...Tesla Motors, Inc.’s 5 most valuable assets and their inherent risks Tesla’s most valuable assets are the ones in the company’s control and on which the company’s future growth will largely depend. This paper identifies: the CEO, customer loyalty, execution of strategy, workforce, and hardware and software systems security as the most important drivers of growth in the company’s control. 1. CEO Elon Musk: One of Tesla’s most valuable assets is CEO Elon Musk. Musk is a charismatic leader who is admired by the company’s employees. Talented people choose his company because they believe that, by working for a person with his track record, they will be a part of something great.1 Elon Musk made his millions selling PayPal to eBay. He is also the CEO and founder of Space X and serves as the chairman at SolarCity, where he plans to bring solar Photovoltaic (PV)...
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...000 2x + 5y + 3z + S2 = 2,500 8x + 10y + 3z + S3 = 10,000 Initial Simplex Tableau Solution Variables Products X Y Z Slack Variables S1 S2 S3 S1 5 3 6 1 0 0 3,000 S2 2 5 3 0 1 0 2,500 S3 8 10 3 0 0 0 10,000 Z 20 18 16 0 0 0 0 (B) Initial Simplex Tableau Solution Variables Products X Y Z Slack Variables S₁ S₂ S₃ S₁ 5 3 6 1 0 0 3,000 S₂ 2 5 3 0 1 0 2,500 S₃ 8 10 3 0 0 0 10,000 Z 20 18 16 0 0 0 0 3,000/5 = 600 2,500/2 = 1,250 10,000/8 = 1,250 Therefore, the pivot element in row S₁ and column X is 5. Dividing the non-zero elements in row S₁ by 5 to make the pivot element 1: S₁ 5/5 3/5...
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...deadline: June 3rd (week 5) The paper should contain a minimum of 2000 words (4 A4’s) + pictures / diagrams etc. (added to the 4 A4’s) - 2 A4’s consist of a summary on the subjects within the theme International Distribution, incl. literature list (min. 5 references) - 2 A4’s consist of a case study applying the theoretical knowledge of the first A4’s to a company of your own choice - Literature list (min. 5 written references, no URL’s) - ½ A4 (250 words) consists of your own opinion about the paper Assignment 1: International distribution paper; deadline: June 3rd (week 5) The paper should contain a minimum of 2000 words (4 A4’s) + pictures / diagrams etc. (added to the 4 A4’s) - 2 A4’s consist of a summary on the subjects within the theme International Distribution, incl. literature list (min. 5 references) - 2 A4’s consist of a case study applying the theoretical knowledge of the first A4’s to a company of your own choice - Literature list (min. 5 written references, no URL’s) - ½ A4 (250 words) consists of your own opinion about the paper Assignment 1: International distribution paper; deadline: June 3rd (week 5) The paper should contain a minimum of 2000 words (4 A4’s) + pictures / diagrams etc. (added to the 4 A4’s) - 2 A4’s consist of a summary on the subjects within the theme International Distribution, incl. literature list (min. 5 references) - 2 A4’s consist of a case study applying the theoretical knowledge of the first A4’s to a company of your...
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...altre grandezze d’interesse 2 Piani delle attività • L’input è il grafo delle precedenze generalizzate H = (V, F) con insieme di attività V e insieme di archi F (precedenze generalizzate), unitamente al vettore delle durate delle attività d R+|V| • Principale prodotto dell’ottimizzazione è il piano temporale delle attività (schedule) che soddisfi tutti i vincoli di precedenza e ottimizzi una specifica funzione obiettivo. • Il piano può essere rappresentato associando a ogni attività i V una variabile reale si che indica l’istante iniziale dell’attività. • Il piano è quindi un vettore s R|V| • L’obiettivo è minimizzare la durata dell’intero progetto, ovvero la quantità: sn – s1 (inizio attività fine progetto – inizio attività inizio progetto) 3 Problema del makespan Def. Makespan: durata minima del progetto Problema del calcolo del makespan: trovare un piano s che soddisfi tutti i vincoli di precedenza e minimizzi la durata del progetto sn – s1 . • Questo problema può essere formulato come problema di PL, costruito a partire dal grafo delle precedenze generalizzate. • Introduciamo anche la variabile fi che...
