Shrimp Partial Harvesting Model: Decision Support System User Manual

Size: px
Start display at page:

Download "Shrimp Partial Harvesting Model: Decision Support System User Manual"

Transcription

1 CTSA Publcaton No. 153 Shrmp Partal Harvestng Model: Decson Support System User Manual Lotus E. Kam, Run Yu, and PngSun Leung Department of Molecular Boscences and Boengneerng College of Tropcal Agrculture and Human Resources Unversty of Hawa` at Mānoa, Honolulu, USA Paul Benfang Department of Oceanography School of Ocean and Earth Scence and Technology Unversty of Hawa` at Mānoa, Honolulu, USA CENTER FOR TROPICAL AND SUBTROPICAL AQUACULTURE

2

3 User Informaton & Program Requrements Ths Shrmp Partal Harvestng Model, often hereafter referred to as Model, ncludes worksheets to smplfy data entry and navgaton. Ths manual descrbes the data entry procedures, equatons unque to the Model, and a smplfed fnancal analyss. Users of the Model should have a general workng knowledge of Mcrosoft Excel. For a detaled descrpton of the program, ts functons and commands, consult a Mcrosoft Excel manual or one of the many books descrbng Mcrosoft Excel. Ths beta verson of the Model requres a Wndows PC or compatble that runs Mcrosoft Excel 2002, 2003, or Free space of 1MB s requred. Ths applcaton requres the Solver add-n avalable wth Mcrosoft Excel. Ths applcaton s not compatble wth the Premum verson of the Solver avalable through Frontlne Systems Inc. If you have nstalled the Premum Solver, you must unnstall the advanced verson of the add-n n order to use ths Model software. The provders are not responsble for conflcts between versons of the Solver and Mcrosoft Excel. Lmts of Lablty and Dsclamer of Warranty The authors of ths manual have used ther best efforts to prepare ths manual and the program t supports. The authors make no warranty of any knd, expressed or mpled, wth regard to these programs or the documentaton contaned heren. The authors shall not be held lable for ncdental or consequental damages n connecton wth, or arsng from, the furnshng, performance or use of the program. Shrmp Partal Harvestng Model: Decson Support System User Manual

4 Contact Please drect nqures regardng ths publcaton and assocated software to PngSun Leung through ether e-mal or regular mal usng the below contact nformaton: PngSun Leung Department of Molecular Boscences and Boengneerng Unversty of Hawa` at Mānoa 3050 Male Way, Glmore 111 Honolulu, HI 96822, USA Trademarks Mcrosoft and Excel are trademarks of the Mcrosoft Corporaton. Copyrght 2008 CTSA Publcaton No. 153

5 Acknowledgements Many people contrbuted to the development of ths Shrmp Partal Harvestng Model. The authors thank Mr. James N. Sweeney and Mr. Davd Godn of the former Ceatech USA Inc. for ther varous forms of support for and assstance wth the valdaton of ths Model. The Model and software development was made possble by the generous support of the Center for Tropcal and Subtropcal Aquaculture through grant nos and from the U.S. Department of Agrculture Cooperatve State Research, Educaton, and Extenson Servce. Shrmp Partal Harvestng Model: Decson Support System User Manual

6

7 Shrmp Partal Harvestng Model Decson Support System User Manual 1. Introducton In tradtonal shrmp culture and other ntensve aquaculture systems, an entre crop can be harvested at one tme. However, snce growth and survval are densty-dependent, sngle-batch harvestng can lead to compettve pressures that lower ndvdual growth and ncrease mortaltes. In comparson to sngle-batch harvestng, a partal harvestng approach can enhance growth rates and total yeld, snce a reducton n total bomass reduces compettve pressure. In partal harvestng, a crop can be partally-harvested so that only a porton of the crop s extracted. Ths Shrmp Partal Harvestng Model was created to smulate the effects of partal harvestng and assst farm managers n decdng the most proftable harvestng schedule. Ths Shrmp Partal Harvestng Model, often hereafter referred to as Model, s a decson support system based on a network-flow approach. Ths Model determnes the optmal harvestng tmes that maxmze the overall net revenue based on bologcal and economc factors (e.g., survval, growth, and prce). Ths Model was mplemented n a spreadsheet form usng Mcrosoft Excel. The model requres the use of the Solver add-n. See Part B of the Appendx for nstructons on nstallng the standard Solver. Detals of the Model development also can be found n the Appendx. Clck Begn on the ntroducton screen to begn usng the Partal Harvestng Model (Fgure 1-1). Fgure 1-1. Vew of the ntroducton screen. 1

8 2. Man Menu The Man Menu of ths spreadsheet Model provdes access to worksheets to enter nformaton about farm operatons (Operatons), market prce and demand nformaton (Market Info), and boproducton technology performance (Boproducton). Use the Analyss button to determne the optmal harvestng strategy. Use the Results button to fnd the producton schedule and overall net revenue soluton for the most recent analyss. In compatble versons of Excel, a navgaton toolbar s located at the top of the screen. The navgaton toolbar provdes access to the worksheets, fle, vewng, and prntng optons. The navgaton toolbar s not avalable n Mcrosoft Offce Fgure 2-1. Vew of the Man Menu used for navgaton. 3. Operatons Informaton about farm operatons s needed to estmate the operatng expenses that a farm wll ncur. 2 CTSA Publcaton No. 153

