purpose & Overvew Develop understandng of generatng samples from a specfed dstrbuton

More information

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 610-519-4390,

More information

Feature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College

Feature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College Feature selecton for ntruson detecton Slobodan Petrovć NISlab, Gjøvk Unversty College Contents The feature selecton problem Intruson detecton Traffc features relevant for IDS The CFS measure The mrmr measure

More information

Brigid Mullany, Ph.D University of North Carolina, Charlotte

Brigid Mullany, Ph.D University of North Carolina, Charlotte Evaluaton And Comparson Of The Dfferent Standards Used To Defne The Postonal Accuracy And Repeatablty Of Numercally Controlled Machnng Center Axes Brgd Mullany, Ph.D Unversty of North Carolna, Charlotte

More information

Survey on Virtual Machine Placement Techniques in Cloud Computing Environment

Survey on Virtual Machine Placement Techniques in Cloud Computing Environment Survey on Vrtual Machne Placement Technques n Cloud Computng Envronment Rajeev Kumar Gupta and R. K. Paterya Department of Computer Scence & Engneerng, MANIT, Bhopal, Inda ABSTRACT In tradtonal data center

More information

Dynamic optimization of the LNG value chain

Dynamic optimization of the LNG value chain Proceedngs of the 1 st Annual Gas Processng Symposum H. Alfadala, G.V. Rex Reklats and M.M. El-Halwag (Edtors) 2009 Elsever B.V. All rghts reserved. 1 Dynamc optmzaton of the LNG value chan Bjarne A. Foss

More information

Aryabhata s Root Extraction Methods. Abhishek Parakh Louisiana State University Aug 31 st 2006

Aryabhata s Root Extraction Methods. Abhishek Parakh Louisiana State University Aug 31 st 2006 Aryabhata s Root Extracton Methods Abhshek Parakh Lousana State Unversty Aug 1 st 1 Introducton Ths artcle presents an analyss of the root extracton algorthms of Aryabhata gven n hs book Āryabhatīya [1,

More information

Can Auto Liability Insurance Purchases Signal Risk Attitude?

Can Auto Liability Insurance Purchases Signal Risk Attitude? Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang

More information

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression Novel Methodology of Workng Captal Management for Large Publc Constructons by Usng Fuzzy S-curve Regresson Cheng-Wu Chen, Morrs H. L. Wang and Tng-Ya Hseh Department of Cvl Engneerng, Natonal Central Unversty,

More information

Optimal Customized Pricing in Competitive Settings

Optimal Customized Pricing in Competitive Settings Optmal Customzed Prcng n Compettve Settngs Vshal Agrawal Industral & Systems Engneerng, Georga Insttute of Technology, Atlanta, Georga 30332 vshalagrawal@gatech.edu Mark Ferguson College of Management,

More information

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy 4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.

More information

Quality Adjustment of Second-hand Motor Vehicle Application of Hedonic Approach in Hong Kong s Consumer Price Index

Quality Adjustment of Second-hand Motor Vehicle Application of Hedonic Approach in Hong Kong s Consumer Price Index Qualty Adustment of Second-hand Motor Vehcle Applcaton of Hedonc Approach n Hong Kong s Consumer Prce Index Prepared for the 14 th Meetng of the Ottawa Group on Prce Indces 20 22 May 2015, Tokyo, Japan

More information

Optimal portfolios using Linear Programming models

Optimal portfolios using Linear Programming models Optmal portfolos usng Lnear Programmng models Chrstos Papahrstodoulou Mälardalen Unversty, Västerås, Sweden Abstract The classcal Quadratc Programmng formulaton of the well known portfolo selecton problem,

More information

On File Delay Minimization for Content Uploading to Media Cloud via Collaborative Wireless Network

On File Delay Minimization for Content Uploading to Media Cloud via Collaborative Wireless Network On Fle Delay Mnmzaton for Content Uploadng to Meda Cloud va Collaboratve Wreless Network Ge Zhang and Yonggang Wen School of Computer Engneerng Nanyang Technologcal Unversty Sngapore Emal: {zh0001ge, ygwen}@ntu.edu.sg

More information

I. SCOPE, APPLICABILITY AND PARAMETERS Scope

I. SCOPE, APPLICABILITY AND PARAMETERS Scope D Executve Board Annex 9 Page A/R ethodologcal Tool alculaton of the number of sample plots for measurements wthn A/R D project actvtes (Verson 0) I. SOPE, PIABIITY AD PARAETERS Scope. Ths tool s applcable

