The safety stock and inventory cost paradox in a stochastic lead time setting
|
|
- Caroline Warner
- 6 years ago
- Views:
Transcription
1 Dsney, S.M., Malz, A., Wang, X. and Warburon, R., (05), The safey soc and nvenory cos paradox n a sochasc lead me seng, 6 h Producon and Operaons Managemen Socey Annual Conference, Washngon, USA, May 8 h May h, 0 pages. The safey soc and nvenory cos paradox n a sochasc lead me seng Sephen Dsney, Arnold Malz, Xun Wang and Roger Warburon 3 ) Logscs Sysems Dynamcs Group, Cardff Busness School, Cardff Unversy, UK ) W.P. Carey School of Busness, Arzona Sae Unversy USA 3) Dep of Admnsrave Scences, Meropolan College, Boson Unversy, USA DsneySM@cardff.ac.u Absrac We sudy a sochasc lead-me problem movaed by real world global shppng daa. Replenshmen quanes are generaed by he Order-Up-To polcy whch ams o acheve a sraegc avalably arge. We show ha unle he consan lead-me case, mnmum safey socs do no always lead o mnmum coss under sochasc lead-mes. Keywords: order up o polcy, sochasc lead me, order crossover Inroducon Global sourcng ofen allows access o low-cos supply bu s ofen assocaed wh long and varable lead mes (Blacburn 0). These longer and more varable lead mes brng wh hem a number of complcaons and poenal pfalls, from boh cos and servce perspecves (Sal 006). We add o he leraure on plannng wh sochasc lead mes by formulang and esng a calculaon of safey soc ha reflecs hese real-world complcaons. Our mehod allows for order crossover and correlaon beween ppelne nvenory and replenshmen orders, facors ha are ofen gnored. Usng a lnear modfcaon of he famlar Order-Up-To (OUT) orderng polcy we fnd a soluon ha always resuls n lower nvenory holdng and baclog coss. Praccally, we have raced and analyzed logscs daa for global supply chans for boh maor forwarders and realers and were sruc by he volaons of he lead me normaly assumpon see Fgure. When lead mes s hghly varable we may also have ssues wh order crossover, where shpmens are receved n a dfferen sequence from whch hey were dspached. Mos nvenory models do no allow for hs order crossover, ye varable shpmen delays, clercal errors, and random cusom nspecons can easly delay a shpmen long enough for ohers o pass. From he analycal perspecve, wo prescrpons for nvenory managemen are wdely dssemnaed. These approaches eher use an average (or maxmum) lead me n he consan lead me reorder pon soluon or assume ha he demand s normally dsrbued and hen use he mean and varance of a random sum of random varables o deermne he reorder pon (Feller, 966). We show ha neher approach s well-sued o global supply chans wh long rans mes and mulple hand-offs due o he mul-modal nvenory dsrbuon. Ths paper develops an exac heorecal reamen of he mpac of he sochasc lead mes wh order crossover on he probably densy funcon of he ne soc levels. As we progressed n our nvesgaons, we also began quesonng he well-nown asseron (Kaplan 970) ha he OUT model s always a good f for global supply chans. We fnd ha, when here s order crossover, beer economc performance s possble when he orderng sraegy
2 Dsney, S.M., Malz, A., Wang, X. and Warburon, R., (05), The safey soc and nvenory cos paradox n a sochasc lead me seng, 6 h Producon and Operaons Managemen Socey Annual Conference, Washngon, USA, May 8 h May h, 0 pages. Fgure Emprcal por-o-por lead me dsrbuons from Chan o he USA. follows he lnear proporonal order-up-o (POUT) polcy (Dsney and Lambrech, 008). The srucure of our paper s as follows. We frs defne he POUT polcy and hen show how o capure he effec of he sochasc lead mes analycally. Fnally, n order o valdae our heorecal resuls, we numercally analyze a sochasc lead me problem wh lnear nvenory holdng and baclog coss. The proporonal order-up-o polcy We frs brefly revew he POUT polcy before movng on o he case of sochasc lead mes wh order crossover. We assume hroughou ha a lnear sysem exss and ha demand, D, a me, s an ndependenly and dencally dsrbued (..d.) random varable drawn from a normal dsrbuon wh a mean of and a sandard devaon of. The POUT polcy, O, generaes orders a me, wh he followng dfference equaon (Dsney and Towll 003), O T I W. ()
3 Dsney, S.M., Malz, A., Wang, X. and Warburon, R., (05), The safey soc and nvenory cos paradox n a sochasc lead me seng, 6 h Producon and Operaons Managemen Socey Annual Conference, Washngon, USA, May 8 h May h, 0 pages. Here, he varable T, s a safey soc he mean nvenory; s he average lead-me; I s he on-hand nvenory a me ; and W s he on-order nvenory or wor n progress, WIP. The varable 0 s a proporonal feedbac conroller ha regulaes he speed a whch devaons n he nvenory poson are recovered. When, he POUT polcy degeneraes no he OUT polcy. The nvenory balance equaon defnes he nvenory I I R D, () where R O s he receps receved, he ncomng orders afer he sochasc lead me and he sequence of even delay (he ). The sysem s compleed when we defne he WIP, W O. (3) Modellng sochasc lead mes wh order crossover As he OUT/POUT polcy operaes on dscree me he sochasc lead mes mus also be dscreely dsrbued, whch s why we presened he daa n Fgure as a dscree dsrbuon. Le p be he probably ha he lead me of a parcular order s perods long, s an neger greaer han or equal o zero. The maxmum lead me s, he smalles lead me s 0, and he average lead me s p. h 0 Le M be a bnary marx wh = o columns and o rows. Assgn he, elemen of M a value accordng o m,, (4) where. Here x s he celng funcon. Each row of he M marx represens a -uple of bnary dgs ha descrbes he sae of he WIP ppelne. A zero n elemen m, of marx M ndcaes ha for sae, he order placed perods ago has been receved (he order s closed), uny ndcaes ha he order placed perods ago has no ye been receved ( s open). Noe, he order placed perods ago s always closed, hus ndexes from o o denoe he lead mes = 0 o. There are rows o M, one for each possble sae of he order ppelne. The probably ha he WIP ppelne s n sae s gven by q, (5) p One can derve (5) by observng ha n he h ppelne sae, he probably ha an order placed perods ago s open/closed can be expressed unversally as 3
