A mathematical programming approach to the analysis, design and scheduling of offshore oilfields
|
|
- David Edwards
- 5 years ago
- Views:
Transcription
1 17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 A mathematcal programmng approach to the analyss, desgn and schedulng of offshore olfelds Rchard J. Barnes and Antons Kokosss Center for Process & Informaton Systems Engneerng School of Engneerng, Unversty of Surrey, Guldford, Surrey, GU2 7XH, U.K. E-mal: a.kokosss@surrey.ac.uk Abstract Ths paper presents a general and systematc approach to address decsons n the desgn and operaton of offshore olfelds. The approach s based on the formulaton of mathematcal models that are formulated to accommodate multple producton profles. The profles can be used to assess ether the best strategy or, nstead, possble mplcatons n changng polces durng the operaton. The work decomposes the problem n two stages: the determnaton of the optmum drllng centre and the determnaton of the optmum drllng schedule to meet a specfed producton profle. The proposed method smultaneously addresses and optmzes the operaton of the man producton faclty and an arbtrary number of satellte felds. Felds and wells are selected to gve the overall lowest CAPEX for the development. The method s an mprovement over prevous work and provdes a full optmsaton of lfe-cycle drllng costs. Keywords: Offshore olfeld; Optmsaton; Drllng; Offshore platform, Producton capacty, Lfe cycle cost, Economc analyss. 1. Introducton and problem descrpton Fgure 1 shows the general schematc of offshore feld comprsed by a man feld, F1 and three satellte felds, S1, S2 and S3. The optmum drllng centre s
2 2 Rchard J. Barnes et al. defned as the locaton whch has the lowest total cost of drllng suffcent wells that meet a specfed producton capacty. Such a locaton s affected by the layout of the feld, the depth, locaton and the productvty of ndvdual wells. Moreover, there s an optmum, that s lowest CAPEX, development scenaro n whch the felds are brought nto producton n a sequence where maxmum beneft s acheved from each new feld n order that the target producton profle s met at mnmum CAPEX [4]. S1 Producton or Satellte Platform F1 S2 S3 Ol and gas export to trunk The determnaton of the optmum drllng centre and the most economc producton profle for a feld or group of felds s a complex problem that s based on ncomplete and mprecse data. In order to prepare a robust soluton, t s necessary to nvestgate a large number of dfferent optons of locatons, drllng profles and lfe of feld producton profles. Prevous Fgure 1 Schematc of offshore feld work [1, 2] has presented a well optmsaton method to nvestgate parameters affectng the desgn capacty and the locaton of the man capacty. The work used a sngle heurstc profle for the well producton and a yearly schedulng model to determne drllng schedules and the tmng of satellte producton. Ths paper presents a general and systematc approach to address desgn decsons and support schedulng decsons over the entre horzon. The model s formulated to accommodate multple producton profles that can be used to assess ether the best strategy or possble mplcatons n changng polces durng the operaton. The work decomposes the problem n two stages: the determnaton of the optmum drllng centre and the determnaton of the optmum drllng schedule to meet a specfed producton profle. From ths nformaton an economc analyss of the lfe of the development may be made to gude the operator to the most economc method of developng the feld. It s assumed that there s suffcent knowledge of each reservor and that the potental down-hole well locatons can be defned n terms of three dmensonal coordnates and well productvtes. From ths nformaton, the length of a well drlled from a specfed drllng centre can be calculated as a functon of the ts length. For each year target producton rates are specfed as nput data to descrbe the requred producton profle for each partcular case to be examned. The dfferent profles essentally account for dfferent scenaros. The optmal soluton determnes the drllng sequence requred to acheve the most economc
3 A mathematcal programmng approach to the desgn and schedulng of offshore olfelds. 3 operaton. From the data of well costs, facltes costs and producton profle, an economc analyss can be compared wth other producton profles. In all cases, the models are formulated and solved as MILP problems. 2. Locaton of the drllng centre The mathematcal model s formulated as follows. Gven a set of wells and a set of drllng locatons. For each well locaton (x, y and z coordnates are gven together wth the well productvty) the obectve s dentfy the locaton that corresponds to the mnmum drllng cost. The problem parameters nclude: T = Target feld producton. W, = The cost of drllng well from drllng centre The set of varables consst of: Z = Bnary to select or deselect well. Y = Bnary to select or deselect drllng centre. C, = The actual cost of drllng once and are selected The formulaton of the obectve s then: Cost = C, (1), The obectve functon s to mnmse the cost of drllng suffcent wells from locaton to meet the producton target. Equaton (1) calculates the cost of meetng the producton target from each drllng locaton and determnes the lowest cost locaton. The obectve functon s subect to: C Z + Y 1 * W (2), ( ), Equaton (2) sets the cost of drllng well from locaton to zero, unless both Z and Y are equal to 1. Therefore, only the cost of the wells that are actually drlled from each locaton are totalled n Equaton (1). Z P T (3) Equaton (3) ensures the producton target s met for each drllng locaton. Y = 1 (4) Equaton (4) ensures that there s only one drllng centre. Ths can be relaxed to nvestgate the effect of multple drllng centres. The method descrbed n ths paper was used to determne the optmum drllng centre n two felds of the lterature [1]. The frst feld comprses 29 wells and the second 224 wells. The