More information

Abstract. 260 Business Intelligence Journal July IDENTIFICATION OF DEMAND THROUGH STATISTICAL DISTRIBUTION MODELING FOR IMPROVED DEMAND FORECASTING

Abstract. 260 Business Intelligence Journal July IDENTIFICATION OF DEMAND THROUGH STATISTICAL DISTRIBUTION MODELING FOR IMPROVED DEMAND FORECASTING 260 Busness Intellgence Journal July IDENTIFICATION OF DEMAND THROUGH STATISTICAL DISTRIBUTION MODELING FOR IMPROVED DEMAND FORECASTING Murphy Choy Mchelle L.F. Cheong School of Informaton Systems, Sngapore

More information

1 Approximation Algorithms

1 Approximation Algorithms CME 305: Dscrete Mathematcs and Algorthms 1 Approxmaton Algorthms In lght of the apparent ntractablty of the problems we beleve not to le n P, t makes sense to pursue deas other than complete solutons

More information

Price Competition in an Oligopoly Market with Multiple IaaS Cloud Providers

Price Competition in an Oligopoly Market with Multiple IaaS Cloud Providers Prce Competton n an Olgopoly Market wth Multple IaaS Cloud Provders Yuan Feng, Baochun L, Bo L Department of Computng, Hong Kong Polytechnc Unversty Department of Electrcal and Computer Engneerng, Unversty

More information

Overview of monitoring and evaluation

Overview of monitoring and evaluation 540 Toolkt to Combat Traffckng n Persons Tool 10.1 Overvew of montorng and evaluaton Overvew Ths tool brefly descrbes both montorng and evaluaton, and the dstncton between the two. What s montorng? Montorng

More information

Enabling P2P One-view Multi-party Video Conferencing

Enabling P2P One-view Multi-party Video Conferencing Enablng P2P One-vew Mult-party Vdeo Conferencng Yongxang Zhao, Yong Lu, Changja Chen, and JanYn Zhang Abstract Mult-Party Vdeo Conferencng (MPVC) facltates realtme group nteracton between users. Whle P2P

More information

Data Broadcast on a Multi-System Heterogeneous Overlayed Wireless Network *

Data Broadcast on a Multi-System Heterogeneous Overlayed Wireless Network * JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 24, 819-840 (2008) Data Broadcast on a Mult-System Heterogeneous Overlayed Wreless Network * Department of Computer Scence Natonal Chao Tung Unversty Hsnchu,

More information

Optimizing Spare Parts Inventory for Time-Varying Task

Optimizing Spare Parts Inventory for Time-Varying Task A publcaton of CHEMCAL ENGNEERNG TRANSACTONS VOL. 33, 203 Guest Edtors: Enrco Zo, Pero Barald Copyrght 203, ADC Servz S.r.l., SBN 978-88-95608-24-2; SSN 974-979 The talan Assocaton of Chemcal Engneerng

More information

EVERY year, seasonal hurricanes threaten coastal areas.

EVERY year, seasonal hurricanes threaten coastal areas. 1 Strategc Stockplng of Power System Supples for Dsaster Recovery Carleton Coffrn, Pascal Van Hentenryck, and Russell Bent Abstract Ths paper studes the Power System Stochastc Storage Problem (PSSSP),

More information

What is Candidate Sampling

What is Candidate Sampling What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble

More information

Multiple-Period Attribution: Residuals and Compounding

Multiple-Period Attribution: Residuals and Compounding Multple-Perod Attrbuton: Resduals and Compoundng Our revewer gave these authors full marks for dealng wth an ssue that performance measurers and vendors often regard as propretary nformaton. In 1994, Dens

More information

Planning for Marketing Campaigns

Planning for Marketing Campaigns Plannng for Marketng Campagns Qang Yang and Hong Cheng Department of Computer Scence Hong Kong Unversty of Scence and Technology Clearwater Bay, Kowloon, Hong Kong, Chna (qyang, csch)@cs.ust.hk Abstract

More information

The Current Employment Statistics (CES) survey,

The Current Employment Statistics (CES) survey, Busness Brths and Deaths Impact of busness brths and deaths n the payroll survey The CES probablty-based sample redesgn accounts for most busness brth employment through the mputaton of busness deaths,

More information

Stochastic Inventory Management for Tactical Process Planning under Uncertainties: MINLP Models and Algorithms