4 Dsney, S.M., Malz, A., Wang, X. and Warburon, R., (05), The safey soc and nvenory cos paradox n a sochasc lead me seng, 6 h Producon and Operaons Managemen Socey Annual Conference, Washngon, USA, May 8 h May h, 0 pages. q, m, pm, p ( ) p, (6) and ha q s he produc of q, over. We now requre he varance of he nvenory levels n each of he sub-processes. We oban hs by frs deermnng he varance of he WIP n each sub-process and hen each subprocess s combned wh a scaled order o oban somehng we call he scaled shorfall dsrbuon sub-process, from whch we can oban he nvenory dsrbuon. We can rearrange () o oban I T W O. (7) For OUT polcy (ha s, when ) we can see ha he nvenory dsrbuon s a refleced shorfall dsrbuon, W O, T (Zalnd, 978; ranslaed by Robnson e al., 00). When he O componen has become scaled by O, n whch case we call he dsrbuon of W O he scaled shorfall dsrbuon. We now requre he mean and he varance of he scaled shorfall dsrbuon of each sub-process. The complcang facors are ha O s auo-correlaed and ha he dsrbuons of W and O are correlaed wh each oher. As he sysem s lnear he smples way o proceed s o explo he z-ransform, whch s defned by F z Z f f z. (8) 0 To deermne he varance of he WIP n sub-process,, we frs noe ha he varance of he orders mananed by he POUT polcy s ndependen of he lead-me, as O z 0 z 0 Here, z s he z-ransform operaor, Z. (9) d s he nverse z- Z F z F z z z f C Fz. ransform of ransfer funcon, z z s he ransfer funcon of he orders mananed by he POUT polcy under..d. demand and mnmum mean squared error forecasng (Dsney and Towll 003). The relaonshp beween he varance rao and he sum of he squared mpulse response s nown as Tsypn s (964) relaonshp. The probably densy funcon of he normal dsrbuon wh an argumen of x, a mean of, and a sandard devaon of, s defned by x e x,. (0) Usng hs noaon, (9) leads o an order process descrbed by he pdf, 4
5 Dsney, S.M., Malz, A., Wang, X. and Warburon, R., (05), The safey soc and nvenory cos paradox n a sochasc lead me seng, 6 h Producon and Operaons Managemen Socey Annual Conference, Washngon, USA, May 8 h May h, 0 pages. Fgure. The bullwhp rao for dfferen O x,. () The bullwhp rao, O, s ploed n Fgure. The rao s uny a, zero a 0, a, srcly ncreasng, and convex n. Noe ha he bullwhp rao and order varance are no affeced by he sochasc lead me. The varance of WIP sub-process,, s gven by he varance of he sum of he mpulse responses of he open orders, W, z m, Z 0 z. () where m, s an elemen of he bnary marx M ha capures wheher an order s open or closed. The dsrbuon of he scaled orders, O, for all sub-processes, can be obaned usng O O Z z z 0 0, (3) whch leads o he followng expressons for s pdf,, O x. (4) The covarance beween he WIP sub-process and he scaled orders sub-process s hen z z cov,, c 0 z z. (5) W O Z m Z ov W, O NS,, he varance of sub-process n he nvenory dsrbuon, s equal o he varance of he shorfall dsrbuon, whch s gven by NS S W O,,, cov W, O. (6) 5
6 Dsney, S.M., Malz, A., Wang, X. and Warburon, R., (05), The safey soc and nvenory cos paradox n a sochasc lead me seng, 6 h Producon and Operaons Managemen Socey Annual Conference, Washngon, USA, May 8 h May h, 0 pages. The mean of he each of he sub-processes of he nvenory dsrbuon can be shown o be NS,, The complee pdf nvenory dsrbuon s gven by T m. (7) NS q x NS,, NS,. (8) The varance of he complee, mul-modal, nvenory s gven by NS NS T x dxq m, NS,, (9) Equaon (9) shows ha he mean demand has an nfluence on he varance of he nvenory levels, somehng ha does no happen wh consan lead mes. Furhermore, we can see ha he nvenory varance also conans a weghed sum of ndvdual sub-process s varances. A numercal example when 4 Consder he suaon when 4. Table deals he ppelne saes M, he varance of he ne soc, and he mean of each of he 6 ndvdual sub-processes o he nvenory dsrbuon. I can be easly shown ha each of he expressons for he varance (and he sandard devaons) of he nvenory sub-processes s nfne a 0,. Furhermore, each sub-process has a sngle unque mnmum,, whch s also dealed n Table. We can see ha exss only n he sub-processes ha do no conan order cross-overs. All of he sub-processes ha conan order-crossover have. Ths leads us o speculae ha n cases where order crossover exss ha he POUT wll produce nvenory pdfs wh less varance han he OUT polcy and ha he opmal les n he regon, 0. To mae he resuls n Table specfc, we frs need o specfy he lead me probables assume p0, p p p3 0, p4. The maxmum lead me s 4 and he average lead me s. Usng (5) we are hen able o deermne he probably ha he ppelne s n sae, s, q Noe ha n general, he probably ha he ppelne s n a parcular sae need no be, and almos never s, he same as he probably ha he ppelne s anoher sae. Consder now ha he followng nvenory cos funcon J, exss J Eh ns b ns, (0) where h s he un nvenory holdng cos, and b s he un baclog cos. I s possble o show ha 6
7 Dsney, S.M., Malz, A., Wang, X. and Warburon, R., (05), The safey soc and nvenory cos paradox n a sochasc lead me seng, 6 h Producon and Operaons Managemen Socey Annual Conference, Washngon, USA, May 8 h May h, 0 pages. Fgure 3 Dsrbuon of he nvenory levels mananed by he OUT polcy for 90% avalably when p, p p p 0, p,, Table. Generc characerscs of he sub-processes when 4 M NS, NS, T T T T T T T T T 3 T T 3 T 3 T 4 T T T