4 4 Rchard J. Barnes et al. optmzaton revealed the optmum solutons n 0.1 and 1.6 CPU sec respectvely. 3. Schedulng multple felds The second stage of the nvestgaton s to determne the optmum development schedule o acheve a specfed feld producton profle. The optmsaton task s to determne the drllng sequence and the feld selecton that mnmses the total drllng cost to meet Producton Rate, BPD 120, ,000 80,000 60,000 40,000 20,000 the target producton. The model assumes a man feld and an arbtrary number of satellte felds feedng the man feld facltes. The recoverable reserves, and the locaton and productvty of potental well locatons are fxed parameters whch are used to defne the reservor. These parameters would normally reman constant unless the effect of uncertanty n the reservor were beng nvestgated. The target producton profle descrbes a partcular case beng nvestgated and determnes the speed wth whch the felds are developed. The problem s formulated mathematcally as follows. Gven s a set wells, a set of felds, a set of wells, and a producton tme comprsed by t perods (years). The drllng schedule and annual productons are determned over the feld lfe and over a fxed tme of producton (n years). Problem parameters nclude the: C, = Producton Year Fgure 2 Fgure 2 Producton profle The cost of drllng Well I from the specfed drllng locaton n Feld. T n = Target producton rate for Year n. W n = Potental producton from well n year n. The problem varables nclude: Z t = Bnary varable set to zero except n the year t when a specfc well s drlled. The array descrbes each feld. P,t n, = Actual producton from well n Year n, when drlled n Year t n Feld.
5 A mathematcal programmng approach to the desgn and schedulng of offshore olfelds. 5 The model mnmses the drllng cost over the lfe of the feld, consder wells drlled from all dfferent platforms. The obectve functon s formulated as: Cost = Z * C (5) t,t,, 120, , , ,000 Producton Rate, BPD 80,000 60,000 40,000 Producton Rate, BPD 80,000 60,000 40,000 20,000 20, Producton Year Producton Year Fgure 3: Accelerated producton Fgure 4: Slow developng producton Equaton (5) sums the costs of drllng the wells for each year and each feld or platform. Ths s the obectve functon that must be mnmsed over the lfe of the proect. The obectve functon n (5) s subect to P Z * W (6),t,n,,t,,n t +1 Equaton (6) sets the producton from each well to zero f t s not n operaton or to the specfed producton rate f the well has been drlled. T (7) n P,t,n, t Equaton (7) ensures that the total producton from each feld meets or exceeds the specfed target producton for that year. R P t, n, n t, (8) Equaton (8) ensures that producton from ndvdual felds does not exceed the recoverable reserves for that feld. Fgure 2 shows a typcal producton profle. Producton bulds up n the frst two years, and then remans constant for the plateau perod. Producton then enters the declne perod, contnung untl the revenue from the ol producton no longer exceeds the cost of operatng the feld. The feld s then no longer economc and s abandoned. Fgure 3 shows an accelerated producton programme n whch wells have been pre-drlled
6 6 Rchard J. Barnes et al. before nstallaton of the platform. Contnued drllng mantans the plateau for several years. Producton then declnes relatvely slowly by contnued drllng or workover durng part of the declne perod. Fgure 4 s of a producton profle that bulds up relatvely slow to plateau. Producton begns to rapdly declne wth the cessaton of drllng at the end of plateau producton. The grd spacng may be ncreased to dstrbute the wells over a larger area. Smlarly, an addtonal constrant can be added to lmt the well step out: D M (9) Where: D = Horzontal dstance between drllng centre and well. M = Maxmum permtted step out. The new method has been tested aganst models developed earler [2] and has gven comparable results. To date the new model has not been extended to model declne n well productvty durng feld lfe. 4. Conclusons The paper presents general mathematcal models to enable the optmal development of sngle and multple felds. By decomposng the problem nto two parts: selectng an optmum drllng centre and optmsng the well selecton; the problem complexty s sgnfcantly reduced. Although the problem can become qute large when there are several hundred potental well locatons over a feld lfe of 20 years or more, the problem stll remans well wthn the computatonal capacty of the modern personal computer. The model does not perform an economc analyss on the soluton to permt comparson of the case wth other cases wth dfferent producton profles. It also does not nclude a functon to model the declne n well performance wth producton. However, ths feature could be added n a further refnement. References 1. Barnes, R.J., Lnke, P. and Kokosss, A (2002). Optmsaton of olfeld development producton capacty. ESCAPE 12 proceedngs, The Hague (NL), Barnes, R.J., Kokosss, A. and Shang, Z. An ntegrated mathematcal programmng approach for the desgn and optmsaton of offshore felds. Computers and Chemcal Engneerng (2006). 3. Iyer, R. R., Grossmann, I. E., Vasantharaan, S., & Cullck, A. S. (1998). Optmal Plannng and Schedulng of Offshore Ol Feld Infrastructure Investment and Operaton. Industral and Engneerng chemstry Research, 37, Nero SMS, Pnto JM. A general modellng framework for the operatonal plannng of petroleum supply chans. Internatonal Conference on Foundatons of Computer- Aded Process Operatons, 2003, Computers & Chemcal Engneerng 28 (6-7): , Jun
An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation
17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 An Iteratve Soluton Approach to Process Plant Layout usng Mxed
More informationNUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS
ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana
More informationSupport Vector Machines
/9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.