Stochastic Inventory Management for Tactical Process Planning under Uncertainties: MINLP Models and Algorithms Stochastc Inventory Management for Tactcal Process Plannng under Uncertantes: MINLP Models and Algorthms Fengq You, Ignaco E. Grossmann Department of Chemcal Engneerng, Carnege Mellon Unversty Pttsburgh,

More information

Determination of Integrated Risk Degrees in Product Development Project

Determination of Integrated Risk Degrees in Product Development Project Proceedngs of the World Congress on Engneerng and Computer Scence 009 Vol II WCECS 009, October 0-, 009, San Francsco, USA Determnaton of Integrated sk Degrees n Product Development Project D. W. Cho.,

More information

PAS: A Packet Accounting System to Limit the Effects of DoS & DDoS. Debish Fesehaye & Klara Naherstedt University of Illinois-Urbana Champaign

PAS: A Packet Accounting System to Limit the Effects of DoS & DDoS. Debish Fesehaye & Klara Naherstedt University of Illinois-Urbana Champaign PAS: A Packet Accountng System to Lmt the Effects of DoS & DDoS Debsh Fesehaye & Klara Naherstedt Unversty of Illnos-Urbana Champagn DoS and DDoS DDoS attacks are ncreasng threats to our dgtal world. Exstng

More information

ORDER ALLOCATION FOR SERVICE SUPPLY CHAIN BASE ON THE CUSTOMER BEST DELIVERY TIME UNDER THE BACKGROUND OF BIG DATA

ORDER ALLOCATION FOR SERVICE SUPPLY CHAIN BASE ON THE CUSTOMER BEST DELIVERY TIME UNDER THE BACKGROUND OF BIG DATA Internatonal Journal of Computer Scence and Applcatons, Technomathematcs Research Foundaton Vol. 13, No. 1, pp. 84 92, 2016 ORDER ALLOCATION FOR SERVICE SUPPLY CHAIN BASE ON THE CUSTOMER BEST DELIVERY

More information

Describing Communities. Species Diversity Concepts. Species Richness. Species Richness. Species-Area Curve. Species-Area Curve

Describing Communities. Species Diversity Concepts. Species Richness. Species Richness. Species-Area Curve. Species-Area Curve peces versty Concepts peces Rchness peces-area Curves versty Indces - mpson's Index - hannon-wener Index - rlloun Index peces Abundance Models escrbng Communtes There are two mportant descrptors of a communty:

More information

In some supply chains, materials are ordered periodically according to local information. This paper investigates

In some supply chains, materials are ordered periodically according to local information. This paper investigates MANUFACTURING & SRVIC OPRATIONS MANAGMNT Vol. 12, No. 3, Summer 2010, pp. 430 448 ssn 1523-4614 essn 1526-5498 10 1203 0430 nforms do 10.1287/msom.1090.0277 2010 INFORMS Improvng Supply Chan Performance:

More information

SIMULATION OPTIMIZATION: APPLICATIONS IN RISK MANAGEMENT

SIMULATION OPTIMIZATION: APPLICATIONS IN RISK MANAGEMENT Internatonal Journal of Informaton Technology & Decson Makng Vol. 7, No. 4 (2008) 571 587 c World Scentfc Publshng Company SIMULATION OPTIMIZATION: APPLICATIONS IN RISK MANAGEMENT MARCO BETTER and FRED

More information

Using Series to Analyze Financial Situations: Present Value

Using Series to Analyze Financial Situations: Present Value 2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated

More information

AN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE

AN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE AN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE Yu-L Huang Industral Engneerng Department New Mexco State Unversty Las Cruces, New Mexco 88003, U.S.A. Abstract Patent

More information

L10: Linear discriminants analysis

L10: Linear discriminants analysis L0: Lnear dscrmnants analyss Lnear dscrmnant analyss, two classes Lnear dscrmnant analyss, C classes LDA vs. PCA Lmtatons of LDA Varants of LDA Other dmensonalty reducton methods CSCE 666 Pattern Analyss

More information

Optimization Model of Reliable Data Storage in Cloud Environment Using Genetic Algorithm

Optimization Model of Reliable Data Storage in Cloud Environment Using Genetic Algorithm Internatonal Journal of Grd Dstrbuton Computng, pp.175-190 http://dx.do.org/10.14257/gdc.2014.7.6.14 Optmzaton odel of Relable Data Storage n Cloud Envronment Usng Genetc Algorthm Feng Lu 1,2,3, Hatao