8 Dsney, S.M., Malz, A., Wang, X. and Warburon, R., (05), The safey soc and nvenory cos paradox n a sochasc lead me seng, 6 h Producon and Operaons Managemen Socey Annual Conference, Washngon, USA, May 8 h May h, 0 pages. Fgure 4. The percenage economc gan from usng he POUT polcy T 4 T T 8 T T b h e e e 3 dj 0, () d 64 mplyng ha for all cos combnaons he OUT polcy s never opmal as here always exss a whch s more economcal. Togeher wh he varances of he ndvdual sub-processes dealed n Table, (4) and (8) we are now able o oban an expresson of he pdf of he nvenory levels, whch we plo n Fgure 3. Fgure 3 plos wo cases of he OUT polcy, and n boh we have se he safey soc, T, o mnmze J when h =, b = 9. In he frs case 00, and we can clearly see ha here are fve modes n he nvenory pdf. In he oher case, 40 and he fve modes overlap somewha. Furhermore, he complee pdf of he 40 case, has less varance, and requres less safey soc, han he 00 case. When 00, he nvenory levels have a varance of 0,300 for he OUT polcy. Numercal expermens show us ha here s a sngle mnmum nvenory varance (or sandard devaon) a 0.73 and he ne soc varance s 0,80 0.% less han he OUT varance. Usng numercal echnques we can fnd he opmal proporonal feedbac conroller, and safey soc T, ha mnmzes he nvenory cos. When we have se,t opmally, Fgure 4 descrbes he percenage economc gan JOUT JPOUT J OUT 00%, from usng he POUT polcy. Whle he mprovemen s raher small (noe ha 0.8 means 0.8% no 80%), he POUT s always more economcal han he OUT polcy. The economc benef ncreases as decreases. When he cos rao s such ha near 00% avalably s desrable,. Fgure 5 plos for dfferen cos raos and dfferen mean demands. We see ha s near uny when he avalably arge s (very) near 0% or 00%, bu for mos avalably arges Ineresngly, almos always, mplyng ha he ghes nvenory conrol does no always lead o he mnmal cos. Fgure 6 shows he safey soc requremens when s used for dfferen cos raos. The mul-model naure of he 00 case resuls n rapd ncreases n he safey soc requremen a predcable pons on he avalably scales. These are relaed o he mul-model pdf of he nvenory levels. Furhermore, beween 40-60% avalably, he wo demand sengs requre very smlar amouns of safey soc. As s no possble o vsually dsngush 8
9 Dsney, S.M., Malz, A., Wang, X. and Warburon, R., (05), The safey soc and nvenory cos paradox n a sochasc lead me seng, 6 h Producon and Operaons Managemen Socey Annual Conference, Washngon, USA, May 8 h May h, 0 pages. beween he opmal POUT safey soc, T POUT, and he opmal safey soc for he OUT polcy, T OUT, we have ploed he dfference, TPOUT TOUT, n Fgure 7. Here we can see ha, alhough he POUT polcy s economcally superor, mnmum safey soc do no always concde wh leas coss. Ths s an nsgh ha s conrary o he consan lead me case where he leas cos soluon always has he smalles safey socs. Fgure 8 hghlghs he bullwhp rao acheved when s used n he POUT polcy. We can see ha a 40% reducon n bullwhp s possble beween 8% o 9% avalably. Ths s neresng as he obecve funcon consss only of nvenory relaed coss. If coss are assocaed wh bullwhp are also presen, hese wll also be reduced. Ths s an mporan resul as bullwhp coss are somewha harder o quanfy, even hough bullwhp s wdely recognzed as havng a negave effec on supply chan performance (Lee e al. 000). Fgure 5. The nvenory cos opmal n he POUT polcy Fgure 6. Safey soc wh he nvenory cos opmal POUT polcy Fgure 7. Safey soc dfference beween he OUT polcy and he nvenory cos opmal POUT polcy T T POUT OUT Fgure 8. Bullwhp wh he nvenory cos opmal Conclusons We have suded he mpac of a sochasc lead me on a lnear perodc revew OUT polcy. Our novel conrbuon s a new mehod o oban he dsrbuon of he nvenory levels n he presence of correlaon beween he WIP and orders, va he so-called scaled shorfall dsrbuon. Ths bulds upon anoher unque conrbuon he M-marx and he assocaed mehod o deermne he probably of he ppelne beng n each of s possble saes. In he consan lead me case, wll mnmze he varance (or equvalenly he sandard devaon) of he nvenory levels and resul n he mnmum nvenory coss when he 9
10 Dsney, S.M., Malz, A., Wang, X. and Warburon, R., (05), The safey soc and nvenory cos paradox n a sochasc lead me seng, 6 h Producon and Operaons Managemen Socey Annual Conference, Washngon, USA, May 8 h May h, 0 pages. safey soc s se o he crcal fracle (Brown 963). However, n he sochasc lead me case, mnmzng he varance of he nvenory levels, by unng, wll no always resul n mnmal coss. Whle he opmal,, s never uny, may be near uny, and changes sgnfcanly wh he avalably arge, see Fgure 5. The sochasc lead me case wh order crossover resuls n a surprsng paradox. Mnmzng nvenory coss does no always lead o mnmum safey socs. However, he relaonshp beween holdng and baclog coss and he avalably acheved a he mos economcal soluon does sll hold. Ths leads o an mporan nsgh: Coss should be used o desgn he sysem because focusng on mnmzng nvenory varance, or safey socs, can lead o an ncorrecly specfed sysem. We have demonsraed ha he OUT polcy s no he opmal polcy when order crossover exss, as he lnear POUT economcally ouperforms. We have no proven he opmaly of he POUT polcy self because we do no now wheher here exss a beer performng polcy, lnear or non-lnear. Indeed, s nown ha he opmal polcy s non-lnear (Srnvasan e al., 0). However, he POUT polcy has a long hsory and has been successfully mplemened n pracce. See Poer and Dsney (00) for deals of an mplemenaon a he UK grocery realer, Tesco and Dsney e al., (03) for an mplemenaon n he global prner manufacurer, Lexmar. References Blacburn, J. 0. Valung me n supply chans: Esablshng lms of me-based compeon. Journal of Operaons Managemen 30(5): Brown, R. G Smoohng Forecasng and Predcon of Dscree Tme Seres. Prence-Hall, Mchgan. Dsney, S. M., M. R. Lambrech On replenshmen rules, forecasng and he Bullwhp Effec n supply chans. Foundaons and Trends n Technology, Informaon and Operaons Managemen (): 80. Dsney, S. M., D. R. Towll On he Bullwhp and Invenory varance produced by an orderng polcy. OMEGA: The Inernaonal Journal of Managemen Scence 3(3): Dsney, S. M., L. Hosho, L. Polley, C. Wegel. 03. Removng bullwhp from Lexmar s oner operaons. Producon and Operaons Managemen Socey Annual Conference, May 3 rd 6 h, Denver, USA. Feller, W An Inroducon o Probably Theory and s Applcaons, Vol. I. John Wley & Sons, Inc., New Yor. Kaplan, R. S A dynamc nvenory model wh sochasc lead mes. Managemen Scence 6(7): Lee, H. L., K. C. So, C. S. Tang The value of nformaon sharng n a wo-level supply chan. Managemen Scence 46(5): Poer, A. and S. M. Dsney. 00. Removng bullwhp from he Tesco supply chan. Producon and Operaons Managemen Socey Annual Conference, May 7 h 0 h, Vancouver, Canada. Robnson, L. W., J. R. Bradley, L. J. Thomas. 00. Consequences of order crossover under order-up-o nvenory polces. Manufacurng and Servce Operaons Managemen 3(3): Srnvasan, M., R. Novac, D. Thomas. 0. Opmal and approxmae polces for nvenory sysems wh order crossover. Journal of Busness Logscs 3(): Sal, G Survvng he Chna rpde. Supply Chan Managemen Revew 0(4): 8 6. Tsypn, Y. Z Samplng sysems heory and s applcaon. Pergamon Press, Oxford. Zalnd, D Order-level nvenory sysems wh ndependen sochasc lead mes. Managemen Scence 4(3):