9 The total pond area must be specfed n acres. The total pond area refers to the sze of a sngle pond. Multple ponds are not consdered n ths smplfed partal-harvestng model. Multple ponds ntroduce a more complex schedulng problem (Yu and Leung 2005). Key farm costs are requred, ncludng the expenses lsted as examples below: weekly mantenance cost ($/week), feed cost ($/lb), harvest cost ($/harvest), and seed cost ($/1000). Ths Model s desgned to determne f t s more advantageous to engage n multple small partal harvests n comparson to a sngle (or few) very large harvests. In order to nvestgate the benefts of the partal-harvest strategy, users must specfy the cost for a tradtonal full harvest and a partal harvest. Fgure 3-1. Vew of the nput screen for Operatons. In ths partal harvestng software, users specfy the maxmum sze of the partal harvest (partal harvest lmt) n lbs/harvest. The partal harvest lmt should be greater than 0 and ndcates the maxmum amount that can be harvested at the partal harvest cost ($/harvest). In Fgure 3-1, for example, the cost to harvest any amount over 1,000 lb. s $1,000. For harvests less than or equal to 1,000 lb., the cost s $250. Shrmp Partal Harvestng Model: Decson Support System User Manual 3

10 Specfy the seed sze n grams. The seed cost must be specfed n conventonal unts of dollars per thousand peces ($/1,000). The cost s converted nto $/lb based on the seed sze entered. Note: If there s no dstncton between a partal harvest and large harvest, the harvest cost should be the same. Do not leave ether of these cells blank. For large pond szes, t may make sense to ncrease the partal harvest lmt accordngly. 4. Market Informaton (Input) Informaton about the market sze and demand s needed to determne the potental revenue avalable to the farm enterprse. Fgure 4-1. Vew of the Market Informaton nput screen. 4 CTSA Publcaton No. 153

11 4.1. Market Sze (B1) Ths spreadsheet dvdes the market demand for shrmp products nto seven common count szes. Snce market szes are specfed as ranges correspondng to the number of shrmp per pound, a farmer can specfy hs target weght crtera. In partcular, a farmer may choose to meet the mnmum, average, maxmum, or other weght crtera correspondng to each range. In ths example, the farmer has chosen to meet the mnmum shrmp szes, whch are 9.07 g (40 50 count), g (41 45 count) and g (16 20 count). The mnmum settng s the most conservatve and recommended for most producton systems. Hgh crtera requre more effcent boproducton technologes. Ths Model wll not be able to provde a harvest soluton f boproducton technology (dscussed n Secton 5) cannot acheve the sze crtera specfed. Therefore, f you have dffculty runnng your model, you may want to revse your Market Sze nformaton. The count ranges are based on current market demand but can be customzed by a user. As llustrated n Fgure 4-2, a user can customze the range settngs by modfyng the maxmum count correspondng to each growout phase and the overall mnmum count (.e., the largest shrmp). The maxmum count of shrmp for each market range should be decreased for successve ranges. The length of a growout perod corresponds to shrmp sze and shrmp count. Specfcally, extendng the length of growout means ncreasng shrmp sze and reducng shrmp counts. Ths Model requres consecutve ranges such that the maxmum counts for each range decrease for later growout phases. Ths Model wll verfy whether szes are sequental before conductng analyss. A user can specfy target szes n the Other column. When selectng Other for crtera, weghts must be specfed for each range n the far rght column. These target weghts should fall between the mnmum and maxmum count szes for each sze range. The values n the Other column cells are only actve when the target weght crtera s set to Other from the drop down menu. Fgure 4-2. Specfyng target weght crtera and market sze range. Shrmp Partal Harvestng Model: Decson Support System User Manual 5

12 4.2. Market Demand The Target Sze (n grams) should reflect the weght correspondng to your target weght crtera. Enter the Mnmum Producton correspondng to each sze. If no producton s requred for a sze category, enter 0. In Fgure 4-5, no mnmum producton s requred. Enter the Maxmum Demand correspondng to each sze. If demand s unknown or not very large, type =MaxValue and the maxmum default value wll appear. Fgure 4-3. Default Mnmum Producton and Maxmum Demand (Partal Harvest Scenaro). Farms may be requred to fulfll a mnmum producton level f they have contracts wth shrmp wholesalers. If a mnmum level of producton s requred, the Mnmum Producton amount must reflect the requred producton for each sze. Fgure 4-4 llustrates the case where a farm s requred to produce a mnmum volume of 5,000 lb. of count and 14,000 lb count shrmp. For the sngle-batch harvest scenaro, the Mnmum Producton for all product szes should be set to 0. The Maxmum Demand should also be equal to 0 for all growout szes except for the fnal harvest sze (see Fgure 4-5). The mnmum producton requred must be less than the carryng capacty. The Maxmum Demand for the fnal harvest (growout phase) cannot be equal to zero. Fgure 4-4. Specfyng a producton requrement (Partal Harvest Scenaro wth Producton Requrement). Fgure 4-5. Sngle-batch harvest scenaro (baselne scenaro). 6 CTSA Publcaton No. 153

13 5. Boproducton (Input) In order to measure the fnancal performance of a shrmp farm, nformaton about boproducton technology s needed. Recommended boproducton performance data s avalable n the Boproducton worksheet. Values are based on data collected from a commercal shrmp farm n Hawa`, whch operated 40 one-acre ntensve shrmp ponds (Yu, Leung, and Benfang 2007). Growth and mortalty nformaton s based on tradtonal sngle-batch harvest practces. Fgure 5-1. Vew of nput screen for Boproducton Technology. Boproducton performance values may be changed based on your farm s producton specfcatons: Indcate the carryng capacty for your pond (kg/m 2 ). The carryng capacty s assumed to be the same for all phases and s based on the value you specfy. Provde the ntal stockng densty for the frst growout perod n shrmp per square meter (.e., frst row n the current densty column). Estmate the densty for subsequent phases based on your pond performance. These densty estmates should be based on sngle-batch harvest practces. Estmate the weekly growth rate (g/week) for each growout phase. Provde the current survval rate for each phase (% survval). Shrmp Partal Harvestng Model: Decson Support System User Manual 7