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...hfltnfO{ ;w} kl/jf/sf] af]em 7fGg] cr]t k/Dk/f / k'Vof}{nL dfGotfnfO{ jt{dfg g]kfnL ;dfhn] klg 5f]8g ;s]sf] 5}g . hasL k/fk'j{sfnb]vl ;dfhdf o; lsl;dsf] efjgf ljBdfg /x]sf] kfOG5 ls dlxnf / k'?if Ps /ysf b'O kfªu|f x'g Pssf] cefjdf csf]{ ;+rfngdf /xg ;Sb}g . wfld{s b[li6sf]0faf6 klg oxl dfGotf /x]sf] kfOG5 ls dlxnf / k'?if Ps csf{ k|lt cfl>t 5g\ . CUj]bdf dlxnf / k'?if cyf{t klt,kTgLn] ;dfg :yfg xfl;n u/]sf] s'/f pNn]v ul/Psf] 5 . eg] dg'sf] egfOdf a|fDXfn] cfˆg} z/L/nfO{ b'O{ efudf ljefhg u/]/ g/ tyf gf/Lsf] ;[li6 u/]sf] s'/f pNn]v 5 .-g]kfn sfg'g kl/rrf{ )#%_ o; k|sf/ wd{u|GynfO{ cfwf/ dfg]/ x]g]{ xf] eg] klg dlxnf / k'?ifsf] pTklQ ;dfg cj:yfaf6 ePsf] TfYojf/] JofVof ul/Psf] kfOG5 . o;/L wd{u|Gydf ;dfg ?kdf x]/]klg dlxnf lj?4sf] e]befj xfd|f] ;dfhdf lhjg rs|sf ?kdf b]vfk/]sf] 5 . lxGb' wd{df b'uf{, eujtL,nIdL,;/:jtL,kfj{tL,/fwf h:tf b]jLsf ufyf kfOG5 . / lhjLt b]jLsf] ?kdf s'df/L nfO{ k'hf cr{gf ul/G5 . h:nfO{ dlxnfsf] k|lts sf ?kdf lnOG5 . h:fn] ubf{ lxGb' wd{df dlxnfnfO{ b]jL zQmL jf eujfgsf ?kdf k'hf cr{gf ul/P klg Jojxf/df eg] dlxnfnfO{ ckx]ngf jf s'g} j:t'sf ?kdf lnOG5 . dlxnfnfO{ ;fdGo xs clwsf/af6 klg jlGrt ul/G5 . lxGb' wd{df dlxnfsf] :yfg o;/L j0f{g jf k|:6fOPsf] 5 . — dlxnf eg]sf] glb xf], k'?if eg]s]f ;d'Gb| xf] . -dg'_ — dlxnfx? ;fgf] pd]/df afa'sf] lgoGq0fdf,ljjflxt cj:yfdf >ldfgsf] lgoGq0fdf eg] a'9of}nL cj:yfdf 5f]/f dfly lge{/ x'G5g .-dg':d[tL_ wfld{s plQmaf6 klg dlxnfsf] af/]...
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...West Indies 3 Tests, 5 ODIs, 2 T20 SRI LANKA WEST INDIES Host SA 3 Tests, 5 ODIs, 2 T20 ZIMBABWE BANGLADESH To Eng 2 Tests To Zimb tri-series SL Host Aus 5ODI To Scotland 1 ODI (Host WI Cancelled) Host Ban 3 ODIs Host Pakistan v Australia 2 Tests and 2T20 Host Pakistan 4T, 5ODIs, 2T20 (additional) To England 5 ODIs (Additional) To Pak (in Eng) 2 Tests, 2 T20Is (rescheduled from Host Ban 2 Tests (rescheduled from Aug 08) 4/5 ODIs (Additional) Asia Cup 3-4 matches To Zimbabwe 2T, Postponed to June v SL in USA 2 T20Is To Zimb tri-series Ind 4/5 ODIs (Additional) Asia Cup 3-4 matches To England 4 Tests and 5 ODIs (cancelled - a series played in 2009) Host Ind, SL tri-series 4/5 ODIs Host NZ Host NZ (resch. From Postponed to June Jun-10 Asia Cup 3-4 matches Host Aus in England 2 Tests, 2 T20Is (rescheduled from 2008) Asia Cup 3-4 matches To Eng 3 ODIs To Aus (POSTPONED) 2 Tests (rescheduled from Aug 08) To Pak (moved to Apr 2012) 2 Tests and 3 ODIs Jul-10 To SL 3 Tests (additional) 4-5 ODI tri-series with NZ (Additional) Host Ind 3 Tests (additional) Host Ind and NZ 4-5 ODIs tri - series (Additional) Aug-10 To SL To England 4-5 ODIs tri-series with 4T, 5ODIs, 2T20 (Additional) (additional) Sep-10 To India 2 Tests, 3 ODIs (Additional) 2 weeks warm up Host SL 3ODIs, 1 T20 (Additional) To Aus 5 Tests, 7 ODIs, 2T20Is Host England 5 Tests, 7 ODIs, 2 T20Is To SA (add - prev FTP) Host Pak 3 Tests and 5 ODIs 3 Tests and 5 ODIs (Additional) Jan-11...