Robustness Experiments with Two Variance Components
Naonal Insue of Sandards and Technology (NIST) Informaon Technology Laboraory (ITL) Sascal Engneerng Dvson (SED) Robusness Expermens wh Two Varance Componens by Ana Ivelsse Avlés avles@ns.gov Conference
More informationThe Dynamic Programming Models for Inventory Control System with Time-varying Demand
The Dynamc Programmng Models for Invenory Conrol Sysem wh Tme-varyng Demand Truong Hong Trnh (Correspondng auhor) The Unversy of Danang, Unversy of Economcs, Venam Tel: 84-236-352-5459 E-mal: rnh.h@due.edu.vn
More informationV.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS
R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon
More informationOnline Appendix for. Strategic safety stocks in supply chains with evolving forecasts
Onlne Appendx for Sraegc safey socs n supply chans wh evolvng forecass Tor Schoenmeyr Sephen C. Graves Opsolar, Inc. 332 Hunwood Avenue Hayward, CA 94544 A. P. Sloan School of Managemen Massachuses Insue
More informationSolution in semi infinite diffusion couples (error function analysis)
Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of
More informationOn One Analytic Method of. Constructing Program Controls
Appled Mahemacal Scences, Vol. 9, 05, no. 8, 409-407 HIKARI Ld, www.m-hkar.com hp://dx.do.org/0.988/ams.05.54349 On One Analyc Mehod of Consrucng Program Conrols A. N. Kvko, S. V. Chsyakov and Yu. E. Balyna
More informationDynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005
Dynamc Team Decson Theory EECS 558 Proec Shruvandana Sharma and Davd Shuman December 0, 005 Oulne Inroducon o Team Decson Theory Decomposon of he Dynamc Team Decson Problem Equvalence of Sac and Dynamc
More informationVariants of Pegasos. December 11, 2009
Inroducon Varans of Pegasos SooWoong Ryu bshboy@sanford.edu December, 009 Youngsoo Cho yc344@sanford.edu Developng a new SVM algorhm s ongong research opc. Among many exng SVM algorhms, we wll focus on
More informationAvailable online at ScienceDirect. Procedia Technology 25 (2016 )
Avalable onlne a www.scencedrec.com cencedrec Proceda Technology 5 (016 ) 1064 1071 Global Colloquum n Recen Advancemen and Effecual Researches n Engneerng, cence and Technology (RAERET 016) Transfer Funcon
More informationUNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION
INTERNATIONAL TRADE T. J. KEHOE UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 27 EXAMINATION Please answer wo of he hree quesons. You can consul class noes, workng papers, and arcles whle you are workng on he
More informationJohn Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany
Herarchcal Markov Normal Mxure models wh Applcaons o Fnancal Asse Reurns Appendx: Proofs of Theorems and Condonal Poseror Dsrbuons John Geweke a and Gann Amsano b a Deparmens of Economcs and Sascs, Unversy
More informationModeling and Solving of Multi-Product Inventory Lot-Sizing with Supplier Selection under Quantity Discounts
nernaonal ournal of Appled Engneerng Research SSN 0973-4562 Volume 13, Number 10 (2018) pp. 8708-8713 Modelng and Solvng of Mul-Produc nvenory Lo-Szng wh Suppler Selecon under Quany Dscouns Naapa anchanaruangrong
More informationCS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4
CS434a/54a: Paern Recognon Prof. Olga Veksler Lecure 4 Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped
More informationOnline Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading
Onlne Supplemen for Dynamc Mul-Technology Producon-Invenory Problem wh Emssons Tradng by We Zhang Zhongsheng Hua Yu Xa and Baofeng Huo Proof of Lemma For any ( qr ) Θ s easy o verfy ha he lnear programmng
More informationTSS = SST + SSE An orthogonal partition of the total SS
ANOVA: Topc 4. Orhogonal conrass [ST&D p. 183] H 0 : µ 1 = µ =... = µ H 1 : The mean of a leas one reamen group s dfferen To es hs hypohess, a basc ANOVA allocaes he varaon among reamen means (SST) equally
More informationPerformance Analysis for a Network having Standby Redundant Unit with Waiting in Repair
TECHNI Inernaonal Journal of Compung Scence Communcaon Technologes VOL.5 NO. July 22 (ISSN 974-3375 erformance nalyss for a Nework havng Sby edundan Un wh ang n epar Jendra Sngh 2 abns orwal 2 Deparmen
More informationEEL 6266 Power System Operation and Control. Chapter 5 Unit Commitment
EEL 6266 Power Sysem Operaon and Conrol Chaper 5 Un Commmen Dynamc programmng chef advanage over enumeraon schemes s he reducon n he dmensonaly of he problem n a src prory order scheme, here are only N
More information( ) () we define the interaction representation by the unitary transformation () = ()
Hgher Order Perurbaon Theory Mchael Fowler 3/7/6 The neracon Represenaon Recall ha n he frs par of hs course sequence, we dscussed he chrödnger and Hesenberg represenaons of quanum mechancs here n he chrödnger
More informationComb Filters. Comb Filters
The smple flers dscussed so far are characered eher by a sngle passband and/or a sngle sopband There are applcaons where flers wh mulple passbands and sopbands are requred Thecomb fler s an example of
More informationGraduate Macroeconomics 2 Problem set 5. - Solutions
Graduae Macroeconomcs 2 Problem se. - Soluons Queson 1 To answer hs queson we need he frms frs order condons and he equaon ha deermnes he number of frms n equlbrum. The frms frs order condons are: F K
More informationVolatility Interpolation
Volaly Inerpolaon Prelmnary Verson March 00 Jesper Andreasen and Bran Huge Danse Mares, Copenhagen wan.daddy@danseban.com brno@danseban.com Elecronc copy avalable a: hp://ssrn.com/absrac=69497 Inro Local
More informationOptimal Buyer-Seller Inventory Models in Supply Chain
