An operational policy for a single vendor multi buyer integrated inventory supply chain system considering shipping time

Size: px
Start display at page:

Download "An operational policy for a single vendor multi buyer integrated inventory supply chain system considering shipping time"

Transcription

1 Lousaa Sae Uversy LSU al Coos LSU Maser's Theses Grauae School 00 A operaoal polcy for a sle veor ul buyer erae veory supply cha syse coser shpp e Chraj Saha Lousaa Sae Uversy a Arculural a Mechacal Collee, chrajsaha@al.co Follow hs a aoal wors a: hps://alcoos.lsu.eu/raschool_heses Par of he Cosruco Eeer a Maaee Coos Recoee Cao Saha, Chraj, "A operaoal polcy for a sle veor ul buyer erae veory supply cha syse coser shpp e" (00). LSU Maser's Theses hps://alcoos.lsu.eu/raschool_heses/348 Ths Thess s brouh o you for free a ope access by he Grauae School a LSU al Coos. I has bee accepe for cluso LSU Maser's Theses by a auhorze rauae school eor of LSU al Coos. For ore forao, please coac rae@lsu.eu.

2 AN OPERATIONAL POLICY FOR A SINGLE VENOR MULTI BUYER INTEGRATE INVENTORY SUPPLY CHAIN SYSTEM CONSIERING SHIPPING TIME A Thess Sube o he Grauae Faculy of he Lousaa Sae Uversy a Arculure a Mechacal Collee paral fulflle of he requree for he eree of Maser of Scece Iusral Eeer The epare of Cosruco Maaee a Iusral Eeer By Chraj Saha B.S., Wes Beal Uversy of Techoloy, Slur, Ia, 006 Auus 00

3 ACKNOWLEGEMENT Frs of all, I wsh o ha he Alhy for v e he eurace o coplee y ere course. I wa o express y eepes sese of raue o r. Bhaba R. Sarer, Elo G. Yaes sushe Professor of Iusral Eeer a Charperso of y Coee, for hs valuable uace, cosa ecouraee, cooperave aue, ese paece, useful scussos ur he course of research a preparao of he auscrp. He has always bee a foua of sprao o e. Also, I a hoore o express y esee a profou sese of raue o r. Pus J. Ebelu, Coee eber a Professor of Iusral Eeer, for hs cosrucve a valuable suesos. He was always quc o exe hs help wheever I ase for, a he ever fale o offer cooperave a accooa servces. I a horouhly elhe o recoze r. Lawrece Ma Jr., Professor Eerus of Iusral Eeer epare a Avsory Coee eber, for hs valuable suesos eowe ur he course of y research wor. I a also very prou o acowlee Suaa, Raesh, Shalal a Soesh, whose cosa help a collecve effors are uoubely reflece he copleo of hs veure. Also, oh coul have bee possble whou he cessa love, affeco, sacrfce a sprao fro y pares. I feel blesse o have such supporve pares, who were a ceral force u e o hs ay. Fally, I woul also le o evoe y success o he facal asssace prove by he CMIE epare he for of asssashp ur he eure.

4 TABLE OF CONTENTS ACKNOWLEGEMENT... LIST OF TABLES... v LIST OF FIGURES... v ABSTRACT... v CHAPTER : INTROUCTION.... The Proble.... Applcaos Research Goals Research Objecves... 5 CHAPTER : LITERATURE REVIEW Sle Veor Sle Buyer Supply Cha Syse Sle Veor Mul Buyer Supply Cha Syse Shorcos of Prevous Leraure... 0 CHAPTER 3: MOEL EVELOPMENT Assupos Noaos Moel Forulao Moel I: Ie Shppe Rh Afer Prouco Averae Iveory Calculao Toal Averae Iveory Calculao Toal Cos For Moel I Orer Cos Seup Cos Iveory Carry Cos Trasporao Cos Toal Syse Cos Soluo Mehooloy Sesvy Aalyss for Moel I Effec of S o... 4 TC I 3.7. Effec of h o... 4 TC I Effec of P/ o... 4 TC I 3.8 Moel II: Ies Are Shppe I Every Pero Averae Iveory Calculao Toal Averae Iveory Calculao Toal Cos for Moel II Orer Cos Seup Cos Iveory Carry Cos Trasporao Cos... 33