More informationLECTURE NOTES Duality Theory, Sensitivity Analysis, and Parametric Programming
CEE 60 Davd Rosenberg p. LECTURE NOTES Dualty Theory, Senstvty Analyss, and Parametrc Programmng Learnng Objectves. Revew the prmal LP model formulaton 2. Formulate the Dual Problem of an LP problem (TUES)
More informationSLAM Summer School 2006 Practical 2: SLAM using Monocular Vision
SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,
More informationAn Optimal Algorithm for Prufer Codes *
J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,
More informationKent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming
CS 4/560 Desgn and Analyss of Algorthms Kent State Unversty Dept. of Math & Computer Scence LECT-6 Dynamc Programmng 2 Dynamc Programmng Dynamc Programmng, lke the dvde-and-conquer method, solves problems
More informationA Binarization Algorithm specialized on Document Images and Photos
A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a
More informationAPPLICATION OF A COMPUTATIONALLY EFFICIENT GEOSTATISTICAL APPROACH TO CHARACTERIZING VARIABLY SPACED WATER-TABLE DATA
RFr"W/FZD JAN 2 4 1995 OST control # 1385 John J Q U ~ M Argonne Natonal Laboratory Argonne, L 60439 Tel: 708-252-5357, Fax: 708-252-3 611 APPLCATON OF A COMPUTATONALLY EFFCENT GEOSTATSTCAL APPROACH TO
More informationA MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS
Proceedngs of the Wnter Smulaton Conference M E Kuhl, N M Steger, F B Armstrong, and J A Jones, eds A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Mark W Brantley Chun-Hung
More informationCompiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz
Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster
More informationCourse Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms
Course Introducton Course Topcs Exams, abs, Proects A quc loo at a few algorthms 1 Advanced Data Structures and Algorthms Descrpton: We are gong to dscuss algorthm complexty analyss, algorthm desgn technques
More informationSum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints
Australan Journal of Basc and Appled Scences, 2(4): 1204-1208, 2008 ISSN 1991-8178 Sum of Lnear and Fractonal Multobjectve Programmng Problem under Fuzzy Rules Constrants 1 2 Sanjay Jan and Kalash Lachhwan
More informationRepeater Insertion for Two-Terminal Nets in Three-Dimensional Integrated Circuits
Repeater Inserton for Two-Termnal Nets n Three-Dmensonal Integrated Crcuts Hu Xu, Vasls F. Pavlds, and Govann De Mchel LSI - EPFL, CH-5, Swtzerland, {hu.xu,vasleos.pavlds,govann.demchel}@epfl.ch Abstract.
More informationAADL : about scheduling analysis
AADL : about schedulng analyss Schedulng analyss, what s t? Embedded real-tme crtcal systems have temporal constrants to meet (e.g. deadlne). Many systems are bult wth operatng systems provdng multtaskng
More informationParallelism for Nested Loops with Non-uniform and Flow Dependences
Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr
More informationSPE = 1, then the. Application of this technique to a large number of wells in the Carthage field, Cotton Valley formation is presented.
SPE 104550 Identfyng Infll Locatons and Underperformer Wells n Mature Felds usng Monthly Producton Rate Data, Carthage Feld, Cotton Valley Formaton, Texas Jalal Jalal, Shahab D. Mohaghegh, Raz Gaskar,
More informationLobachevsky State University of Nizhni Novgorod. Polyhedron. Quick Start Guide
Lobachevsky State Unversty of Nzhn Novgorod Polyhedron Quck Start Gude Nzhn Novgorod 2016 Contents Specfcaton of Polyhedron software... 3 Theoretcal background... 4 1. Interface of Polyhedron... 6 1.1.
More informationDecision Support for the Dynamic Reconfiguration of Machine Layout and Part Routing in Cellular Manufacturing
Decson Support for the Dynamc Reconfguraton of Machne Layout and Part Routng n Cellular Manufacturng Hao W. Ln and Tomohro Murata Abstract A mathematcal based approach s presented to evaluate the dynamc
More informationLoad-Balanced Anycast Routing
Load-Balanced Anycast Routng Chng-Yu Ln, Jung-Hua Lo, and Sy-Yen Kuo Department of Electrcal Engneerng atonal Tawan Unversty, Tape, Tawan sykuo@cc.ee.ntu.edu.tw Abstract For fault-tolerance and load-balance
More informationPose, Posture, Formation and Contortion in Kinematic Systems
Pose, Posture, Formaton and Contorton n Knematc Systems J. Rooney and T. K. Tanev Department of Desgn and Innovaton, Faculty of Technology, The Open Unversty, Unted Kngdom Abstract. The concepts of pose,
More informationThe Codesign Challenge
ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble.