Inernaonal Conference on Educaon echnology and Informaon Sysem (ICEIS 03 Opmal Buyer-Seller Invenory Models n Supply Chan Gaobo L Shandong Women s Unversy, Jnan, 50300,Chna emal: lgaobo_979@63.com Keywords:
More informationCS286.2 Lecture 14: Quantum de Finetti Theorems II
CS286.2 Lecure 14: Quanum de Fne Theorems II Scrbe: Mara Okounkova 1 Saemen of he heorem Recall he las saemen of he quanum de Fne heorem from he prevous lecure. Theorem 1 Quanum de Fne). Le ρ Dens C 2
More information( t) Outline of program: BGC1: Survival and event history analysis Oslo, March-May Recapitulation. The additive regression model
BGC1: Survval and even hsory analyss Oslo, March-May 212 Monday May 7h and Tuesday May 8h The addve regresson model Ørnulf Borgan Deparmen of Mahemacs Unversy of Oslo Oulne of program: Recapulaon Counng
More informationLecture 18: The Laplace Transform (See Sections and 14.7 in Boas)
Lecure 8: The Lalace Transform (See Secons 88- and 47 n Boas) Recall ha our bg-cure goal s he analyss of he dfferenal equaon, ax bx cx F, where we emloy varous exansons for he drvng funcon F deendng on
More informationKeywords: integration, innovative heuristic, interval order policy, inventory total cost 1. INTRODUCTION
eermnaon o Inerval Order Polcy a srbuor and ealers usng Innovave Heursc Mehod o Mnmze Invenory Toal Cos (Applcaon Case a srbuor X n Indonesa) ansa Man Heryano, Sanoso, and Elzabeh Ivana Krsano Bachelor
More informationGENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim
Korean J. Mah. 19 (2011), No. 3, pp. 263 272 GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS Youngwoo Ahn and Kae Km Absrac. In he paper [1], an explc correspondence beween ceran
More informationEcon107 Applied Econometrics Topic 5: Specification: Choosing Independent Variables (Studenmund, Chapter 6)
Econ7 Appled Economercs Topc 5: Specfcaon: Choosng Independen Varables (Sudenmund, Chaper 6 Specfcaon errors ha we wll deal wh: wrong ndependen varable; wrong funconal form. Ths lecure deals wh wrong ndependen
More informationLet s treat the problem of the response of a system to an applied external force. Again,
Page 33 QUANTUM LNEAR RESPONSE FUNCTON Le s rea he problem of he response of a sysem o an appled exernal force. Agan, H() H f () A H + V () Exernal agen acng on nernal varable Hamlonan for equlbrum sysem
More informationStochastic Repair and Replacement with a single repair channel
Sochasc Repar and Replacemen wh a sngle repar channel MOHAMMED A. HAJEEH Techno-Economcs Dvson Kuwa Insue for Scenfc Research P.O. Box 4885; Safa-309, KUWAIT mhajeeh@s.edu.w hp://www.sr.edu.w Absrac: Sysems
More informationNew M-Estimator Objective Function. in Simultaneous Equations Model. (A Comparative Study)
Inernaonal Mahemacal Forum, Vol. 8, 3, no., 7 - HIKARI Ld, www.m-hkar.com hp://dx.do.org/.988/mf.3.3488 New M-Esmaor Objecve Funcon n Smulaneous Equaons Model (A Comparave Sudy) Ahmed H. Youssef Professor
More informatione-journal Reliability: Theory& Applications No 2 (Vol.2) Vyacheslav Abramov
June 7 e-ournal Relably: Theory& Applcaons No (Vol. CONFIDENCE INTERVALS ASSOCIATED WITH PERFORMANCE ANALYSIS OF SYMMETRIC LARGE CLOSED CLIENT/SERVER COMPUTER NETWORKS Absrac Vyacheslav Abramov School
More informationThis document is downloaded from DR-NTU, Nanyang Technological University Library, Singapore.
Ths documen s downloaded from DR-NTU, Nanyang Technologcal Unversy Lbrary, Sngapore. Tle A smplfed verb machng algorhm for word paron n vsual speech processng( Acceped verson ) Auhor(s) Foo, Say We; Yong,
More informationDynamic Team Decision Theory
Dynamc Team Decson Theory EECS 558 Proec Repor Shruvandana Sharma and Davd Shuman December, 005 I. Inroducon Whle he sochasc conrol problem feaures one decson maker acng over me, many complex conrolled
More informationOptimal environmental charges under imperfect compliance
ISSN 1 746-7233, England, UK World Journal of Modellng and Smulaon Vol. 4 (28) No. 2, pp. 131-139 Opmal envronmenal charges under mperfec complance Dajn Lu 1, Ya Wang 2 Tazhou Insue of Scence and Technology,
More informationLinear Response Theory: The connection between QFT and experiments
Phys540.nb 39 3 Lnear Response Theory: The connecon beween QFT and expermens 3.1. Basc conceps and deas Q: ow do we measure he conducvy of a meal? A: we frs nroduce a weak elecrc feld E, and hen measure
More informationCS 268: Packet Scheduling
Pace Schedulng Decde when and wha pace o send on oupu ln - Usually mplemened a oupu nerface CS 68: Pace Schedulng flow Ion Soca March 9, 004 Classfer flow flow n Buffer managemen Scheduler soca@cs.bereley.edu
More informationLecture 2 M/G/1 queues. M/G/1-queue
Lecure M/G/ queues M/G/-queue Posson arrval process Arbrary servce me dsrbuon Sngle server To deermne he sae of he sysem a me, we mus now The number of cusomers n he sysems N() Tme ha he cusomer currenly
More informationDepartment of Economics University of Toronto
Deparmen of Economcs Unversy of Torono ECO408F M.A. Economercs Lecure Noes on Heeroskedascy Heeroskedascy o Ths lecure nvolves lookng a modfcaons we need o make o deal wh he regresson model when some of
More informationExistence and Uniqueness Results for Random Impulsive Integro-Differential Equation
Global Journal of Pure and Appled Mahemacs. ISSN 973-768 Volume 4, Number 6 (8), pp. 89-87 Research Inda Publcaons hp://www.rpublcaon.com Exsence and Unqueness Resuls for Random Impulsve Inegro-Dfferenal
More informationAnalysis And Evaluation of Econometric Time Series Models: Dynamic Transfer Function Approach
1 Appeared n Proceedng of he 62 h Annual Sesson of he SLAAS (2006) pp 96. Analyss And Evaluaon of Economerc Tme Seres Models: Dynamc Transfer Funcon Approach T.M.J.A.COORAY Deparmen of Mahemacs Unversy
More informationCubic Bezier Homotopy Function for Solving Exponential Equations
Penerb Journal of Advanced Research n Compung and Applcaons ISSN (onlne: 46-97 Vol. 4, No.. Pages -8, 6 omoopy Funcon for Solvng Eponenal Equaons S. S. Raml *,,. Mohamad Nor,a, N. S. Saharzan,b and M.