5 3.9.5 Toal Syse Cos Soluo Mehooloy Sesvy Aalyss for Moel II Effec of S o TC II Effec of h o TC II Effec of P/ o TC II Moel III: Ies Shppe Uequal Baches Averae Iveory Calculao Toal Averae Iveory Calculao Toal Cos for Moel III Orer Cos Seup Cos Iveory Carry Cos Trasporao Cos Toal Syse Cos Soluo Mehooloy Sesvy Aalyss for Moel III Effec of S o TC III Effec of h o TC III Effec of P/ o TC III CHAPTER 4: RESULTS Perforace Coparso Coparso wh Alerave Approach CHAPTER 5: RESEARCH SUMMARY AN CONCLUSIONS Coclusos Research Sfcace Possble Fuure Exesos BIBLIOGRAPHY APPENIX VITA v

6 LIST OF TABLES Table : aa for sle veor 9 buyers proble... Table : Suary of resuls... Table 3: Effec of P/ ( A) rao o TC I... 6 Table 4: aa for sle veor 9 buyers proble Table 5: Suary of resuls Table 6: Effec of P/ ( A) rao o TC II Table 7: aa for sle veor 9 buyers proble Table 8: Suary of resuls Table 9: Effec of P/ ( A) rao o TC III... 5 Table 0: aa for sle veor 9 buyers proble... 5 Table : Suary of resuls... 5 Table : Perforace coparso wh alerave oels v

7 LIST OF FIGURES Fure.: Flow ara of a sle veor ul buyer erae supply cha... Fure 3. Iveory flow Moel I... 5 Fure 3.: Graphcal represeao of TC I... 3 Fure 3.3: Effec of P/ ( A) rao o TC I... 6 Fure 3.4 Iveory flow Moel II... 7 Fure 3.5: Graphcal represeao of TC II Fure 3. 6: Effec of P/ ( A) rao o TC II Fure 3.7 Iveory flow Moel III... 4 Fure 3.8: Graphcal represeao of TC III Fure 3.9: Effec of P/ ( A) rao o TC III... 5 v

8 ABSTRACT Sce s rouco, he cocep of erae veory supply cha has receve a coserable aou of aeo. The ajory of sues he las hree ecaes reveale a crease hol cos as prouc oves furher ow he cha or up he cha. A rece suy Hoque (008) cosere veor s seup cos a veory hol cos. Soe research also cosere fxe rasporao cos, whch s urealsc. Ths suy focuses o a sle-veor, ul-buyer scearo a preses hree oels. Frs, wo oels llusrae he rasferr of equally-sze baches. The, a hr oel cosers he rasferr of uequally-sze baches a lo. Ths suy relaxes he assupo ha veor s hol cos us be reaer ha or less ha all buyer s hol coss he syse. Also, hs research faclaes uequal rasporao e a cos for ffere buyers for reaer flexbly. The oal syse cos s calculae by su he aual operaoal cos for all he pares he syse. Opu values of he ecso varables are eere us a rec search eho. As presee by he hr oel, a uercal exaple eosraes ha he oal syse cos s less whe copare wh oher wo oels presee. Ths suy also preses he follow: soluo proceures o solve each oel, ay uercal exaples o suppor aheacal fs, a perforace coparsos ao hree fs. I orer o jusfy he lo-spl approach for solv he erae veory proble, alerave oels wh o lo spl are evse a ese uer he sae crcusaces. Alerave oels wh o lo spl prouce slar or beer resuls. Uer he sae crcusaces, he alerae hr oel s observe o be offer he leas oal cos for he syse. Ths suy also preses a sesvy aalyss o chec he robusess of he hree oels. The fuure exeso of hs research ay volve coser sorae capacy cosra a rao ea. v

9 CHAPTER INTROUCTION Coser a sle veor supply a e o ulple buyers. The veor prouces he e baches a a a fe rae. The veor he ses he fshe es o ulple buyers. I hs process, he veor curs bach se-up a rasporao cos, a he veor a buyer boh carry he e hol cos proporoal o e. Meawhle, each buyer has hs ow eersc yearly ea. I hs scearo, he buyer has a proble wh eer he orer quay, a he veor has a proble wh eer opu prouco quay a shpp scheule, whch zes he opera cos. ur he las hree ecaes, researchers have bee search for he soluo o hese probles. Researchers have show ha by vew he veor a buyers as a syse (also ow as erae supply cha) raher ha as separae vuals, oal syse cos ca be reuce sfcaly. The bass of he erae supply cha cocep s ha each buyer has clear owlee abou her yearly ea, a buyers are reay o share hs forao wh he suppler o ejoy he beefs of coorao. Toay, rea provees elecroc forao exchae have ae hs cocep feasble. I supply cha, rasporao cos s a ajor par of operaoal cos. Trasporao e, cos, a capacy cosra play a role a ecsos. I oay s worl, shor lfe cycle a couless specales of slar proucs have ae he lobal are hhly copeve. I orer o survve are pressure, every copay has o be hhly copeve ers of prouc qualy, prce a prouc supply. The flow ara of a sle veor ul buyer supply cha syse s show Fure..