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...Penerapan Algoritma Ant Colony Optimization (ACO) untuk Menyelesaikan Penjadwalan Flowshop dengan Tujuan Meminimumkan Makespan Oleh: Yuliana Vina Humira 25407044 Lisa Setyawati H. 25407052 Henry Rahardian S. 25407119 Yunita Hartanto 25408066 JURUSAN TEKNIK INDUSTRI FACULTY OF INDUSTRIAL TECHNOLOGY PETRA CHRISTIAN UNIVERSITY SURABAYA 2011 Penerapan Algoritma Ant Colony Optimization (ACO) untuk Menyelesaikan Penjadwalan Flowshop dengan Tujuan Meminimumkan Makespan 1. Pendahuluan Penjadwalan adalah proses pengambilan keputusan dimana melibatkan beragam sumber daya, antara lain manusia/pekerja, mesin, material, energy, uang, waktu, ruang, yang tersedia secara terbatas untuk menyelesaikan sekumpulan job dalam jangka waktu tertentu. Tujuan proses penjadwalan tergantung dengan apa yang ingin diminimalisasikan oleh perusahaan, misalnya meminimalkan keterlambatan, mean flow time, mean tardiness, makespan. Di dalam permasalahan penjadwalan flowshop untuk karakteristik n jobs m machines adalah waktu penyelesaian job yang sangat panjang sehingga efisiensi perusahaan rendah. Laporan ini akan membahas ant colony algorithm, yang merupakan algoritma metaheuristik yang bersifat konstruktif dengan tujuan meminimalisasikan makespan. 2. Ant Colony Algorithm (M’Hallah & Alhajraf, 2008) Ant Colony Optimization (ACO) algorithm merupakan salah satu algoritma kostruktif metaheuristik. ACO meniru perilaku koloni semut yang dikenal dengan perilaku sosialnya...
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...Online Market (Apple Iphone 5) The online market offers Apple Iphone 5 the ability to greatly expand their business. Apple Iphone 5... Innovation (Apple Iphone 5) Greater innovation can help Apple Iphone 5 to produce unique products and services that meet... New Services (Apple Iphone 5) New services help Apple Iphone 5 to better meet their customer’s needs. These services can expand... New Technology (Apple Iphone 5) New technology helps Apple Iphone 5 to better meet their customer’s needs with new and improved... new products (Apple Iphone 5) Please edit this page to add a description… New Markets (Apple Iphone 5) New markets allow Apple Iphone 5 to expand their business and diversify their portfolio of products... International Expansion (Apple Iphone 5) International high price (Apple Iphone 5) Please edit this page to add a description… Volatile Currencies (Apple Iphone 5) Volatile currencies make Apple Iphone 5’s investments difficult, because costs and revenues change... gray market (Apple Iphone 5) Please edit this page to add a description… Intense Competition (Apple Iphone 5) Intense completion can lower Apple Iphone 5’s profits, because competitors can entice consumers... Change in Tastes (Apple Iphone 5) Consumers can change their tastes very quickly. Apple Iphone 5 depends on knowing which goods and... Political Risk (Apple Iphone 5) Politics can increase Apple Iphone 5’s risk factors, because governments can quickly change... Volatile...