More informationM. Y. Adamu Mathematical Sciences Programme, AbubakarTafawaBalewa University, Bauchi, Nigeria
IOSR Journal of Mahemacs (IOSR-JM e-issn: 78-578, p-issn: 9-765X. Volume 0, Issue 4 Ver. IV (Jul-Aug. 04, PP 40-44 Mulple SolonSoluons for a (+-dmensonalhroa-sasuma shallow waer wave equaon UsngPanlevé-Bӓclund
More informationIn the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!
ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal
More informationNational Exams December 2015 NOTES: 04-BS-13, Biology. 3 hours duration
Naonal Exams December 205 04-BS-3 Bology 3 hours duraon NOTES: f doub exss as o he nerpreaon of any queson he canddae s urged o subm wh he answer paper a clear saemen of any assumpons made 2 Ths s a CLOSED
More informationHEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD
Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,
More information5th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2015)
5h Inernaonal onference on Advanced Desgn and Manufacurng Engneerng (IADME 5 The Falure Rae Expermenal Sudy of Specal N Machne Tool hunshan He, a, *, La Pan,b and Bng Hu 3,c,,3 ollege of Mechancal and
More informationNotes on the stability of dynamic systems and the use of Eigen Values.
Noes on he sabl of dnamc ssems and he use of Egen Values. Source: Macro II course noes, Dr. Davd Bessler s Tme Seres course noes, zarads (999) Ineremporal Macroeconomcs chaper 4 & Techncal ppend, and Hamlon
More informationComputing Relevance, Similarity: The Vector Space Model
Compung Relevance, Smlary: The Vecor Space Model Based on Larson and Hears s sldes a UC-Bereley hp://.sms.bereley.edu/courses/s0/f00/ aabase Managemen Sysems, R. Ramarshnan ocumen Vecors v ocumens are
More informationDisclosure Quality, Diversification and the Cost of Capital. Greg Clinch University of Melbourne June 2013
Dsclosure Qualy, Dversfcaon and he Cos of Capal Greg Clnch Unversy of Melbourne clnchg@unmelb.edu.au June 03 I hank Cynha Ca, Kevn L, and Sorabh Tomar for helpful commens and suggesons on an earler (ncomplee)
More informationAdvanced time-series analysis (University of Lund, Economic History Department)
Advanced me-seres analss (Unvers of Lund, Economc Hsor Dearmen) 3 Jan-3 Februar and 6-3 March Lecure 4 Economerc echnues for saonar seres : Unvarae sochasc models wh Box- Jenns mehodolog, smle forecasng
More informationAppendix H: Rarefaction and extrapolation of Hill numbers for incidence data
Anne Chao Ncholas J Goell C seh lzabeh L ander K Ma Rober K Colwell and Aaron M llson 03 Rarefacon and erapolaon wh ll numbers: a framewor for samplng and esmaon n speces dversy sudes cology Monographs
More informationTesting a new idea to solve the P = NP problem with mathematical induction
Tesng a new dea o solve he P = NP problem wh mahemacal nducon Bacground P and NP are wo classes (ses) of languages n Compuer Scence An open problem s wheher P = NP Ths paper ess a new dea o compare he
More informationRelative controllability of nonlinear systems with delays in control
Relave conrollably o nonlnear sysems wh delays n conrol Jerzy Klamka Insue o Conrol Engneerng, Slesan Techncal Unversy, 44- Glwce, Poland. phone/ax : 48 32 37227, {jklamka}@a.polsl.glwce.pl Keywor: Conrollably.
More informationChapter 6: AC Circuits
Chaper 6: AC Crcus Chaper 6: Oulne Phasors and he AC Seady Sae AC Crcus A sable, lnear crcu operang n he seady sae wh snusodal excaon (.e., snusodal seady sae. Complee response forced response naural response.
More informationII. Light is a Ray (Geometrical Optics)
II Lgh s a Ray (Geomercal Opcs) IIB Reflecon and Refracon Hero s Prncple of Leas Dsance Law of Reflecon Hero of Aleandra, who lved n he 2 nd cenury BC, posulaed he followng prncple: Prncple of Leas Dsance:
More informationComparison of Differences between Power Means 1
In. Journal of Mah. Analyss, Vol. 7, 203, no., 5-55 Comparson of Dfferences beween Power Means Chang-An Tan, Guanghua Sh and Fe Zuo College of Mahemacs and Informaon Scence Henan Normal Unversy, 453007,
More informationBernoulli process with 282 ky periodicity is detected in the R-N reversals of the earth s magnetic field
Submed o: Suden Essay Awards n Magnecs Bernoull process wh 8 ky perodcy s deeced n he R-N reversals of he earh s magnec feld Jozsef Gara Deparmen of Earh Scences Florda Inernaonal Unversy Unversy Park,
More informationLecture Notes 4. Univariate Forecasting and the Time Series Properties of Dynamic Economic Models
Tme Seres Seven N. Durlauf Unversy of Wsconsn Lecure Noes 4. Unvarae Forecasng and he Tme Seres Properes of Dynamc Economc Models Ths se of noes presens does hree hngs. Frs, formulas are developed o descrbe
More informationAn introduction to Support Vector Machine
An nroducon o Suppor Vecor Machne 報告者 : 黃立德 References: Smon Haykn, "Neural Neworks: a comprehensve foundaon, second edon, 999, Chaper 2,6 Nello Chrsann, John Shawe-Tayer, An Inroducon o Suppor Vecor Machnes,
More informationInventory Balancing in Disassembly Line: A Multiperiod Problem
Invenory Balancng n Dsassembly Lne: A Mulperod Problem [007-0662] Badr O. Johar and Surendra M. Gupa * Laboraory for Responsble Manufacurng 334 SN Deparmen of MIE Norheasern Unversy 360 Hunngon Avenue
More informationTHE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS
THE PREICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS INTROUCTION The wo dmensonal paral dfferenal equaons of second order can be used for he smulaon of compeve envronmen n busness The arcle presens he
More informationLecture 6: Learning for Control (Generalised Linear Regression)
Lecure 6: Learnng for Conrol (Generalsed Lnear Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure 6: RLSC - Prof. Sehu Vjayakumar Lnear Regresson
More informationRELATIONSHIP BETWEEN VOLATILITY AND TRADING VOLUME: THE CASE OF HSI STOCK RETURNS DATA
RELATIONSHIP BETWEEN VOLATILITY AND TRADING VOLUME: THE CASE OF HSI STOCK RETURNS DATA Mchaela Chocholaá Unversy of Economcs Braslava, Slovaka Inroducon (1) one of he characersc feaures of sock reurns
More informationCHAPTER 10: LINEAR DISCRIMINATION
CHAPER : LINEAR DISCRIMINAION Dscrmnan-based Classfcaon 3 In classfcaon h K classes (C,C,, C k ) We defned dscrmnan funcon g j (), j=,,,k hen gven an es eample, e chose (predced) s class label as C f g
More informationSolving the multi-period fixed cost transportation problem using LINGO solver
Inernaonal Journal of Pure and Appled Mahemacs Volume 119 No. 12 2018, 2151-2157 ISSN: 1314-3395 (on-lne verson) url: hp://www.pam.eu Specal Issue pam.eu Solvng he mul-perod fxed cos ransporaon problem
More information. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue.