10 Buyer 3 Buyer Buyer Buyer Veor Traspor Vehcle Fure.: Flow ara of a sle veor ul buyer erae supply cha. The Proble For ecaes, he prary objecves ao research have bee eer he opal bach sze, ubers of bach szes a lo, uber of los per year, a shpp scheule of erae veory oels. The ajory of hs research s oe wh eal assupos. I boh sle a ulsae supply chas, ecso paraeers, rasporao e, cos a yearly ea vary wh e. ea of a vual buyer ca be ffere, a hol cos a shpp cos ay also vary. A vual buyer s ecooc orer quay a shpp scheule are also lely o ffer. Uwse choces of hese varables ca lea o excessve prouc coss, whch, ur, ca lea o cusoer ssasfaco a los sales. The prese research focuses o eer ecooc orer quay (EOQ) a shpp sraey of a veory syse erae wh a sle-prouc, sle-veor,

11 a ul-buyer. I a sle-veor-ul-buyer syse, a veor prouces a elvers a e o ulple buyers. epe o he ea of all buyers, prouco a shpp polces are eere. If, for he sae of splcy, we se a uversal orer quay for all buyers, he we ay fal o opze everyoe s cos. Soe buyers ay receve ore ha hey ee a parcular e spa whle ohers ay receve less ha eee. Aa, ae buyers are sprea across he coury where shpp cos a e vares sfcaly ao buyers. For exaple, buyers who are far fro he veor ee o shp earler ha hose buyers who are relavely close o veors. Bu aa ffere scheules for all buyers s ffcul o accooae, so we us f a cos-effecve polcy ha beer corols he coplexy of operao. Also, we us eere how o reulae bach sze such a way ha o oly reuces orer cos, seup cos a hol cos, bu also avos shorae.. Applcaos Auoobles are a exaple of such a e. All he showroos arou he coury receve shpe of cars fro he veor. Suppose a aufacur facly s Mcha, a soe of he showroos are Iaa, Lousaa or Alasa. Shpp cos a e s ffere for each case, a each showroo requres space for splay, whch requres hh aeace a survellace cos. I s obvous ha cos Calfora s ore ha ha Olahoa or Mssour. No ealer les o eep a excess of veory because such veory creases he hol cos. Aa, f we loo arou we oe, ulple showroos wh sae auoobles whch resuls copeo. No ealer ca affor o o hav cars ea because he cusoer has a choce o e aoher ex oor. I s pora ha he ealer es he shpe o e. 3

12 Aoher proble s every showroo has s ow ea; base o orer cos a hol cos, he showroos ecooc orer quaes ffer fro oe aoher. Aa, coser seup cos a hol cos, veor s ecooc aufacur quay a bach ca be ffere fro he EOQ of all he showroos. Now, f we arbrarly ass each showroo he sae shpp quay a scheule, he syse s lely o fal. The veor eres s o prouce as ay es a shp he o he realer as soo as possble o avo hol cos. The eres of he realer s o e he rh aou of prouc wh he rh efrae o avo hol cos a oher scellaeous expeses. Aa, rasferr es saller los resuls lower veory cos bu hher orer, seup, a rasporao cos. O he oher ha, rasferr es larer los leas o hher veory cos bu lower orer, seup, rasporao cos, a scheul erfereces ue o scarce sorae capacy for boh he veor a he buyer. Soe exaples of such usres are BMW, For, GMC, Mercees, a Toyoa, whch prouce cars, rucs, a oher oorze vehcles. The propose research wll prove he veory aaee of he supply cha syse, whch wll also sfcaly reuce he syse s opera cos a crease s profably..3 Research Goals The objecve of hs research s o suy a oel a sle-veor-ul-buyer veory syse, whch cosras he raspor capacy a aas varous rasporao es bewee ffere buyers. I realsc suaos, veory hol coss are ffere for veors a buyers; hese rasporao coss affec orer polcy, prouco polcy, a oal syse cos, a rasporao coss vary ao all buyers. Ths research preses a operaoal polcy o prouce a elver es he rh quay a he rh e whle reuc he oal syse cos. 4