More informationCluster Analysis of Electrical Behavior
Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School
More informationProblem Definitions and Evaluation Criteria for Computational Expensive Optimization
Problem efntons and Evaluaton Crtera for Computatonal Expensve Optmzaton B. Lu 1, Q. Chen and Q. Zhang 3, J. J. Lang 4, P. N. Suganthan, B. Y. Qu 6 1 epartment of Computng, Glyndwr Unversty, UK Faclty
More informationINTRODUCTION INTRODUCTION. Moisès Graells Semi-continuous processes
INTRODUCTION Mosès Graells (moses.graells@upc.edu) Barcelona / Catalona / Span Unverstat Poltècnca de Catalunya CEPIMA, PSE research group Emertus Prof. Lus Puganer IECR Specal Issue INTRODUCTION Sem-contnuous
More informationAn Investigation into Server Parameter Selection for Hierarchical Fixed Priority Pre-emptive Systems
An Investgaton nto Server Parameter Selecton for Herarchcal Fxed Prorty Pre-emptve Systems R.I. Davs and A. Burns Real-Tme Systems Research Group, Department of omputer Scence, Unversty of York, YO10 5DD,
More informationClassifying Acoustic Transient Signals Using Artificial Intelligence
Classfyng Acoustc Transent Sgnals Usng Artfcal Intellgence Steve Sutton, Unversty of North Carolna At Wlmngton (suttons@charter.net) Greg Huff, Unversty of North Carolna At Wlmngton (jgh7476@uncwl.edu)
More informationReducing Frame Rate for Object Tracking
Reducng Frame Rate for Object Trackng Pavel Korshunov 1 and We Tsang Oo 2 1 Natonal Unversty of Sngapore, Sngapore 11977, pavelkor@comp.nus.edu.sg 2 Natonal Unversty of Sngapore, Sngapore 11977, oowt@comp.nus.edu.sg
More informationPositive Semi-definite Programming Localization in Wireless Sensor Networks
Postve Sem-defnte Programmng Localzaton n Wreless Sensor etworks Shengdong Xe 1,, Jn Wang, Aqun Hu 1, Yunl Gu, Jang Xu, 1 School of Informaton Scence and Engneerng, Southeast Unversty, 10096, anjng Computer
More informationSENSITIVITY ANALYSIS IN LINEAR PROGRAMMING USING A CALCULATOR
SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING USING A CALCULATOR Judth Aronow Rchard Jarvnen Independent Consultant Dept of Math/Stat 559 Frost Wnona State Unversty Beaumont, TX 7776 Wnona, MN 55987 aronowju@hal.lamar.edu
More informationIntra-Parametric Analysis of a Fuzzy MOLP
Intra-Parametrc Analyss of a Fuzzy MOLP a MIAO-LING WANG a Department of Industral Engneerng and Management a Mnghsn Insttute of Technology and Hsnchu Tawan, ROC b HSIAO-FAN WANG b Insttute of Industral
More informationCSCI 104 Sorting Algorithms. Mark Redekopp David Kempe
CSCI 104 Sortng Algorthms Mark Redekopp Davd Kempe Algorthm Effcency SORTING 2 Sortng If we have an unordered lst, sequental search becomes our only choce If we wll perform a lot of searches t may be benefcal
More informationCHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION
24 CHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION The present chapter proposes an IPSO approach for multprocessor task schedulng problem wth two classfcatons, namely, statc ndependent tasks and
More informationInvestigations of Topology and Shape of Multi-material Optimum Design of Structures
Advanced Scence and Tecnology Letters Vol.141 (GST 2016), pp.241-245 ttp://dx.do.org/10.14257/astl.2016.141.52 Investgatons of Topology and Sape of Mult-materal Optmum Desgn of Structures Quoc Hoan Doan
More informationMeta-heuristics for Multidimensional Knapsack Problems
2012 4th Internatonal Conference on Computer Research and Development IPCSIT vol.39 (2012) (2012) IACSIT Press, Sngapore Meta-heurstcs for Multdmensonal Knapsack Problems Zhbao Man + Computer Scence Department,
More informationA Fast Content-Based Multimedia Retrieval Technique Using Compressed Data
A Fast Content-Based Multmeda Retreval Technque Usng Compressed Data Borko Furht and Pornvt Saksobhavvat NSF Multmeda Laboratory Florda Atlantc Unversty, Boca Raton, Florda 3343 ABSTRACT In ths paper,
More informationEVALUATION OF THE PERFORMANCES OF ARTIFICIAL BEE COLONY AND INVASIVE WEED OPTIMIZATION ALGORITHMS ON THE MODIFIED BENCHMARK FUNCTIONS
Academc Research Internatonal ISS-L: 3-9553, ISS: 3-9944 Vol., o. 3, May 0 EVALUATIO OF THE PERFORMACES OF ARTIFICIAL BEE COLOY AD IVASIVE WEED OPTIMIZATIO ALGORITHMS O THE MODIFIED BECHMARK FUCTIOS Dlay
More informationSURFACE PROFILE EVALUATION BY FRACTAL DIMENSION AND STATISTIC TOOLS USING MATLAB
SURFACE PROFILE EVALUATION BY FRACTAL DIMENSION AND STATISTIC TOOLS USING MATLAB V. Hotař, A. Hotař Techncal Unversty of Lberec, Department of Glass Producng Machnes and Robotcs, Department of Materal
More information5.0 Quality Assurance
5.0 Dr. Fred Omega Garces Analytcal Chemstry 25 Natural Scence, Mramar College Bascs of s what we do to get the rght answer for our purpose QA s planned and refers to planned and systematc producton processes
More informationLife Tables (Times) Summary. Sample StatFolio: lifetable times.sgp
Lfe Tables (Tmes) Summary... 1 Data Input... 2 Analyss Summary... 3 Survval Functon... 5 Log Survval Functon... 6 Cumulatve Hazard Functon... 7 Percentles... 7 Group Comparsons... 8 Summary The Lfe Tables
More informationA Facet Generation Procedure. for solving 0/1 integer programs
A Facet Generaton Procedure for solvng 0/ nteger programs by Gyana R. Parja IBM Corporaton, Poughkeepse, NY 260 Radu Gaddov Emery Worldwde Arlnes, Vandala, Oho 45377 and Wlbert E. Wlhelm Teas A&M Unversty,
More informationSome Advanced SPC Tools 1. Cumulative Sum Control (Cusum) Chart For the data shown in Table 9-1, the x chart can be generated.