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...:11 Unit Two LA .6J._-_.9.11 Drill 1. Hearing frontal and deep alif 1.D 7.D 2. F 8.F 3. D 9. F 4.D 5.F 11. F 6.F 12. D io. D Drill 2. Dictation 1. L! 2. L..$1.3 3. U I..! 4. 1..) Li 5. L.../ 1.) • • 6. C.31.3 Drill 3. Word recognition 1 . C.J1..$ . 2. C.-) Li 3. LA.). 4. lf....4.3 5. C.A..)12.1. 6 C.A..4:3 Drill 4. Dictation 1. ye,' 2. C...)3.3 3 ' L-) 33 4. C3y11; Answer Key Drill 5. Dictation 1. 2. 3. 1.1%-3 4. L31.! Drill 6. Distinguishing between long and short vowels 1. b 2. a 3. a 4. b 5. a 6. b Drill 7. Identifying long and short vowels 1. L 2.S 8. S 3. 9. L L 4. S 5. L 6.S 12. 7. S 10. L 11. S L Drill 8. FatHa dictation I CL.LL,f3 2. C2.)L:6 3. C-431..) 4. C...)0 5.L i 6. C2_4:11:3 Drill 10. Short vowel dictation .9 S. .9 2. .9 i 9 3. 4. L..)32.3 5. ‘2_42 2.:3 6. C.)9-,.j Drill 11. Dictation 9 3. 7. Lib 4. 8. Answer Key Drill 13. Letter connection 2. 41.,.S. 3. t-? L! 9 4 L..4..4..$ 10. . „. 5. Lit..! 11 L.7..,...,... ) 6 .J 3 8. 1..$33 . .LP-.1) . C.,_,.,3 Unit Three ZJWI ii.J.,- .911 Drill 1. Dictation 1. L..J12• • 2. ei 3. 49? -9 4 L...,..,..P- 5 L...a.'' 62- Drill 2. Dictation 1. C.33.2s 2. t.....3 :-"; 4. C..4.Pc:i 5....
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...2 3 4 5 6 7 8 9 10 11 12 13 Task Mode Task Name Duration Start Finish Predecessors ug 3, '14 M T W T F S IT314_CamilleGabol_ePaymentApplication days 59.5 Project Initiation Analysis and Design Initial Software Package Software Package Evaluation Final Package Selection Implementation Services RFP Bidding Process and Vendor Implementation Vendor Award Software Installation User Acceptance Testing Communications and Web Site Cut-Over Final Implementation e-Payment Project Complete 10 days 15 days 2 days 5 days 4.5 days 3 wks 4 wks 5 days 2 wks 2 wks 3 days 40 hrs 0 days Thu 9/18/25 Thu 9/18/25 Thu 9/18/25 Thu 10/9/25 Mon 10/13/25 Mon 10/20/25 Fri 10/24/25 Fri 10/24/25 Fri 10/24/25 Fri 10/31/25 Fri 11/14/25 Fri 11/28/25 Wed 12/3/25 Wed 12/10/25 Wed 12/10/25 Wed 10/1/25 Wed 10/8/25 Fri 10/10/25 Fri 10/17/25 Fri 10/24/25 Fri 11/14/25 Fri 11/21/25 Fri 10/31/25 Fri 11/14/25 Fri 11/28/25 Wed 12/3/25 Wed 12/10/25 Wed 12/10/25 1,2 3 3,4 5 5 5 8 9 10 11 12,11 Task Split Milestone Inactive Summary Manual Task Duration-only Manual Summary Rollup Manual Summary Start-only Finish-only External Tasks External Milestone Deadline Progress Manual Progress Project: IT314_CamilleGabol_eP Date: Tue 8/5/14 Summary Project Summary Inactive Task Inactive Milestone Page 1 31, '25 T W T F S Sep 7, '25 S M T W T F S Sep 14, '25 S M T W T F S Sep 21, '25 S M T W T F S Sep 28, '25 S M T W ...
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...= 1/ 50 = 0.02 machines per hour µ = 1/15 = 0.0667 machines per hour 6 6! Po = n =o ( 6 − n ) ! 0.02 0.0667 n −1 = [13.92] = 0.0718 −1 ρ = Jimmy’s utilization = 1 − 0.0718 = 0.9282 Waiting Lines SUPPLEMENT C b. Average number of machines out of service. 0.0667 L = 6− (1 − 0.0718) = 6 − 3.095 = 2.905 machines 0.02 c. Average time a machine is out of service. W = 2.905 ( 6 − 2.905 )( 0.02 ) = 46.93 hours 3. Moore, Akin, and Payne (dental clinic). Multiple-server model. λ 5 s = 3, λ = 5, µ = 2 , ρ = = = 0.8333 sµ 3 ( 2 ) a. Probability of no patients, P0 = ( λ µ )n + ( λ µ ) s s −1 P0 = n! n =0 s! (5 2) + (5 2) + (5 2) 0! 1! 2! 0 = −1 1 1− ρ 1 2 = ( 5 2 )n + ( 5 2 ) s n! n =0 (5 2) + 3! { = [1 + 2.5 + 3.125] + 2.604 ( 6 ) = 2 } 3 3! 1 1− 5 6 −1 −1 1 1− 5 6 −1 1 = 0.04494 ( 6.625) + (15.625) b. The probability of 6 or more customers in the clinic is: P0 = 0.04494 (from part a), s = 3 for n < s for n ≥ s ( ) P = λ µ n n n! P0 Pn = ( ) λ µ n s ! s n− s P0 (5) P = 2...