13 .4 Research Objecves Ths proble aresses prouco a shpp polces of a prouc ha flows fro a veor o ulple buyers. Yearly eas for all he buyers are recore a are eersc. Veor a buyers aa a close relaoshp o reuce overall syse cos. I hs research, hree oels are presee o aress he proble uer ffere operaoal polces. The veor ses he prouc o buyers ulple baches. Baches coul be equally or uequally sze. Carry coss for he veor a each buyer ay vary epe o her eoraphc locao. Trasporao e a cos for each buyer ffers. ue o he above reasos, he aure of veory of hs supply cha syse ffers fro raoal syses. Hece, he prary objecves of hs research are: () () () (v) To suy he behavor of he veores uer hree operaoal polces. To f opal bach sze. To f opal bach uber a lo. To f bes polcy o reuce oal syse cos ao hree operaoal polces. 5

14 CHAPTER LITERATURE REVIEW Ths suy has oly cosere he eersc ea. The leraure revew orazes prevous research chroolocal orer sar wh he sples, sle-prouc, sle-veor, sle-buyer scearo, uresrce prouco rae a lo-for-lo polcy (Goyal 976). The revew he shfs o he os rece sle-veor, ul-buyers scearo, whch also volves seup a veory cos for he veor wh fe prouco rae (Hoque 008). Ao he prevous wors, ffereces are aly assupos of prouco rae a repleshe pero. Recely, oher aspecs of hs proble have also bee scusse he leraure. Oher aspecs refer o ulple buyers, rasporao capacy a cos, lea e, varable prouco cos, qualy a process falure, se up cos reuco a ore realsc ea raes.. Sle Veor Sle Buyer Supply Cha Syse Goyal (976) propose hs frs oel, aress a erae supply cha, assu a fe prouco rae, a ufor eersc ea over e. He ore lea e a resrce soc ous. I hs research, he veor prouces los a ses he ere lo o he realer. Ths process ples ha he ere lo us be prouce before shpe. Baerjee (986) ep ha lo-for-lo polcy, bu relaxe he assupo of fe prouco rae. Baerjee (986) also coe he er, JELS (jo Ecooc Lo Sz) a arue abou ecooc beefs of boh veor a realer hrouh JELS. Baerjee (986) cosere purchase ras e, seup e a elvery e accorace wh acual prouco e. Goyal (988) rouce a ore eeralze JELS oel, whch relaxe he assupo of lo-for-lo oel a propose o prouce a lo whch ca be supple eer uber of orers afer he ere lo s prouce. Goyal 6

15 (988) showe ha hs jo oal releva cos s less ha or equal o ha of he JELS oel. Goyal (995) use a ffere approach ha equally-sze shpe o coe up wh a ea of eoerc shpe sze. Ths eas ha he successve shpe sze s he prouc of he pror shpe sze relao o he rao of prouco a ea rae. Vswaaha (998) presee wo oels: oe whch he shpe szes are slar a aoher whch he shpe sze s equal o whaever veory s avalable a ha po. I hs suy Vswaaha ae Lu s (995) equal shpe sze polcy as Iecal elvery quay (IE) a Goyal s (995) a uequal shpe sze oel as elver wha s prouce (WP). Vswaaha showe ha eher polcy s beer ha he oher for all ype of probles, a he coclue ha he bes polcy epes o he proble s paraeers. Goyal a Nebebe (000) poe ou he ffcules face by he veor a realer whle apply he polcy propose by Goyal (995) a Hll (997), who poe ou he ffculy of eer bach sze for he veor, opal uber of shpes, a each shpe sze for he buyer. Goyal a Nebebe (000) propose a alerae soluo whch suess ha ao shpes, he frs shpe s saller a followe by (-) equal- sze, whch s equal o he prouc of he frs shpe sze as well as s rae of prouco over rae of ea. Goyal (000) exee he polcy propose by Hll (997). He propose ha he follow shpe szes wll be eere by frs shpe sze. The follow shpe szes ay be crease by a facor of prouco rae over ea rae ul s possble o o so. A lely rawbac of hs suy s ha he ee applcao of he oel s uclear. Hll a Oar (006) presee ervao of opal aufacur bach sze a shpe polcy whe prouc-hol cos creases as he prouc oves ow o he buyer uer o-requre 7