Some Advanced SP Tools 1. umulatve Sum ontrol (usum) hart For the data shown n Table 9-1, the x chart can be generated. However, the shft taken place at sample #21 s not apparent. 92 For ths set samples,
More informationA Hybrid Genetic Algorithm for Routing Optimization in IP Networks Utilizing Bandwidth and Delay Metrics
A Hybrd Genetc Algorthm for Routng Optmzaton n IP Networks Utlzng Bandwdth and Delay Metrcs Anton Redl Insttute of Communcaton Networks, Munch Unversty of Technology, Arcsstr. 21, 80290 Munch, Germany
More information6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour
6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the
More informationAPPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT
3. - 5. 5., Brno, Czech Republc, EU APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT Abstract Josef TOŠENOVSKÝ ) Lenka MONSPORTOVÁ ) Flp TOŠENOVSKÝ
More information3. CR parameters and Multi-Objective Fitness Function
3 CR parameters and Mult-objectve Ftness Functon 41 3. CR parameters and Mult-Objectve Ftness Functon 3.1. Introducton Cogntve rados dynamcally confgure the wreless communcaton system, whch takes beneft
More informationEECS 730 Introduction to Bioinformatics Sequence Alignment. Luke Huan Electrical Engineering and Computer Science
EECS 730 Introducton to Bonformatcs Sequence Algnment Luke Huan Electrcal Engneerng and Computer Scence http://people.eecs.ku.edu/~huan/ HMM Π s a set of states Transton Probabltes a kl Pr( l 1 k Probablty
More informationElectrical analysis of light-weight, triangular weave reflector antennas
Electrcal analyss of lght-weght, trangular weave reflector antennas Knud Pontoppdan TICRA Laederstraede 34 DK-121 Copenhagen K Denmark Emal: kp@tcra.com INTRODUCTION The new lght-weght reflector antenna
More informationRelated-Mode Attacks on CTR Encryption Mode
Internatonal Journal of Network Securty, Vol.4, No.3, PP.282 287, May 2007 282 Related-Mode Attacks on CTR Encrypton Mode Dayn Wang, Dongda Ln, and Wenlng Wu (Correspondng author: Dayn Wang) Key Laboratory
More informationType-2 Fuzzy Non-uniform Rational B-spline Model with Type-2 Fuzzy Data
Malaysan Journal of Mathematcal Scences 11(S) Aprl : 35 46 (2017) Specal Issue: The 2nd Internatonal Conference and Workshop on Mathematcal Analyss (ICWOMA 2016) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES
More informationHierarchical clustering for gene expression data analysis
Herarchcal clusterng for gene expresson data analyss Gorgo Valentn e-mal: valentn@ds.unm.t Clusterng of Mcroarray Data. Clusterng of gene expresson profles (rows) => dscovery of co-regulated and functonally
More informationVirtual Machine Migration based on Trust Measurement of Computer Node
Appled Mechancs and Materals Onlne: 2014-04-04 ISSN: 1662-7482, Vols. 536-537, pp 678-682 do:10.4028/www.scentfc.net/amm.536-537.678 2014 Trans Tech Publcatons, Swtzerland Vrtual Machne Mgraton based on
More informationMODULE DESIGN BASED ON INTERFACE INTEGRATION TO MAXIMIZE PRODUCT VARIETY AND MINIMIZE FAMILY COST
INTERNATIONAL CONFERENCE ON ENGINEERING DESIGN, ICED 07 28-31 AUGUST 2007, CITE DES SCIENCES ET DE L'INDUSTRIE, PARIS, FRANCE MODULE DESIGN BASED ON INTERFACE INTEGRATION TO MAIMIZE PRODUCT VARIETY AND
More informationUSING GRAPHING SKILLS
Name: BOLOGY: Date: _ Class: USNG GRAPHNG SKLLS NTRODUCTON: Recorded data can be plotted on a graph. A graph s a pctoral representaton of nformaton recorded n a data table. t s used to show a relatonshp
More informationLARRY SNYDER DEPT. OF INDUSTRIAL AND SYSTEMS ENGINEERING CENTER FOR VALUE CHAIN RESEARCH LEHIGH UNIVERSITY
Faclty Locaton Models: An Overvew 1 LARRY SNYDER DEPT. OF INDUSTRIAL AND SYSTEMS ENGINEERING CENTER FOR VALUE CHAIN RESEARCH LEHIGH UNIVERSITY EWO SEMINAR SERIES APRIL 21, 2010 Outlne Introducton Taxonomy
More informationParallel matrix-vector multiplication
Appendx A Parallel matrx-vector multplcaton The reduced transton matrx of the three-dmensonal cage model for gel electrophoress, descrbed n secton 3.2, becomes excessvely large for polymer lengths more
More informationLOOP ANALYSIS. The second systematic technique to determine all currents and voltages in a circuit
LOOP ANALYSS The second systematic technique to determine all currents and voltages in a circuit T S DUAL TO NODE ANALYSS - T FRST DETERMNES ALL CURRENTS N A CRCUT AND THEN T USES OHM S LAW TO COMPUTE