14 Enter the maxmum growth rate for each phase (g/week). Current growth rates should always be less than maxmum growth rates. Enter the feed rate as a percentage of pond bomass. The worksheet also provdes a summary about the growth curve, tme requred to reach market weght, and growth lmts based on the producton technology specfed. Fgure 5-2. Boproducton growth and lmt summary. 6. Analyss To run a partal harvest analyss based on your nput, clck the Analyss button. Some valdaton has been bult nto ths Model. Ths worksheet verfes that entered values are consstent or acceptable wthn the workbook. Ths program wll verfy that the followng constrants are met: Count szes should decrease for subsequent phases. Maxmum growth s less than the growth lmt for each phase. Please be patent as the analyss runs through a sequence of sx dfferent algorthm settngs. If your analyss s successful, you wll be drected to a summary report. The optmal soluton s compared to the sngle-batch harvestng soluton n ths report. 8 CTSA Publcaton No. 153

15 Fgure 6-1. Begnnng the analyss. Note: If the program s not able to run successfully based on your nput, a partal-harvest soluton wll not be gven. You wll receve a message that requests revson of your nputs. Shrmp Partal Harvestng Model: Decson Support System User Manual 9

16 7. Results Results nclude summary nformaton about boproducton performance (D1), market summary (D2), revenue and expenses (D3), and net revenue (D4). Fgure 7-1. Results spreadsheet Boproducton Performance Informaton about Boproducton Performance ncludes ntal densty, duraton of each phase, weekly growth rates, harvest weght, and harvest schedule. The optmal partal harvestng soluton for a scenaro wth no producton requrement and unlmted demand s ndcated n Fgure 7-2. Accordng to these results, the stockng densty should be shrmp/m 2 wth harvests after 7.4 weeks (1,000 lb. of count shrmp), 9.0 weeks (1,000 lb. of count shrmp), 14.9 weeks (102 lb. of count shrmp), and 20.6 weeks (14,738 lb. of count shrmp). The total weght harvested by the end of the 20.6 weeks s 16,840 lb. of shrmp Market Demand In the Market secton, the mnmum demand (requred producton) and maxmum demand values entered earler by a user n the Boproducton sheet nput are dsplayed. These demand ranges ndcate market constrants mposed on the farm. The count ranges used n ths analyss are also dsplayed for reference. Ths market nformaton s llustrated n Fgure CTSA Publcaton No. 153

17 Fgure 7-2. Boproducton performance and market nformaton Revenue and Expenses Operatng costs, proft from sales (revenue operatng costs), and harvest costs are lsted n the results worksheet (D3). Proft from sales (revenue producton costs) s calculated for each growout phase (Fgure 7-2) Net Revenue Summary The Net Revenue Summary provdes nformaton on revenue from sales, expenses (operatng costs and harvest costs), and net revenue for each growout phase. The Pond Summary s based on user-entered nformaton on operatons, boproducton, and market. The partal harvest soluton s always compared to the sngle-batch, full-harvest optmal soluton. In the example above (Fgure 7-3), the partal harvest soluton s expected to generate an overall net revenue of $58,119 n comparson to the sngle-batch harvest of $57,239 at 19.9 weeks. Therefore, the partal harvest method s expected to ncrease overall net revenue by $880. Fgure 7-3. Partal harvest result compared to snglebatch baselne scenaro. Shrmp Partal Harvestng Model: Decson Support System User Manual 11

18 7.5. Producton Requrement Example The followng example (Fgure 7-4) llustrates the mpact of market constrants on the partal harvest soluton. In contractual busness relatonshps, a farm may be requred to produce a mnmum level of producton for a certan market sze of shrmp. Gven these constrants, a producer s forced to harvest a mnmum volume of product at specfc tmes durng the growout perod. In ths producton requrement (PR) example, the farm s requred to produce 5,000 lb. of count shrmp and 14,000 lb. of count shrmp. The market demand nputs for ths scenaro are entered as llustrated earler n Fgure 4 4. Note: Whenever changes are made to nput values, the analyss must be run n order to determne the harvest schedule based on the new nformaton. Usng the Shrmp Partal Harvestng Model on ths example produces the followng analyss. Optmal stockng s anmals per square meter. Shrmp should be harvested as follows: 5,000 lb. at 10.7 weeks (36 40 count), 102 lb. at 18.0 weeks (21 25 count) and 14,738 lb. at 23.7 weeks (16 20 count). The result of ths producton schedule s overall net revenue of $53,317 and a total harvest of 19,840 lb. These producton requrements, then, result n a loss of $3,921 n comparson to the tradtonal sngle-batch harvest method. In comparson to the optmal partal harvest soluton wth no producton requrement exhbted n Fgure 7-3, the projected loss s $4,801 (= $58,119 - $53,317). Ths example llustrates the case where a farmer s dsadvantaged by a wholesalng contract that mposes producton requrements. Fgure 7-4. Partal harvest result for requred producton compared to the sngle-batch baselne scenaro. 12 CTSA Publcaton No. 153

19 8. References R. Yu and P.S. Leung Optmal harvestng strateges for a mult-pond and mult-cycle shrmp operaton: a practcal network model. Mathematcs and Computers n Smulaton 68(4): R. Yu, P.S. Leung, and P. Benfang Modelng partal harvestng n ntensve shrmp culture: a network-flow approach. European Journal of Operatonal Research. do: /j. ejor APPENDIX A. The Mathematcal Model Suppose a shrmp producton cycle s comprsed of N growout phases. Let H and P s denote the amount of shrmp harvested at the th growout phase n kg and ts assocated shrmp prce ($/kg), N N N Ps H - (C f+c m)- HC =1 =1 =1 s.t. HC =0, f H =0 HC =C h, f H > 0 H H H max mn (1) respectvely. The overall net revenue from ths producton cycle can be estmated as follows: where C f, C m, HC are feed cost, mantenance cost, and harvest cost occurrng n the th growout phase, respectvely. Only a quas-fxed harvest cost, C h, s consdered n the present model. In other words, f the producer decdes to do a harvest, t wll result n a fxed expendture; H max and harvest. C h. H mn are the maxmum and mnmum amount of shrmp that can be extracted by the th Let D and B denote the densty of shrmp stock (e.g., kg/m 2 ) at the begnnng and end of the th growout phase, respectvely. Defne V as the total area (or volume) of the water body of the growout faclty (e.g., m 2 ). Assume there s no restockng between two successve growout phases. The amount of shrmp that s extracted by the th harvest at the end of the th growout phase, +1, then can be estmated as H =VB -VD,.e., the dfference between the bomass at the end of the th growout phase and that at the begnnng of the +1 th growout phase. Snce all the shrmp wll be harvested at the end of the growout cycle, t mples producer s to maxmze overall net revenue. H N N H =VB. Assume the objectve of the Shrmp Partal Harvestng Model: Decson Support System User Manual 13