16 equal shpp sze. Zhou a You (007) relaxe he assupos ha veor s soc hol s always reaer ha he realer s. They showe ha her oel perfors equally well reuc averae oal cos rearless of he veor s or realer s sochol coss, whch are ever equal o each oher. I hs suy hey also allowe shoraes bu oly for buyers. They also presee a prouco veory polcy for eerora es. I hs suy hey prove ha he opal polcy for veors whose hol coss are reaer ha he realers hol cos s ha all uequally-sze shpes crease he follow shpes by rao of prouco rae o he ea rae. Pa a You (00) explore how o reuce lea-e a erae syse volv cos. They arue ha beer cusoer sasfaco levels a reuce safey soc levels ca be acheve hrouh prov lea-e; however, hese chaes occur a he expese of lea-e crash cos. Ths suy evelope a oel, whch yels lower oal cos a reuce lea e ha ha presee by Baerjee (986) a Goyal (988). Ulaely, hs research s a exeso of Goyal (988), a relaxe he fe prouco capacy assupo. Hoque a Goyal (000) cosere raspor capacy lao by prese a opal polcy for he sle-veor a sle-buyer erae supply cha, whch cosers equal a uequal shpe sze a rasporao capacy.. Sle Veor Mul Buyer Supply Cha Syse Jolear a Tharhare (990) cosere aoher area of erae supply cha,.e., sle veor a ul buyer. I ha suy hey presee a alerae soluo of he sae proble cosere by Baerjee (986) a ae as he Ivual Resposble a Raoal ecso (IRR). I IRR, hey refe JELS by brea se-up cos o veors orer process a hal cos per prouco ru seup cos. Base o he chaes, he auhors clae IRR s cossecy a free eerprse scearo a 8

17 superory over Baerjee s (986) JELS oel eal wh probles le sle-veor a o-ecal buyers. A sle-veor, ul-realer proble s also aresse by Affsco e al. (988, 99, a 993). I hese sues, researchers aresse he sle veor a ay ecal realers wh a objecve of reuc prouco seup cos a realer s orer cos. They showe ha subsaal provee ca be acheve, uer hs oel, hrouh he epee cos opzao echque; hus, a cooperave evroe, a erae veory approach s suese over epee cos opzao. Lu (995) propose hs oel coex of a sle-veor or ul-buyers scearo. Lu allowe shpes ur prouco a ore Goyal s assupo of prouc a ere lo before shpe. May oher JELS-base oels, e.. Baeerjee a K (995) a K a Ha (003), cosere equally-sze shpe polcy. Yau a Chao (004) presee a oel whch he veor prouces a supples o all he buyers; hs zes he veor s oal aual cos base o he axu cos buyers are wll o cur. They cae up wh a effce alorh o search a opal cos curve. Saja e al. (005) presee a sle-veor-ul-buyer scearo whch he veor s he sole suppler for a specfc e o all buyers. Supples are elvere equal szes, bu he shpe sze ay ffer fro oe buyer o aoher base o her ea. Supples are elvere sequece; e.., he frs buyer wll e frs supply followe by seco a hr a so o, assu prouco-cycle e a buyer s-orer cycle e are he sae. Also, he e bewee oe elvery a he ex s fxe for each buyer. Hoque (008) presee sle-veor ul-buyer syse coser a cosere veor s seup a veory hol cos. I hs research, he arue favor of rasferr of saller los over larer los whe sorae capacy s scarce for boh he veor a he buyer. Vswaaha a Ppla (00) aresse he sae proble a re o solve by us a 9

18 ae heorec approach. They propose ha veors wll specfy he coo repleshe pero for accep he proposal, a ur, buyers wll receve a prce scou fro he veor. The prce scou wll be suffce o copesae he allevae prouc carry cos, f ay. Vswaaha a Ppla (00) erve opal repleshe pero a prce scou quay by solv Sacelber ae. Che e al. (009) scusse elvery a share rasporao cos a ulpleveors erae veory syse, a Coeaux e al. (005) scusse he prouc qualy speco polcy a erae veory syse..3 Shorcos of Prevous Leraure Ths leraure revew brefly recalls he evelope of he erae supply cha aaee syses sar fro he splsc sle-veor, sle-buyer syse a ov owar ore avace sle-veor, ulple-buyers syses. The revew pos ou ha each suy has s ow shorcos. Realscally, os of he probles are cosrae. A veor s hol cos coul be hher or lower ha oher buyers he sae syse; rasporao e o oe buyer coul be ffere fro aoher buyer, a eve rasporao cos ay ffer ao buyers. Alhouh ay researchers cosere cosras eoe above her oels oe a a e, so far, hey have ve lle aeo o bul a sle-veor-ulple-buyer erae oel, whch cosers all he above eoe cosras. Here, we prese a sle-veor-ul-buyer erae supply cha oel wh equal a uequal bach szes; hs oel cosers veor s seup cos, rasporao e, cos, capacy cosrae a uequal hol cos for buyers. Three oels are presee: he frs wo, whch coser equal bach sze a he hr oel, whch cosers uequal bach sze. 0