More informationUB at GeoCLEF Department of Geography Abstract
UB at GeoCLEF 2006 Mguel E. Ruz (1), Stuart Shapro (2), June Abbas (1), Slva B. Southwck (1) and Davd Mark (3) State Unversty of New York at Buffalo (1) Department of Lbrary and Informaton Studes (2) Department
More informationOPTIMAL CONFIGURATION FOR NODES IN MIXED CELLULAR AND MOBILE AD HOC NETWORK FOR INET
OPTIMAL CONFIGURATION FOR NODE IN MIED CELLULAR AND MOBILE AD HOC NETWORK FOR INET Olusola Babalola D.E. Department of Electrcal and Computer Engneerng Morgan tate Unversty Dr. Rchard Dean Faculty Advsor
More informationVoice capacity of IEEE b WLANs
Voce capacty of IEEE 82.b WLANs D. S. Amanatads, V. Vtsas, A. Mantsars 2, I. Mavrds 2, P. Chatzmsos and A.C. Boucouvalas 3 Abstract-There s a tremendous growth n the deployment and usage of Wreless Local
More informationProgramming in Fortran 90 : 2017/2018
Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values
More informationNeeded Information to do Allocation
Complexty n the Database Allocaton Desgn Must tae relatonshp between fragments nto account Cost of ntegrty enforcements Constrants on response-tme, storage, and processng capablty Needed Informaton to
More informationXV International PhD Workshop OWD 2013, October Machine Learning for the Efficient Control of a Multi-Wheeled Mobile Robot
XV Internatonal PhD Workshop OWD 203, 9 22 October 203 Machne Learnng for the Effcent Control of a Mult-Wheeled Moble Robot Uladzmr Dzomn, Brest State Techncal Unversty (prof. Vladmr Golovko, Brest State
More informationAn Entropy-Based Approach to Integrated Information Needs Assessment
Dstrbuton Statement A: Approved for publc release; dstrbuton s unlmted. An Entropy-Based Approach to ntegrated nformaton Needs Assessment June 8, 2004 Wllam J. Farrell Lockheed Martn Advanced Technology
More informationData Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach
Data Representaton n Dgtal Desgn, a Sngle Converson Equaton and a Formal Languages Approach Hassan Farhat Unversty of Nebraska at Omaha Abstract- In the study of data representaton n dgtal desgn and computer
More informationA DATA ANALYSIS CODE FOR MCNP MESH AND STANDARD TALLIES
Supercomputng n uclear Applcatons (M&C + SA 007) Monterey, Calforna, Aprl 15-19, 007, on CD-ROM, Amercan uclear Socety, LaGrange Par, IL (007) A DATA AALYSIS CODE FOR MCP MESH AD STADARD TALLIES Kenneth
More informationTerm Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task
Proceedngs of NTCIR-6 Workshop Meetng, May 15-18, 2007, Tokyo, Japan Term Weghtng Classfcaton System Usng the Ch-square Statstc for the Classfcaton Subtask at NTCIR-6 Patent Retreval Task Kotaro Hashmoto
More informationSequential Projection Maximin Distance Sampling Method
APCOM & ISCM 11-14 th December, 2013, Sngapore Sequental Projecton Maxmn Dstance Samplng Method J. Jang 1, W. Lm 1, S. Cho 1, M. Lee 2, J. Na 3 and * T.H. Lee 1 1 Department of automotve engneerng, Hanyang
More informationChannel 0. Channel 1 Channel 2. Channel 3 Channel 4. Channel 5 Channel 6 Channel 7
Optmzed Regonal Cachng for On-Demand Data Delvery Derek L. Eager Mchael C. Ferrs Mary K. Vernon Unversty of Saskatchewan Unversty of Wsconsn Madson Saskatoon, SK Canada S7N 5A9 Madson, WI 5376 eager@cs.usask.ca
More informationSOLUTION APPROACHES FOR THE CLUSTER TOOL SCHEDULING PROBLEM IN SEMICONDUCTOR MANUFACTURING
SOLUTION APPROACHES FOR THE CLUSTER TOOL SCHEDULING PROBLEM IN SEMICONDUCTOR MANUFACTURING Heko Nedermayer Olver Rose Computer Networks and Internet Wlhelm-Schckard-Insttute for Computer Scence Dstrbuted
More informationData Mining For Multi-Criteria Energy Predictions
Data Mnng For Mult-Crtera Energy Predctons Kashf Gll and Denns Moon Abstract We present a data mnng technque for mult-crtera predctons of wnd energy. A mult-crtera (MC) evolutonary computng method has
More informationDelay Variation Optimized Traffic Allocation Based on Network Calculus for Multi-path Routing in Wireless Mesh Networks
Appl. Math. Inf. Sc. 7, No. 2L, 467-474 2013) 467 Appled Mathematcs & Informaton Scences An Internatonal Journal http://dx.do.org/10.12785/ams/072l13 Delay Varaton Optmzed Traffc Allocaton Based on Network
More informationAssignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.
Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton
More informationA Clustering Algorithm for Chinese Adjectives and Nouns 1
Clusterng lgorthm for Chnese dectves and ouns Yang Wen, Chunfa Yuan, Changnng Huang 2 State Key aboratory of Intellgent Technology and System Deptartment of Computer Scence & Technology, Tsnghua Unversty,
More informationHybrid Heuristics for the Maximum Diversity Problem
Hybrd Heurstcs for the Maxmum Dversty Problem MICAEL GALLEGO Departamento de Informátca, Estadístca y Telemátca, Unversdad Rey Juan Carlos, Span. Mcael.Gallego@urjc.es ABRAHAM DUARTE Departamento de Informátca,
More informationLS-TaSC Version 2.1. Willem Roux Livermore Software Technology Corporation, Livermore, CA, USA. Abstract
12 th Internatonal LS-DYNA Users Conference Optmzaton(1) LS-TaSC Verson 2.1 Wllem Roux Lvermore Software Technology Corporaton, Lvermore, CA, USA Abstract Ths paper gves an overvew of LS-TaSC verson 2.1,
More informationOptimal Scheduling of Capture Times in a Multiple Capture Imaging System
Optmal Schedulng of Capture Tmes n a Multple Capture Imagng System Tng Chen and Abbas El Gamal Informaton Systems Laboratory Department of Electrcal Engneerng Stanford Unversty Stanford, Calforna 9435,
More informationTPL-Aware Displacement-driven Detailed Placement Refinement with Coloring Constraints
TPL-ware Dsplacement-drven Detaled Placement Refnement wth Colorng Constrants Tao Ln Iowa State Unversty tln@astate.edu Chrs Chu Iowa State Unversty cnchu@astate.edu BSTRCT To mnmze the effect of process
More informationProceedings of the 2014 Winter Simulation Conference A. Tolk, S. Y. Diallo, I. O. Ryzhov, L. Yilmaz, S. Buckley, and J. A. Miller, eds.
Proceedngs of the 2014 Wnter Smulaton Conference A. Tolk, S. Y. Dallo, I. O. Ryzhov, L. Ylmaz, S. Buckley, and J. A. Mller, eds. TOPSIS BASED TAGUCHI METHOD FOR MULTI-RESPONSE SIMULATION OPTIMIZATION OF
More informationComparison of Heuristics for Scheduling Independent Tasks on Heterogeneous Distributed Environments
Comparson of Heurstcs for Schedulng Independent Tasks on Heterogeneous Dstrbuted Envronments Hesam Izakan¹, Ath Abraham², Senor Member, IEEE, Václav Snášel³ ¹ Islamc Azad Unversty, Ramsar Branch, Ramsar,
More informationOptimal planning of selective waste collection
Sustanable Development and Plannng V 78 Optmal plannng of selectve aste collecton S. Racu, D. Costescu, E. Roşca & M. Popa Unversty Poltehnca of Bucharest, Romana Abstract The paper presents an optmsaton
More informationLearning the Kernel Parameters in Kernel Minimum Distance Classifier
Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and Zh-Hua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department
More informationVRT012 User s guide V0.1. Address: Žirmūnų g. 27, Vilnius LT-09105, Phone: (370-5) , Fax: (370-5) ,
VRT012 User s gude V0.1 Thank you for purchasng our product. We hope ths user-frendly devce wll be helpful n realsng your deas and brngng comfort to your lfe. Please take few mnutes to read ths manual
More informationLearning to Project in Multi-Objective Binary Linear Programming
Learnng to Project n Mult-Objectve Bnary Lnear Programmng Alvaro Serra-Altamranda Department of Industral and Management System Engneerng, Unversty of South Florda, Tampa, FL, 33620 USA, amserra@mal.usf.edu,
More informationApplication of Improved Fish Swarm Algorithm in Cloud Computing Resource Scheduling
, pp.40-45 http://dx.do.org/10.14257/astl.2017.143.08 Applcaton of Improved Fsh Swarm Algorthm n Cloud Computng Resource Schedulng Yu Lu, Fangtao Lu School of Informaton Engneerng, Chongqng Vocatonal Insttute
More informationA Fuzzy Goal Programming Approach for a Single Machine Scheduling Problem
Proceedngs of e 9 WSEAS Internatonal Conference on Appled Maematcs, Istanbul, Turkey, May 7-9, 006 (pp40-45 A Fuzzy Goal Programmng Approach for a Sngle Machne Schedulng Problem REZA TAVAKKOLI-MOGHADDAM,
More informationModelling a Queuing System for a Virtual Agricultural Call Center