20 The partal harvestng problem can be expressed as follows: D N-1 +1 N N N N s f m + s f m N (2) =1 Max [P V (B -D )-C -C -HC ] P V B -C -C -HC Let W denote the target weght of shrmp n the th growout phase (e.g., g/shrmp). Defne N+1 0 D =0 and Ps = Cs, where C s s seed cost per unt (e.g., $/kg). Problem (2) then can be rewrtten as follows: D N -1 s s f m (3) =1 Max [V (P B -P D )-C -C -HC ] s.t. 0, f D >Dmax G =g(d )= G max, f D <Dmn Gmax -(Dmax -D )(G max -G current ) (Dmax -D current ), otherwse (4) 0, f D >Dmax S =s(d )= Smax, f D <Dcurrent (Dmax -D )(Scurrent -S mn ) (Dmax -D current )+S mn, otherwse (5) W B = W DS -1 (6) V (D +B ) W -W C =P 2 G -1 f f FR (7) W -W G -1 m m C =P H V (B -D ) H +1 max mn (8) (9) +1 0, f V(B -D )=0 HC = C h, otherwse (10) P = C, D =0, (11) 0 N+1 s s 14 CTSA Publcaton No. 153

21 where G and S are the growth (e.g., g/week) and survval rates (%) of shrmp n the th growout phase and equatons (4) and (5) are the correspondng densty-dependent growth and survval functons used to estmate the mpacts of densty on growth and survval. As llustrated n Fgures 1 and 2, D max s the densty at the carryng capacty (e.g., kg/m 2 ), the current practce, and G max and ts assocated mnmum possble densty. Smlarly, Dcurrent s the densty under D mn are the maxmum possble growth (e.g., g/week) and S current s the survval rate under the current densty level ( D current ) and S m n s the survval rate at the carryng capacty ( D max ). Equaton (6) defnes that the densty at the end of the th growout phase ( B ) s the product of the densty at the begnnng of the th growout phase ( D ), the assocated survval rate ( S ), and the rate of ncreased shrmp weght ( W ). Equaton (7) estmates total feed costs n the th growout phase, where P - 1 f W and FR are, respectvely, feed cost per unt (e.g., $/kg) and average feedng rate n terms of percentage of the prevalng bomass. Equaton (8) calculates total mantenance costs, where Pm s mantenance cost per unt (e.g., $/week). Equaton (9) specfes the maxmum and mnmum possble amount of shrmp that can be harvested by the th harvest. Equaton (10) s the quas-fxed harvest cost functon. Fgure A-1. Densty-dependent growth. Note: D max denotes the densty at the carryng capacty (e.g., kg/m 2 ); D current and G current denote the densty (e.g., kg/m 2 ) and growth (e.g., g/week) under the current practce; G max and D denote the mn maxmum possble growth (e.g., g/week) and assocated mnmum possble densty (e.g., kg/m 2 ). Shrmp Partal Harvestng Model: Decson Support System User Manual 15

22 Fgure A-2. Densty-dependent survval. Note: D max denotes the densty at the carryng capacty (e.g., kg/m 2 ), D current and S current denote the densty (e.g., kg/m 2 ) and survval rate (%) under the current practce, and S mn denotes the survval rate (%) at carryng capacty. 16 CTSA Publcaton No. 153

23 B. Installng the Solver The Frontlne Solver s requred n order to run ths Shrmp Partal Harvestng Model. If the Solver add-n has already been actvated n your Mcrosoft Excel software, t wll appear n your Tools drop-down menu. Fgure B-1. Solver appears n the Tools menu of Excel. Shrmp Partal Harvestng Model: Decson Support System User Manual 17

24 B.1. Solver s not located n the Tools menu If the Solver s not located n the Tools menu, the followng steps wll assst you n loadng the Solver add-n. 1) Start Excel and clck on Tools on the menu. Then clck on Add-ns... 2) A box should appear wth a lst of add-ns. If there s no checkbox for Solver Add-n, go to B.2. on the next page. 3) Check the checkbox for the Solver add-n 4) Clck OK. 5) The Solver should now be lsted on the Tools menu n Excel (see Fgure B-1). Fgure B-2. Select the Solver add-n. 18 CTSA Publcaton No. 153

25 B.2. Solver s not located n the lst of avalable add-ns. If the Solver s not located n your lst of add-ns, you wll need your Mcrosoft Offce CD-ROM. The followng steps wll assst you n nstallng the Solver add-n. 1) Insert the Mcrosoft CD-ROM. If the CD does not run the setup program automatcally, open My Computer, locate and double-clck the setup.exe fle on the CD. 2) Clck the Add or Remove Features button. 3) In the graphc that then appears, clck the lttle plus sgn next to Mcrosoft Excel for Wndows. Ths opens up the outlne under that box. 4) Clck the plus sgn next to Add-ns n order to expand the lst. The Solver should be lsted n the expanded lst of add-ins. 5) Clck on Solver and choose Run from My Computer, so that the box s whte, wth no yellow 1. Ths pcture llustrates how t should look when you re done. 6) Then clck Update Now to proceed wth the nstallaton. Dependng on your verson of Mcrosoft Offce, your screen may be dfferent, but ths procedure wll be smlar. 7) Go to step B.1 on the prevous page. Fgure B-3. Installng the Excel Solver add-n. Shrmp Partal Harvestng Model: Decson Support System User Manual 19