19 CHAPTER 3 MOEL EVELOPMENT I hs seco he forulao of he hree operaoal oels are presee o llusrae ffere prouco a shpp polces. These oels are base o soe prevously escrbe assupos a oaos a are followe by averae veory a oal syse cos ervaos. 3. Assupos The follow assupos are ae o cosruc oels: (a) ea a prouco rae are fxe a eersc. (b) Every buyer esaes her ow orer a hol cos uer varous cos facor a les be ow o he veor. (c) The cocere pares share he beefs of coorao base o a cosless way of shar. () No baclo or shoraes are allowe,.e. P. (e) Lo a bach szes are eers. (f) For boh veor a he buyer, sorae capacy s ucosrae. () All shpp vehcles are ecal, a avalably of ay uber of shpp ea s ucosrae. (h) Trasporao es are sfca a ca vary fro buyer o buyer epe o buyer s sace fro he veor. () Se-up e a cos are sfca. (j) Mu bach sze has o be reaer ha or equal o uber of buyer he syse. () For he purpose of splcy, veory carry cos ur rasporao s elece.

20 3. Noaos The follow oaos wll be requre o forulae he oel: h buyer paraeer a Orer cos ($/orer) C Cos of oe vehcle for h buyer ($/vehcle) Aual ea (u/year) aly ea of h buyer (u/ay) h Hol cos per e per year ($/u/year) q Vehcle capacy (us/vehcle) Shpp e o h buyer (ays) Veor Paraeer h Aual rae of ea (u/year) Salles bach sze Hol cos per e per year ($/u/year) P Prouco rae per year ( P >, P / ), (u/year) S Seup cos ($/seup) Varables Oral shpp sze for h buyer G G Shpp sze for h buyer whch clues rasporao e ea Salles bach sze Bach sze whch clues rasporao e ea for buyers Nuber of equal or uequal baches a lo

21 3.3 Moel Forulao I hs research, he veory oels are evelope for hree prouco a shpp polces. The frs oel as o prouce es equal sze baches a lo. Ies are shppe o he buyers as soo as he bach prouco s fshe. The seco oel also assues ha es are prouce equal-sze baches a lo a ha he veor hols he e ul buyers place a orer. The hr oel aeps o prouce es uequal-sze baches a lo, a he veor hols he e ul orer s receve. Assu he veor prouces es los, a lo cosss of baches, a baches are prouce sze of, buyers receve he e proporo o he rao of her ea o he oal ea of bach sze. We ca wre: Thus,. (3.). (3.) I hs research, we are coser rasporao e, a we acowlee ha rasporao es vary bewee buyers. Now, we wa o shp proucs o all buyers a he sae e fro he veor s e, bu sce rasporao es vary bewee buyers, soe buyers h have o receve he prouc before or afer he prevous bach s exhause. To ze hol cos, we wa o assure ha each buyer receves a ew shpe whe he prevous bach s ear epleo. To coser hs scearo, we propose o a vual rasporao e ea wh he buyer s correspo shpe. Assue rasporao e o buyer s, so he ew shpe sze for h buyer becoes where G, (3.3) s he aly ea for h buyer. The suao of oral bach sze a rasporao e ea for all he buyers leas o 3

22 G. (3.4) 3.4 Moel I: Ie Shppe Rh Afer Prouco As escrbe above, a veor shps es o buyers as soo as he bach s prouce. I he frs oel, we are assu all bach szes a lo are equal a ha here are baches a lo. Fure 3. shows he prouco a veory flow for he frs oel. I Fure 3., P,, a are he e sees, whch represe he follow: prouco e for a bach, cosupo e for h buyer a rasporao e for h buyer, respecvely. I Fure 3., rale (ABC) represes he veory for he veor. The rale (MNO) represes he eal veory for he h buyer whe a e s receve a he e of prevous veory. The area (MOPQ) wh a ashe le represes veory he buyers warehouse whle he prevous bach s be cosue. I he be of he cycle, he veor bes prouco a a fe rae, a veory bes o accuulae a a cosa rae. I Fure 3., he slope AC s he rae of bul veory ur prouco. As soo as prouco of he bach s coplee, es are shppe o each buyer, a veory of veor reuces o zero. A hs po, he veor prouces he ex bach, repea he process ul he ere lo s prouce. Alhouh es o all buyers are shppe a he sae e, hey wll receve he afer e, sce shpp e ffers ao he buyers. Ies are avalable for cosupo as soo as he buyer receves he; a, he buyer cosues he e a a cosa rae. I Fure 3., slope MN s he cosupo rae. Sce a buyer cosues es a a fxe rae, o-ha veory sars ecreas a a cosa rae. Aa, as we assue P > a / P < / 4