25-28 July 2005, Vla Real, Portugal Modellng a Queung System for a Vrtual Agrcultural Call Center İnc Şentarlı, a, Arf Orçun Sakarya b a, Çankaya Unversty, Department of Management,06550, Balgat, Ankara,
More informationSHAPE OPTIMIZATION OF STRUCTURES BY MODIFIED HARMONY SEARCH
INTERNATIONAL JOURNAL OF OPTIMIZATION IN CIVIL ENGINEERING Int. J. Optm. Cvl Eng., 2011; 3:485-494 SHAPE OPTIMIZATION OF STRUCTURES BY MODIFIED HARMONY SEARCH S. Gholzadeh *,, A. Barzegar and Ch. Gheyratmand
More informationMIXED INTEGER-DISCRETE-CONTINUOUS OPTIMIZATION BY DIFFERENTIAL EVOLUTION Part 1: the optimization method
MIED INTEGER-DISCRETE-CONTINUOUS OPTIMIZATION BY DIFFERENTIAL EVOLUTION Part : the optmzaton method Joun Lampnen Unversty of Vaasa Department of Informaton Technology and Producton Economcs P. O. Box 700
More informationGreedy Technique - Definition
Greedy Technque Greedy Technque - Defnton The greedy method s a general algorthm desgn paradgm, bult on the follong elements: confguratons: dfferent choces, collectons, or values to fnd objectve functon:
More informationSupport Vector Machines
Support Vector Machnes Decson surface s a hyperplane (lne n 2D) n feature space (smlar to the Perceptron) Arguably, the most mportant recent dscovery n machne learnng In a nutshell: map the data to a predetermned
More informationClassifier Selection Based on Data Complexity Measures *
Classfer Selecton Based on Data Complexty Measures * Edth Hernández-Reyes, J.A. Carrasco-Ochoa, and J.Fco. Martínez-Trndad Natonal Insttute for Astrophyscs, Optcs and Electroncs, Lus Enrque Erro No.1 Sta.
More informationEffective Network Partitioning to Find MIP Solutions to the Train Dispatching Problem
Vrgna Commonwealth Unversty VCU Scholars Compass Theses and Dssertatons Graduate School 2013 ffectve Network Parttonng to Fnd MIP Solutons to the Tran Dspatchng Problem Chrstopher Snellngs Vrgna Commonwealth
More informationOptimal connection strategies in one- and two-dimensional associative memory models
Optmal connecton strateges n one- and two-dmensonal assocatve memory models Lee Calcraft, Rod Adams, and Nel Davey School of Computer Scence, Unversty of Hertfordshre College lane, Hatfeld, Hertfordshre
More informationAnalysis of Continuous Beams in General
Analyss of Contnuous Beams n General Contnuous beams consdered here are prsmatc, rgdly connected to each beam segment and supported at varous ponts along the beam. onts are selected at ponts of support,
More informationSupport Vector Machines. CS534 - Machine Learning
Support Vector Machnes CS534 - Machne Learnng Perceptron Revsted: Lnear Separators Bnar classfcaton can be veed as the task of separatng classes n feature space: b > 0 b 0 b < 0 f() sgn( b) Lnear Separators
More informationA New Approach For the Ranking of Fuzzy Sets With Different Heights
New pproach For the ankng of Fuzzy Sets Wth Dfferent Heghts Pushpnder Sngh School of Mathematcs Computer pplcatons Thapar Unversty, Patala-7 00 Inda pushpndersnl@gmalcom STCT ankng of fuzzy sets plays
More informationSorting Review. Sorting. Comparison Sorting. CSE 680 Prof. Roger Crawfis. Assumptions
Sortng Revew Introducton to Algorthms Qucksort CSE 680 Prof. Roger Crawfs Inserton Sort T(n) = Θ(n 2 ) In-place Merge Sort T(n) = Θ(n lg(n)) Not n-place Selecton Sort (from homework) T(n) = Θ(n 2 ) In-place
More informationConfiguration Management in Multi-Context Reconfigurable Systems for Simultaneous Performance and Power Optimizations*
Confguraton Management n Mult-Context Reconfgurable Systems for Smultaneous Performance and Power Optmzatons* Rafael Maestre, Mlagros Fernandez Departamento de Arqutectura de Computadores y Automátca Unversdad
More informationA Mixed Linear Program for a Multi-Part Cyclic Hoist Scheduling Problem
A Mxed Lnear Program for a MultPart Cyclc Host Schedulng Problem Adnen El Amraou,, MareAnge Maner, Abdellah El Moudn and Mohamed Benrejeb U.R. LARA Automatque, Ecole Natonale d Ingéneurs de Tuns, Tunse.
More information