26 C. Macros used n the Model Ths Shrmp Partal Harvestng Model contans macros and executable VBA scrpt. Macros must be enabled n order to use ths Model. When openng ths Model n Mcrosoft Excel, the followng warnng about macros may appear: C.1. Enablng macros Clck on the Enable Macros optons n order for ths Model to run properly. Fgure C-1. Enable Macros dalog box. If you open the fle and the Enable Macros dalog box does not appear, please change your securty settngs n Excel (see C.2. on the next page). 20 CTSA Publcaton No. 153

27 C.2. Changng your securty settngs 1) From the Excel Tools Menu, select Macro > Securty. Fgure C-2. Changng Macro Securty settngs. Shrmp Partal Harvestng Model: Decson Support System User Manual 21

28 2) In the Dalog box, select Medum securty 3) Press OK. 4) Close the fle, and then open t agan. You should see the Enable Macros dalog box (Fgure C-1) Fgure C-3. Changng the macro securty level. 22 CTSA Publcaton No. 153

29 For more nformaton, please contact the Center for Tropcal and Subtropcal Aquaculture Oceanc Insttute Kalanana`ole Hwy. Wamanalo, HI Tel: (808) Fax: (808) Unversty of Hawa` 3050 Male Way, Glmore 124 Honolulu, HI Tel: (808) Fax: (808)

DEFINING %COMPLETE IN MICROSOFT PROJECT

DEFINING %COMPLETE IN MICROSOFT PROJECT CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMI-SP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,

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

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

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

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

Canon NTSC Help Desk Documentation

Canon NTSC Help Desk Documentation Canon NTSC Help Desk Documentaton READ THIS BEFORE PROCEEDING Before revewng ths documentaton, Canon Busness Solutons, Inc. ( CBS ) hereby refers you, the customer or customer s representatve or agent

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

VRT012 User s guide V0.1. Address: Žirmūnų g. 27, Vilnius LT-09105, Phone: (370-5) 2127472, Fax: (370-5) 276 1380, Email: info@teltonika.

VRT012 User s guide V0.1. Address: Žirmūnų g. 27, Vilnius LT-09105, Phone: (370-5) 2127472, Fax: (370-5) 276 1380, Email: info@teltonika. 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 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

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

How To Get A Tax Refund On A Retirement Account

How To Get A Tax Refund On A Retirement Account CED0105200808 Amerprse Fnancal Servces, Inc. 70400 Amerprse Fnancal Center Mnneapols, MN 55474 Incomng Account Transfer/Exchange/ Drect Rollover (Qualfed Plans Only) for Amerprse certfcates, Columba mutual

More information

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

To manage leave, meeting institutional requirements and treating individual staff members fairly and consistently.

To manage leave, meeting institutional requirements and treating individual staff members fairly and consistently. Corporate Polces & Procedures Human Resources - Document CPP216 Leave Management Frst Produced: Current Verson: Past Revsons: Revew Cycle: Apples From: 09/09/09 26/10/12 09/09/09 3 years Immedately Authorsaton:

More information

Traffic-light a stress test for life insurance provisions

Traffic-light a stress test for life insurance provisions MEMORANDUM Date 006-09-7 Authors Bengt von Bahr, Göran Ronge Traffc-lght a stress test for lfe nsurance provsons Fnansnspetonen P.O. Box 6750 SE-113 85 Stocholm [Sveavägen 167] Tel +46 8 787 80 00 Fax

More information

Nordea G10 Alpha Carry Index

Nordea G10 Alpha Carry Index Nordea G10 Alpha Carry Index Index Rules v1.1 Verson as of 10/10/2013 1 (6) Page 1 Index Descrpton The G10 Alpha Carry Index, the Index, follows the development of a rule based strategy whch nvests and

More information

LIFETIME INCOME OPTIONS

LIFETIME INCOME OPTIONS LIFETIME INCOME OPTIONS May 2011 by: Marca S. Wagner, Esq. The Wagner Law Group A Professonal Corporaton 99 Summer Street, 13 th Floor Boston, MA 02110 Tel: (617) 357-5200 Fax: (617) 357-5250 www.ersa-lawyers.com

More information

Vembu StoreGrid Windows Client Installation Guide

Vembu StoreGrid Windows Client Installation Guide Ser v cepr ov dered t on Cl enti nst al l at ongu de W ndows Vembu StoreGrd Wndows Clent Installaton Gude Download the Wndows nstaller, VembuStoreGrd_4_2_0_SP_Clent_Only.exe To nstall StoreGrd clent on

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

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

7.5. Present Value of an Annuity. Investigate

7.5. Present Value of an Annuity. Investigate 7.5 Present Value of an Annuty Owen and Anna are approachng retrement and are puttng ther fnances n order. They have worked hard and nvested ther earnngs so that they now have a large amount of money on

More information

VIP X1600 M4S Encoder module. Installation and Operating Manual

VIP X1600 M4S Encoder module. Installation and Operating Manual VIP X1600 M4S Encoder module en Installaton and Operatng Manual VIP X1600 XFM4 VIP X1600 Table of Contents en 3 Table of Contents 1 Preface 7 1.1 About ths Manual 7 1.2 Conventons n ths Manual 7 1.3 Intended

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

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

Small pots lump sum payment instruction

Small pots lump sum payment instruction For customers Small pots lump sum payment nstructon Please read these notes before completng ths nstructon About ths nstructon Use ths nstructon f you re an ndvdual wth Aegon Retrement Choces Self Invested

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

Section 5.4 Annuities, Present Value, and Amortization

Section 5.4 Annuities, Present Value, and Amortization Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today

More information

Tuition Fee Loan application notes

Tuition Fee Loan application notes Tuton Fee Loan applcaton notes for new part-tme EU students 2012/13 About these notes These notes should be read along wth your Tuton Fee Loan applcaton form. The notes are splt nto three parts: Part 1