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...Constraint Logic Programming in Prolog: Hanjie Puzzle Solver Lu´ Cleto and Jo˜o Marinheiro ıs a FEUP-PLOG, Turma 3MIEIC05, Group 23 {ei11077,ei11129}@fe.up.pt http://www.fe.up.pt Abstract. The purpose of this project was to use constraint logic programming in Prolog to implement a solver for the 2D puzzle, Hanjie. For this purpose we used the clp(FD) library provided by SICStus Prolog 4.2.3, specifically the sum/3 and automaton/3 combinatorial constraints. The program we developed is able to solve puzzles with dimensions up to 88x88, with only one possible solution, in less than one second. When there are multiple solutions, the execution time for the obtaining the first solution varies with the number of possible solutions. These results show that the execution time of the program is primarily affected by the amount of possible results. While larger grid dimensions do increase the execution time, the increase is linear if the number of possible solutions is maintained. On the other hand, increasing the number of possible solutions will lead to an exponential growth in execution time. 1 Introduction The goal of this project is to use constraint logic programming in Prolog to develop a logic program capable of solving a decision problem in the form of the 2D puzzle, Hanjie. This puzzle consists of a rectangular grid with ’clues’ on top of every column and to the left of every row that indicate the number and length of gray blocks in that column/row. To achieve this goal, first...
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...0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4 2 5 2 6 2 7 2 8 2 9 3 0 3 1 3 2 3 3 3 4 3 5 3 6 3 7 3 8 3 9 4 0 4 1 4 2 4 3 4 4 PiaeSbFr1La(edrA Ojc,eA Eetrs rvt u om_odsne s bet s vnAg) FlCuty) ilonr( EdSb n u PiaeSbFlCuty) rvt u ilonr( DmcnA NwSloncinsron i o s e qCneto(tCn) DmcdA NwSlomn( i m s e qCmad) cdCneto =cn m.oncin o cdCmadye=CmadyeTx m.omnTp omnTp.et cdCmadet="EETCutyD CutyaeFO Cuty m.omnTx SLC onrI, onrNm RM onr" DmojsA NwDtSt) i bD s e aae( Dmddpe A NwSlaadpe( i Aatr s e qDtAatr) ddpe.eetomn =cd AatrSlcCmad m cnOe( o.pn) ddpe.ilojs AatrFl(bD) cnCoe) o.ls( cbonr.auMme ="onrI" mCutyVleebr CutyD cbonr.ipaMme ="onrNm" mCutyDslyebr Cutyae cbonr.aaore=ojsTbe() mCutyDtSuc bD.als0 EdSb n u PiaeSbcbonr_eetdneCagdsne A Ojc,eA rvt u mCutySlceIdxhne(edr s bet s Eetrs vnAg) I cbonr.eetdau.otig)< " Te f mCutySlceVleTSrn( > " hn DmCutyDA Itgr= i onrI s nee CnetTIt2cbonr.eetdau.otig) ovr.on3(mCutySlceVleTSrn() FlSae(onrI) ilttsCutyD cbiySlceIdx=0 mCt.eetdne EdI n f EdSb n u PiaeSbFlSae(onrI A Itgr rvt u ilttscutyD s nee) DmcnA NwSloncinsron i o s e qCneto(tCn) DmcdA NwSlomn( i m s e qCmad) cdCneto =cn m.oncin o cdCmadye=CmadyeTx m.omnTp omnTp.et cdCmadet="EETSaeD SaeaeFO SaeWEE m.omnTx SLC ttI, ttNm RM tt HR CutyD=CutyD onrI @onrI" cdPrmtr.dWtVle"CutyD,cutyD m.aaeesAdihau(@onrI" onrI) DmojsA NwDtSt) i bD s e aae( Dmddpe A NwSlaadpe( i Aatr s e qDtAatr) ddpe.eetomn =cd AatrSlcCmad m cnOe( o.pn) ddpe.ilojs AatrFl(bD) cnCoe) o.ls(...
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