More information

Joint Scheduling of Processing and Shuffle Phases in MapReduce Systems

Joint Scheduling of Processing and Shuffle Phases in MapReduce Systems Jont Schedulng of Processng and Shuffle Phases n MapReduce Systems Fangfe Chen, Mural Kodalam, T. V. Lakshman Department of Computer Scence and Engneerng, The Penn State Unversty Bell Laboratores, Alcatel-Lucent

More information

LITERATURE REVIEW: VARIOUS PRIORITY BASED TASK SCHEDULING ALGORITHMS IN CLOUD COMPUTING

LITERATURE REVIEW: VARIOUS PRIORITY BASED TASK SCHEDULING ALGORITHMS IN CLOUD COMPUTING LITERATURE REVIEW: VARIOUS PRIORITY BASED TASK SCHEDULING ALGORITHMS IN CLOUD COMPUTING 1 MS. POOJA.P.VASANI, 2 MR. NISHANT.S. SANGHANI 1 M.Tech. [Software Systems] Student, Patel College of Scence and

More information

A Novel Auction Mechanism for Selling Time-Sensitive E-Services

A Novel Auction Mechanism for Selling Time-Sensitive E-Services A ovel Aucton Mechansm for Sellng Tme-Senstve E-Servces Juong-Sk Lee and Boleslaw K. Szymansk Optmaret Inc. and Department of Computer Scence Rensselaer Polytechnc Insttute 110 8 th Street, Troy, Y 12180,

More information

行 政 院 國 家 科 學 委 員 會 補 助 專 題 研 究 計 畫 成 果 報 告 期 中 進 度 報 告

行 政 院 國 家 科 學 委 員 會 補 助 專 題 研 究 計 畫 成 果 報 告 期 中 進 度 報 告 行 政 院 國 家 科 學 委 員 會 補 助 專 題 研 究 計 畫 成 果 報 告 期 中 進 度 報 告 畫 類 別 : 個 別 型 計 畫 半 導 體 產 業 大 型 廠 房 之 設 施 規 劃 計 畫 編 號 :NSC 96-2628-E-009-026-MY3 執 行 期 間 : 2007 年 8 月 1 日 至 2010 年 7 月 31 日 計 畫 主 持 人 : 巫 木 誠 共 同

More information

Exhaustive Regression. An Exploration of Regression-Based Data Mining Techniques Using Super Computation

Exhaustive Regression. An Exploration of Regression-Based Data Mining Techniques Using Super Computation Exhaustve Regresson An Exploraton of Regresson-Based Data Mnng Technques Usng Super Computaton Antony Daves, Ph.D. Assocate Professor of Economcs Duquesne Unversty Pttsburgh, PA 58 Research Fellow The

More information

Fragility Based Rehabilitation Decision Analysis

Fragility Based Rehabilitation Decision Analysis .171. Fraglty Based Rehabltaton Decson Analyss Cagdas Kafal Graduate Student, School of Cvl and Envronmental Engneerng, Cornell Unversty Research Supervsor: rcea Grgoru, Professor Summary A method s presented

More information

PEER REVIEWER RECOMMENDATION IN ONLINE SOCIAL LEARNING CONTEXT: INTEGRATING INFORMATION OF LEARNERS AND SUBMISSIONS

PEER REVIEWER RECOMMENDATION IN ONLINE SOCIAL LEARNING CONTEXT: INTEGRATING INFORMATION OF LEARNERS AND SUBMISSIONS PEER REVIEWER RECOMMENDATION IN ONLINE SOCIAL LEARNING CONTEXT: INTEGRATING INFORMATION OF LEARNERS AND SUBMISSIONS Yunhong Xu, Faculty of Management and Economcs, Kunmng Unversty of Scence and Technology,

More information

Outsourcing inventory management decisions in healthcare: Models and application

Outsourcing inventory management decisions in healthcare: Models and application European Journal of Operatonal Research 154 (24) 271 29 O.R. Applcatons Outsourcng nventory management decsons n healthcare: Models and applcaton www.elsever.com/locate/dsw Lawrence Ncholson a, Asoo J.

More information

Feasibility of Using Discriminate Pricing Schemes for Energy Trading in Smart Grid

Feasibility of Using Discriminate Pricing Schemes for Energy Trading in Smart Grid Feasblty of Usng Dscrmnate Prcng Schemes for Energy Tradng n Smart Grd Wayes Tushar, Chau Yuen, Bo Cha, Davd B. Smth, and H. Vncent Poor Sngapore Unversty of Technology and Desgn, Sngapore 138682. Emal:

More information