More information

Study on Model of Risks Assessment of Standard Operation in Rural Power Network

Study on Model of Risks Assessment of Standard Operation in Rural Power Network Study on Model of Rsks Assessment of Standard Operaton n Rural Power Network Qngj L 1, Tao Yang 2 1 Qngj L, College of Informaton and Electrcal Engneerng, Shenyang Agrculture Unversty, Shenyang 110866,

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

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

Updating the E5810B firmware

Updating the E5810B firmware Updatng the E5810B frmware NOTE Do not update your E5810B frmware unless you have a specfc need to do so, such as defect repar or nstrument enhancements. If the frmware update fals, the E5810B wll revert

More information

A Secure Password-Authenticated Key Agreement Using Smart Cards

A Secure Password-Authenticated Key Agreement Using Smart Cards A Secure Password-Authentcated Key Agreement Usng Smart Cards Ka Chan 1, Wen-Chung Kuo 2 and Jn-Chou Cheng 3 1 Department of Computer and Informaton Scence, R.O.C. Mltary Academy, Kaohsung 83059, Tawan,

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

Texas Instruments 30X IIS Calculator

Texas Instruments 30X IIS Calculator Texas Instruments 30X IIS Calculator Keystrokes for the TI-30X IIS are shown for a few topcs n whch keystrokes are unque. Start by readng the Quk Start secton. Then, before begnnng a specfc unt of the

More information

IMPACT ANALYSIS OF A CELLULAR PHONE

IMPACT ANALYSIS OF A CELLULAR PHONE 4 th ASA & μeta Internatonal Conference IMPACT AALYSIS OF A CELLULAR PHOE We Lu, 2 Hongy L Bejng FEAonlne Engneerng Co.,Ltd. Bejng, Chna ABSTRACT Drop test smulaton plays an mportant role n nvestgatng

More information

A Performance Analysis of View Maintenance Techniques for Data Warehouses

A Performance Analysis of View Maintenance Techniques for Data Warehouses A Performance Analyss of Vew Mantenance Technques for Data Warehouses Xng Wang Dell Computer Corporaton Round Roc, Texas Le Gruenwald The nversty of Olahoma School of Computer Scence orman, OK 739 Guangtao

More information

INVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMA-HDR NETWORKS

INVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMA-HDR NETWORKS 21 22 September 2007, BULGARIA 119 Proceedngs of the Internatonal Conference on Informaton Technologes (InfoTech-2007) 21 st 22 nd September 2007, Bulgara vol. 2 INVESTIGATION OF VEHICULAR USERS FAIRNESS

More information

ELM for Exchange version 5.5 Exchange Server Migration

ELM for Exchange version 5.5 Exchange Server Migration ELM for Exchange verson 5.5 Exchange Server Mgraton Copyrght 06 Lexmark. All rghts reserved. Lexmark s a trademark of Lexmark Internatonal, Inc., regstered n the U.S. and/or other countres. All other trademarks

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

YIELD CURVE FITTING 2.0 Constructing Bond and Money Market Yield Curves using Cubic B-Spline and Natural Cubic Spline Methodology.

YIELD CURVE FITTING 2.0 Constructing Bond and Money Market Yield Curves using Cubic B-Spline and Natural Cubic Spline Methodology. YIELD CURVE FITTING 2.0 Constructng Bond and Money Market Yeld Curves usng Cubc B-Splne and Natural Cubc Splne Methodology Users Manual YIELD CURVE FITTING 2.0 Users Manual Authors: Zhuosh Lu, Moorad Choudhry

More information

How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence

How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence 1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh

More information

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by 6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng

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

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

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

A system for real-time calculation and monitoring of energy performance and carbon emissions of RET systems and buildings

A system for real-time calculation and monitoring of energy performance and carbon emissions of RET systems and buildings A system for real-tme calculaton and montorng of energy performance and carbon emssons of RET systems and buldngs Dr PAAIOTIS PHILIMIS Dr ALESSADRO GIUSTI Dr STEPHE GARVI CE Technology Center Democratas

More information

Allocating Collaborative Profit in Less-than-Truckload Carrier Alliance

Allocating Collaborative Profit in Less-than-Truckload Carrier Alliance J. Servce Scence & Management, 2010, 3: 143-149 do:10.4236/jssm.2010.31018 Publshed Onlne March 2010 (http://www.scrp.org/journal/jssm) 143 Allocatng Collaboratve Proft n Less-than-Truckload Carrer Allance

More information

Software project management with GAs

Software project management with GAs Informaton Scences 177 (27) 238 241 www.elsever.com/locate/ns Software project management wth GAs Enrque Alba *, J. Francsco Chcano Unversty of Málaga, Grupo GISUM, Departamento de Lenguajes y Cencas de

More information

CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol

CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK Sample Stablty Protocol Background The Cholesterol Reference Method Laboratory Network (CRMLN) developed certfcaton protocols for total cholesterol, HDL

More information

Staff Paper. Farm Savings Accounts: Examining Income Variability, Eligibility, and Benefits. Brent Gloy, Eddy LaDue, and Charles Cuykendall

Staff Paper. Farm Savings Accounts: Examining Income Variability, Eligibility, and Benefits. Brent Gloy, Eddy LaDue, and Charles Cuykendall SP 2005-02 August 2005 Staff Paper Department of Appled Economcs and Management Cornell Unversty, Ithaca, New York 14853-7801 USA Farm Savngs Accounts: Examnng Income Varablty, Elgblty, and Benefts Brent

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

Instructions for Analyzing Data from CAHPS Surveys:

Instructions for Analyzing Data from CAHPS Surveys: Instructons for Analyzng Data from CAHPS Surveys: Usng the CAHPS Analyss Program Verson 4.1 Purpose of ths Document...1 The CAHPS Analyss Program...1 Computng Requrements...1 Pre-Analyss Decsons...2 What

More information

Sciences Shenyang, Shenyang, China.