23 ( ) P X Y Q M BUYER BATCH SIZE P O C N NEW LOT VENOR /P A B NEW LOT TIME Fure 3.: Iveory flow Moel I o avo shorae, a buyer wll receve he ex shpe before her o-ha veory s exhause. Ths ew shpe reas he warehouse ul he prevous bach s cosue. Noce, as he cycle coues, he e ew shpe reas le he warehouse ulples, as show as MQ he Fure 3., where ( ){ ( P )} he e ew bach reas he buyer s veory. I coras o hs research, Hoque (008) o coser shpp e, a he buyer receves he e as soo as s shppe. So, he prese research, he buyer oes o have o hol veory s warehouse for aou of e Averae Iveory Calculao Ths seco erves he averae veory Moel I for he ere supply cha syse coa a sle veor a ulple buyers. Sce he ea s eersc 5

24 a prohbs baclo or shorae, he prouco rae us be reaer ha he ea rae. The oal syse veory cosss of buyer s veory, I a veor s veory, I. (a) Buyer s Averae Iveory Calculao b Each buyer receves aou of a e each shpe a aes aou of e o cosue. So, each buyer s averae veory for he frs bach s ( ). Sce he seco shpe arrves before he frs shpe s fshe, he buyer us hol he seco shpe for { P )} aoal aou of e ( ul prevous shpe s cosue. Therefore, averae veory ur he seco } shpe s [ { ( P ) ]. The hr shpe arrves before he seco shpe s fshe a acually reas he warehouse wce he e of he seco shpe. I Fure 3., we ca see QM s ouble of XY, whch are he curre es whch shpp reas le he warehouse. Ths coues ul he ere lo s supple, f here are baches a lo, he he h shpe us rea he warehouse for ) { ( P )} aou of e. Therefore, he oal averae veory for h ( buyer per cycle ca be expresse as follows: Iv cycle ( 3... ( ). (3.5) P If each bach sze s a here are baches a lo, he a coplee cycle, aou of es are prouce. So, he oal uber of cycles a year s ( ). Therefore, averae veory for h buyer per year ca be expresse as follows: Iv year ( 3... ( ) P. (3.6a) 6

25 By subsu equao (3.6a) a upo splfcao, yels P Iv year Therefore, he oal averae veory for all buyers per year s. (3.6) Iv b ( ) P. (3.7) (b) Veor s Averae Iveory Calculao The veor prouces es baches a a fe rae a hols he ul prouco of he frs bach s fshe. If each bach sze s a requres P pero o prouce, he averae veory ur he frs cycle s ( P ). Sce here are baches a cycle, he averae veory per cycle ca be expresse as Iv cycle v. (3.8a) P Sce here are uber of cycles per year, averae veory per year ca be expresse as follows: Iv yearv. (3.8) P P 3.4. Toal Averae Iveory Calculao I a erae supply cha syse, he oal averae veory s calculae by su averae yearly veores of he veor, equao (3.8), a all buyers, equao (3.7). Therefore, he yearly averae veory of he syse ca be wre as follows: Iv yearsyse P ( ) P. (3.9) 7

26 3.5 Toal Cos For Moel I I he curre a prevous secos, veores for he veor a all buyers are calculae uer he assupos of frs oel. Geerally, he oal cos of he syse cosss of ajor coss such as (a) orer cos, (b) seup cos, (c) veory hol cos, a () rasporao cos. The oal syse cos, coss of orer cos, seup cos, veory hol cos, a rasporao cos, ca be calculae Orer Cos Each e a buyer places a orer o he veor curs a cos, whch ay coss of paper wor, elephoe coversaos, ec., he each buyer presuably places a orer before he cycle sars. Hece, each buyer wll place uber of orers a year. Therefore, ( ) buyers a year wll place uber of orers. The cos of plac oe orer for h buyer s a. Therefore, he oal orer cos, A, for all buyers ca be expresse as A a, (3.0) where s he ea rae (us/year), s he uber of baches a cycle a s bach sze Seup Cos I each ew prouco cycle ha a veor sars for a ew lo, a seup cos, such as cha ye, pu raw aerals ec s requre. If he aufacur process requres seup for every ew lo, he oal uber of seups requre s cos ( S ) ca be calculae as,. Hece, he oal seup S S, (3.) 8