Lnear Algebra Lecure # Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons
More informationOrdinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s
Ordnary Dfferenal Equaons n Neuroscence wh Malab eamples. Am - Gan undersandng of how o se up and solve ODE s Am Undersand how o se up an solve a smple eample of he Hebb rule n D Our goal a end of class
More information[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5
TPG460 Reservor Smulaon 08 page of 5 DISCRETIZATIO OF THE FOW EQUATIOS As we already have seen, fne dfference appromaons of he paral dervaves appearng n he flow equaons may be obaned from Taylor seres
More informationApproximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy
Arcle Inernaonal Journal of Modern Mahemacal Scences, 4, (): - Inernaonal Journal of Modern Mahemacal Scences Journal homepage: www.modernscenfcpress.com/journals/jmms.aspx ISSN: 66-86X Florda, USA Approxmae
More informationA NEW TECHNIQUE FOR SOLVING THE 1-D BURGERS EQUATION
S19 A NEW TECHNIQUE FOR SOLVING THE 1-D BURGERS EQUATION by Xaojun YANG a,b, Yugu YANG a*, Carlo CATTANI c, and Mngzheng ZHU b a Sae Key Laboraory for Geomechancs and Deep Underground Engneerng, Chna Unversy
More informationA Novel Iron Loss Reduction Technique for Distribution Transformers. Based on a Combined Genetic Algorithm - Neural Network Approach
A Novel Iron Loss Reducon Technque for Dsrbuon Transformers Based on a Combned Genec Algorhm - Neural Nework Approach Palvos S. Georglaks Nkolaos D. Doulams Anasasos D. Doulams Nkos D. Hazargyrou and Sefanos
More informationRobust and Accurate Cancer Classification with Gene Expression Profiling
Robus and Accurae Cancer Classfcaon wh Gene Expresson Proflng (Compuaonal ysems Bology, 2005) Auhor: Hafeng L, Keshu Zhang, ao Jang Oulne Background LDA (lnear dscrmnan analyss) and small sample sze problem
More informationShould Exact Index Numbers have Standard Errors? Theory and Application to Asian Growth
Should Exac Index umbers have Sandard Errors? Theory and Applcaon o Asan Growh Rober C. Feensra Marshall B. Rensdorf ovember 003 Proof of Proposon APPEDIX () Frs, we wll derve he convenonal Sao-Vara prce
More informationEpistemic Game Theory: Online Appendix
Epsemc Game Theory: Onlne Appendx Edde Dekel Lucano Pomao Marcano Snscalch July 18, 2014 Prelmnares Fx a fne ype srucure T I, S, T, β I and a probably µ S T. Le T µ I, S, T µ, βµ I be a ype srucure ha
More information. The geometric multiplicity is dim[ker( λi. A )], i.e. the number of linearly independent eigenvectors associated with this eigenvalue.
Mah E-b Lecure #0 Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons are
More informationPERISHABLES INVENTORY CONTROL MODEL UNDER TIME- VARYING AND CONTINUOUS DEMAND
PERISHABLES INVENTORY CONTROL MODEL UNDER TIME- VARYING AND CONTINUOUS DEMAND Xangyang Ren 1, Hucong L, Meln Ce ABSTRACT: Ts paper consders e yseress persable caracerscs and sorage amoun of delayed rae
More informationPolitical Economy of Institutions and Development: Problem Set 2 Due Date: Thursday, March 15, 2019.