Sciences Shenyang, Shenyang, China. Advanced Materals Research Vols. 314-316 (2011) pp 1315-1320 (2011) Trans Tech Publcatons, Swtzerland do:10.4028/www.scentfc.net/amr.314-316.1315 Solvng the Two-Obectve Shop Schedulng Problem n MTO Manufacturng

More information

CISCO SPA500G SERIES REFERENCE GUIDE

CISCO SPA500G SERIES REFERENCE GUIDE CISCO SPA500G SERIES REFERENCE GUIDE Part of the Csco Small Busness Pro Seres, the SIP based Csco SPA504G 4-Lne IP phone wth 2-port swtch has been tested to ensure comprehensve nteroperablty wth equpment

More information

Improved SVM in Cloud Computing Information Mining

Improved SVM in Cloud Computing Information Mining Internatonal Journal of Grd Dstrbuton Computng Vol.8, No.1 (015), pp.33-40 http://dx.do.org/10.1457/jgdc.015.8.1.04 Improved n Cloud Computng Informaton Mnng Lvshuhong (ZhengDe polytechnc college JangSu

More information

GENESYS BUSINESS MANAGER

GENESYS BUSINESS MANAGER GENESYS BUSINESS MANAGER e-manager Onlne Conference User Account Admnstraton User Gude Ths User Gude contans the followng sectons: Mnmum Requrements...3 Gettng Started...4 Sgnng On to Genesys Busness Manager...7

More information

Optimization of network mesh topologies and link capacities for congestion relief

Optimization of network mesh topologies and link capacities for congestion relief Optmzaton of networ mesh topologes and ln capactes for congeston relef D. de Vllers * J.M. Hattngh School of Computer-, Statstcal- and Mathematcal Scences Potchefstroom Unversty for CHE * E-mal: rwddv@pu.ac.za

More information

An Evaluation of the Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Lifecycle Products and Services

An Evaluation of the Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Lifecycle Products and Services An Evaluaton of the Extended Logstc, Smple Logstc, and Gompertz Models for Forecastng Short Lfecycle Products and Servces Charles V. Trappey a,1, Hsn-yng Wu b a Professor (Management Scence), Natonal Chao

More information

UTILIZING MATPOWER IN OPTIMAL POWER FLOW

UTILIZING MATPOWER IN OPTIMAL POWER FLOW UTILIZING MATPOWER IN OPTIMAL POWER FLOW Tarje Krstansen Department of Electrcal Power Engneerng Norwegan Unversty of Scence and Technology Trondhem, Norway Tarje.Krstansen@elkraft.ntnu.no Abstract Ths

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

EE31 Series. Manual. Logger & Visualisation Software. BA_EE31_VisuLoggerSW_01_eng // Technical data are subject to change V1.0

EE31 Series. Manual. Logger & Visualisation Software. BA_EE31_VisuLoggerSW_01_eng // Technical data are subject to change V1.0 EE31 Seres Manual Logger & Vsualsaton Software BA_EE31_VsuLoggerSW_01_eng // Techncal data are subject to change V1.0 Logger & Vsualsaton Software - EE31 Seres GENERAL Ths software has been developed by

More information

Reporting Forms ARF 113.0A, ARF 113.0B, ARF 113.0C and ARF 113.0D FIRB Corporate (including SME Corporate), Sovereign and Bank Instruction Guide

Reporting Forms ARF 113.0A, ARF 113.0B, ARF 113.0C and ARF 113.0D FIRB Corporate (including SME Corporate), Sovereign and Bank Instruction Guide Reportng Forms ARF 113.0A, ARF 113.0B, ARF 113.0C and ARF 113.0D FIRB Corporate (ncludng SME Corporate), Soveregn and Bank Instructon Gude Ths nstructon gude s desgned to assst n the completon of the FIRB

More information

How To Calculate The Accountng Perod Of Nequalty

How To Calculate The Accountng Perod Of Nequalty Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.

More information

SPEE Recommended Evaluation Practice #6 Definition of Decline Curve Parameters Background:

SPEE Recommended Evaluation Practice #6 Definition of Decline Curve Parameters Background: SPEE Recommended Evaluaton Practce #6 efnton of eclne Curve Parameters Background: The producton hstores of ol and gas wells can be analyzed to estmate reserves and future ol and gas producton rates and

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

Kiel Institute for World Economics Duesternbrooker Weg 120 24105 Kiel (Germany) Kiel Working Paper No. 1120

Kiel Institute for World Economics Duesternbrooker Weg 120 24105 Kiel (Germany) Kiel Working Paper No. 1120 Kel Insttute for World Economcs Duesternbrooker Weg 45 Kel (Germany) Kel Workng Paper No. Path Dependences n enture Captal Markets by Andrea Schertler July The responsblty for the contents of the workng

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

Analysis of Premium Liabilities for Australian Lines of Business

Analysis of Premium Liabilities for Australian Lines of Business Summary of Analyss of Premum Labltes for Australan Lnes of Busness Emly Tao Honours Research Paper, The Unversty of Melbourne Emly Tao Acknowledgements I am grateful to the Australan Prudental Regulaton

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

1.1 The University may award Higher Doctorate degrees as specified from time-to-time in UPR AS11 1.

1.1 The University may award Higher Doctorate degrees as specified from time-to-time in UPR AS11 1. HIGHER DOCTORATE DEGREES SUMMARY OF PRINCIPAL CHANGES General changes None Secton 3.2 Refer to text (Amendments to verson 03.0, UPR AS02 are shown n talcs.) 1 INTRODUCTION 1.1 The Unversty may award Hgher

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

How To Solve An Onlne Control Polcy On A Vrtualzed Data Center

How To Solve An Onlne Control Polcy On A Vrtualzed Data Center Dynamc Resource Allocaton and Power Management n Vrtualzed Data Centers Rahul Urgaonkar, Ulas C. Kozat, Ken Igarash, Mchael J. Neely urgaonka@usc.edu, {kozat, garash}@docomolabs-usa.com, mjneely@usc.edu

More information

The Choice of Direct Dealing or Electronic Brokerage in Foreign Exchange Trading

The Choice of Direct Dealing or Electronic Brokerage in Foreign Exchange Trading The Choce of Drect Dealng or Electronc Brokerage n Foregn Exchange Tradng Mchael Melvn & Ln Wen Arzona State Unversty Introducton Electronc Brokerage n Foregn Exchange Start from a base of zero n 1992

More information

Section 5.3 Annuities, Future Value, and Sinking Funds

Section 5.3 Annuities, Future Value, and Sinking Funds Secton 5.3 Annutes, Future Value, and Snkng Funds Ordnary Annutes A sequence of equal payments made at equal perods of tme s called an annuty. The tme between payments s the payment perod, and the tme

More information

Stress test for measuring insurance risks in non-life insurance

Stress test for measuring insurance risks in non-life insurance PROMEMORIA Datum June 01 Fnansnspektonen Författare Bengt von Bahr, Younes Elonq and Erk Elvers Stress test for measurng nsurance rsks n non-lfe nsurance Summary Ths memo descrbes stress testng of nsurance

More information

Chapter 4 ECONOMIC DISPATCH AND UNIT COMMITMENT

Chapter 4 ECONOMIC DISPATCH AND UNIT COMMITMENT Chapter 4 ECOOMIC DISATCH AD UIT COMMITMET ITRODUCTIO A power system has several power plants. Each power plant has several generatng unts. At any pont of tme, the total load n the system s met by the

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

SUPPLIER FINANCING AND STOCK MANAGEMENT. A JOINT VIEW.

SUPPLIER FINANCING AND STOCK MANAGEMENT. A JOINT VIEW. SUPPLIER FINANCING AND STOCK MANAGEMENT. A JOINT VIEW. Lucía Isabel García Cebrán Departamento de Economía y Dreccón de Empresas Unversdad de Zaragoza Gran Vía, 2 50.005 Zaragoza (Span) Phone: 976-76-10-00

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

Faraday's Law of Induction

Faraday's Law of Induction Introducton Faraday's Law o Inducton In ths lab, you wll study Faraday's Law o nducton usng a wand wth col whch swngs through a magnetc eld. You wll also examne converson o mechanc energy nto electrc energy

More information

Chapter 7: Answers to Questions and Problems

Chapter 7: Answers to Questions and Problems 19. Based on the nformaton contaned n Table 7-3 of the text, the food and apparel ndustres are most compettve and therefore probably represent the best match for the expertse of these managers. Chapter

More information

Activity Scheduling for Cost-Time Investment Optimization in Project Management

Activity Scheduling for Cost-Time Investment Optimization in Project Management PROJECT MANAGEMENT 4 th Internatonal Conference on Industral Engneerng and Industral Management XIV Congreso de Ingenería de Organzacón Donosta- San Sebastán, September 8 th -10 th 010 Actvty Schedulng

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

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

1 Example 1: Axis-aligned rectangles

1 Example 1: Axis-aligned rectangles COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture # 6 Scrbe: Aaron Schld February 21, 2013 Last class, we dscussed an analogue for Occam s Razor for nfnte hypothess spaces that, n conjuncton

More information

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy Fnancal Tme Seres Analyss Patrck McSharry patrck@mcsharry.net www.mcsharry.net Trnty Term 2014 Mathematcal Insttute Unversty of Oxford Course outlne 1. Data analyss, probablty, correlatons, vsualsaton

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

A Cost-Effective Strategy for Intermediate Data Storage in Scientific Cloud Workflow Systems

A Cost-Effective Strategy for Intermediate Data Storage in Scientific Cloud Workflow Systems A Cost-Effectve Strategy for Intermedate Data Storage n Scentfc Cloud Workflow Systems Dong Yuan, Yun Yang, Xao Lu, Jnjun Chen Faculty of Informaton and Communcaton Technologes, Swnburne Unversty of Technology

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

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

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

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

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

Help is a tou ch of a button away. Telecare - keeping you safe and independent in your own home. i Personal emergency equipment

Help is a tou ch of a button away. Telecare - keeping you safe and independent in your own home. i Personal emergency equipment Help s a tou ch of a button away Telecare - keepng you safe and ndependent n your own home Personal emergency equpment 24/7 moble response - ncludng a dgnty savng lftng servce Professonal support Welcome

More information

Single and multiple stage classifiers implementing logistic discrimination

Single and multiple stage classifiers implementing logistic discrimination Sngle and multple stage classfers mplementng logstc dscrmnaton Hélo Radke Bttencourt 1 Dens Alter de Olvera Moraes 2 Vctor Haertel 2 1 Pontfíca Unversdade Católca do Ro Grande do Sul - PUCRS Av. Ipranga,

More information

Implementation of Deutsch's Algorithm Using Mathcad

Implementation of Deutsch's Algorithm Using Mathcad Implementaton of Deutsch's Algorthm Usng Mathcad Frank Roux The followng s a Mathcad mplementaton of Davd Deutsch's quantum computer prototype as presented on pages - n "Machnes, Logc and Quantum Physcs"

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

Uncrystallised funds pension lump sum payment instruction

Uncrystallised funds pension lump sum payment instruction For customers Uncrystallsed funds penson lump sum payment nstructon Don t complete ths form f your wrapper s derved from a penson credt receved followng a dvorce where your ex spouse or cvl partner had

More information

NPAR TESTS. One-Sample Chi-Square Test. Cell Specification. Observed Frequencies 1O i 6. Expected Frequencies 1EXP i 6

NPAR TESTS. One-Sample Chi-Square Test. Cell Specification. Observed Frequencies 1O i 6. Expected Frequencies 1EXP i 6 PAR TESTS If a WEIGHT varable s specfed, t s used to replcate a case as many tmes as ndcated by the weght value rounded to the nearest nteger. If the workspace requrements are exceeded and samplng has

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