Polcal Economy of Insuons and Developmen: 14.773 Problem Se 2 Due Dae: Thursday, March 15, 2019. Please answer Quesons 1, 2 and 3. Queson 1 Consder an nfne-horzon dynamc game beween wo groups, an ele and
More informationPart II CONTINUOUS TIME STOCHASTIC PROCESSES
Par II CONTINUOUS TIME STOCHASTIC PROCESSES 4 Chaper 4 For an advanced analyss of he properes of he Wener process, see: Revus D and Yor M: Connuous marngales and Brownan Moon Karazas I and Shreve S E:
More informationTime-interval analysis of β decay. V. Horvat and J. C. Hardy
Tme-nerval analyss of β decay V. Horva and J. C. Hardy Work on he even analyss of β decay [1] connued and resuled n he developmen of a novel mehod of bea-decay me-nerval analyss ha produces hghly accurae
More informationReactive Methods to Solve the Berth AllocationProblem with Stochastic Arrival and Handling Times
Reacve Mehods o Solve he Berh AllocaonProblem wh Sochasc Arrval and Handlng Tmes Nsh Umang* Mchel Berlare* * TRANSP-OR, Ecole Polyechnque Fédérale de Lausanne Frs Workshop on Large Scale Opmzaon November
More informationForecasting customer behaviour in a multi-service financial organisation: a profitability perspective
Forecasng cusomer behavour n a mul-servce fnancal organsaon: a profably perspecve A. Audzeyeva, Unversy of Leeds & Naonal Ausrala Group Europe, UK B. Summers, Unversy of Leeds, UK K.R. Schenk-Hoppé, Unversy
More informationarxiv: v1 [cs.sy] 2 Sep 2014
Noname manuscrp No. wll be nsered by he edor Sgnalng for Decenralzed Roung n a Queueng Nework Y Ouyang Demoshens Tenekezs Receved: dae / Acceped: dae arxv:409.0887v [cs.sy] Sep 04 Absrac A dscree-me decenralzed
More informationEP2200 Queuing theory and teletraffic systems. 3rd lecture Markov chains Birth-death process - Poisson process. Viktoria Fodor KTH EES
EP Queung heory and eleraffc sysems 3rd lecure Marov chans Brh-deah rocess - Posson rocess Vora Fodor KTH EES Oulne for oday Marov rocesses Connuous-me Marov-chans Grah and marx reresenaon Transen and
More informationMath 128b Project. Jude Yuen
Mah 8b Proec Jude Yuen . Inroducon Le { Z } be a sequence of observed ndependen vecor varables. If he elemens of Z have a on normal dsrbuon hen { Z } has a mean vecor Z and a varancecovarance marx z. Geomercally
More informationAdvanced Macroeconomics II: Exchange economy
Advanced Macroeconomcs II: Exchange economy Krzyszof Makarsk 1 Smple deermnsc dynamc model. 1.1 Inroducon Inroducon Smple deermnsc dynamc model. Defnons of equlbrum: Arrow-Debreu Sequenal Recursve Equvalence
More informationPanel Data Regression Models
Panel Daa Regresson Models Wha s Panel Daa? () Mulple dmensoned Dmensons, e.g., cross-secon and me node-o-node (c) Pongsa Pornchawseskul, Faculy of Economcs, Chulalongkorn Unversy (c) Pongsa Pornchawseskul,
More informationNPTEL Project. Econometric Modelling. Module23: Granger Causality Test. Lecture35: Granger Causality Test. Vinod Gupta School of Management
P age NPTEL Proec Economerc Modellng Vnod Gua School of Managemen Module23: Granger Causaly Tes Lecure35: Granger Causaly Tes Rudra P. Pradhan Vnod Gua School of Managemen Indan Insue of Technology Kharagur,
More informationDual Approximate Dynamic Programming for Large Scale Hydro Valleys
Dual Approxmae Dynamc Programmng for Large Scale Hydro Valleys Perre Carpener and Jean-Phlppe Chanceler 1 ENSTA ParsTech and ENPC ParsTech CMM Workshop, January 2016 1 Jon work wh J.-C. Alas, suppored
More informationOn computing differential transform of nonlinear non-autonomous functions and its applications
On compung dfferenal ransform of nonlnear non-auonomous funcons and s applcaons Essam. R. El-Zahar, and Abdelhalm Ebad Deparmen of Mahemacs, Faculy of Scences and Humanes, Prnce Saam Bn Abdulazz Unversy,
More informationABSTRACT KEYWORDS. Bonus-malus systems, frequency component, severity component. 1. INTRODUCTION
EERAIED BU-MAU YTEM ITH A FREQUECY AD A EVERITY CMET A IDIVIDUA BAI I AUTMBIE IURACE* BY RAHIM MAHMUDVAD AD HEI HAAI ABTRACT Frangos and Vronos (2001) proposed an opmal bonus-malus sysems wh a frequency
More informationMANY real-world applications (e.g. production
Barebones Parcle Swarm for Ineger Programmng Problems Mahamed G. H. Omran, Andres Engelbrech and Ayed Salman Absrac The performance of wo recen varans of Parcle Swarm Opmzaon (PSO) when appled o Ineger
More informationThe topology and signature of the regulatory interactions predict the expression pattern of the segment polarity genes in Drosophila m elanogaster
The opology and sgnaure of he regulaory neracons predc he expresson paern of he segmen polary genes n Drosophla m elanogaser Hans Ohmer and Réka Alber Deparmen of Mahemacs Unversy of Mnnesoa Complex bologcal
More informationDensity Matrix Description of NMR BCMB/CHEM 8190
Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon If we say wh one bass se, properes vary only because of changes n he coeffcens weghng each bass se funcon x = h< Ix > - hs s how we calculae
More informationThe preemptive resource-constrained project scheduling problem subject to due dates and preemption penalties: An integer programming approach
Journal of Indusral Engneerng 1 (008) 35-39 The preempve resource-consraned projec schedulng problem subjec o due daes and preempon penales An neger programmng approach B. Afshar Nadjaf Deparmen of Indusral
More informationMechanics Physics 151
Mechancs Physcs 5 Lecure 0 Canoncal Transformaons (Chaper 9) Wha We Dd Las Tme Hamlon s Prncple n he Hamlonan formalsm Dervaon was smple δi δ Addonal end-pon consrans pq H( q, p, ) d 0 δ q ( ) δq ( ) δ
More informationA Globally Optimal Local Inventory Control Policy for Multistage Supply Chains
Inernaonal Journal of Producon Research, Vol. X, o. X, Monh 2X, xxx xxx A Globally Opmal Local Invenory Conrol Polcy for Mulsage Supply Chans J.-C. HEE CRS-LSIS, Unversé Paul Cézanne, Faculé de San Jérôme
More informationJanuary Examinations 2012
Page of 5 EC79 January Examnaons No. of Pages: 5 No. of Quesons: 8 Subjec ECONOMICS (POSTGRADUATE) Tle of Paper EC79 QUANTITATIVE METHODS FOR BUSINESS AND FINANCE Tme Allowed Two Hours ( hours) Insrucons
More informationSingle-loop System Reliability-Based Design & Topology Optimization (SRBDO/SRBTO): A Matrix-based System Reliability (MSR) Method
10 h US Naonal Congress on Compuaonal Mechancs Columbus, Oho 16-19, 2009 Sngle-loop Sysem Relably-Based Desgn & Topology Opmzaon (SRBDO/SRBTO): A Marx-based Sysem Relably (MSR) Mehod Tam Nguyen, Junho
More informationEfficient Asynchronous Channel Hopping Design for Cognitive Radio Networks
Effcen Asynchronous Channel Hoppng Desgn for Cognve Rado Neworks Chh-Mn Chao, Chen-Yu Hsu, and Yun-ng Lng Absrac In a cognve rado nework (CRN), a necessary condon for nodes o communcae wh each oher s ha
More information