27 where s he ea rae (us/year), s he uber of baches a cycle, s he bach sze, a S he seup cos per lo Iveory Carry Cos Whle he bach s be prouce, veory buls up. Thus, he veor curs he veory carry cos ul prouco s fshe a he es are shppe. Slarly, each buyer receves es a hols he ul all he es are cosue. Therefore, each buyer also curs e carry coss. The oal syse veory carry cos ca be calculae. (a) Iveory Carry Cos for Buyers Fro equao (3.7) we ow he averae yearly veory for all buyers. If h s he carry cos for h buyer, veory hol cos for all buyers per year ca be calculae as Iv b h ( ) h h P, (3.) where s yearly ea of h buyer, s bach sze, s ea rae (us/year), s uber of baches a lo; P s prouco capacy (us/year) a s rasporao for h buyer. (b) Iveory Carry Cos for Veor Fro equao (3.8) we ow averae yearly veory for he veor. If h s he carry cos for veor, veory hol cos ( h ) for he veor per year ca be calculae as h h. (3.3) P 9

28 (c) Toal Syse Carry Cos Calculao Toal syse s carry cos cosss of he buyer s carry cos per year a veor s carry cos per year. Equaos (3.) a (3.3) represe all buyers carry coss a he veor s carry cos, respecvely. Hece, he oal syse s carry cos ( h sys ) ca be expresse as h sys h h h P h. (3.4) P Trasporao Cos Every e he veor ses a shpe o a buyer, he buyer curs a rasporao cos. Realscally, he capacy of a rasporao vehcle s le. Aa, eve f a coveyace s parally flle, he buyer has o pay he prce of a full loa. Aoher coserao s rasporao cos for oe shpe, whch ay ffer ao buyers epe o her saces fro he veor. If q s he carry capacy of a coveyace a s he shpe sze, he recev buyer has o pay for ( q ) uber of loas. If he coveyace cos s C for h buyer a here are uber of baches a cycle, he rasporao cos per cycle ( T cycle ), pa by h buyer, ca be calculae as T C. (3.5) cycle q h Sce here are ( ) uber of cycles a year, rasporao cos o buyer per year ca be calculae as T year C C. (3.6) q q 0

29 By equa ( ), where s bach sze (es/bach) a s he yearly ea (es/year). Sce here are buyers he syse, oal rasporao cos ( buyers a year ca be expresse as T b ) for all T b C q. (3.7) Toal Syse Cos The oal syse cos, TC I cosss of orer cos, seup cos, veory carry cos, a rasporao cos. Hece, he oal syse cos for Moel I ca be calculae by a equaos (3.0), (3.), (3.4), a (3.7) as TC I S h P a C q h h P h.(3.8) 3.6 Soluo Mehooloy A hs po, oe us uersa he aure of he oal syse cos ( TC I ) fuco for opzao purposes. If he oal cos fuco s fou o be covex, he TC I 0 leas o opaly. By loo a q C equao (3.8), s clear ha TC I s o a covex fuco ve cosa. A opal bach quay * ca be calculae by a rec search eho wh a bouary of (,0), a (, ). Slarly, a opal uber of baches * ca also be calculae by a rec search eho wh a bouary of (,0), a (, ) us equao (3.8).

30 Exaple 3. Ie shppe equally-sze baches upo prouco copleo Assue, P 900 us/year, 50 us/year, S 90, h. 3, q 30. aa s ve Table. For eer, he u oal cos s presee Table. Necessary u coverso s perfore pror o soluo. For eale resuls, see Appex. Fure 3., a plo us MaLab 009b; he u oal cos s are o he fure. I Fure 3., a sue cu occurs he plo because a ha po he uber of vehcle requre chaes for soe buyers. Buyer Table : aa for sle veor 9 buyers proble (ays) a SUM 4 50 h C Table : Suary of resuls N TC I

31 TC TC Fure 3.: Graphcal represeao of TC I 3.7 Sesvy Aalyss for Moel I The oal cos s a soluo of he oel where oel paraeers (veor hol cos, seup cos, a prouco o oal yearly ea rao) are presuably fxe. I s useful o carry ou sesvy aalyss of he oel. For hs purpose, effec of chaes syse paraeers us be verfe o chec f he curre soluo. Reas uchae.. Becoes sub-opal, ec. 3

32 3.7. Effec of S o TC I The veor seup cos S plays a ajor role eer oal cos of he syse, a also affecs he opal bach sze a uber of baches a lo. The effec of S o oal syse cos, TC I s expresse aheacally equao (3.9), TC I S, (3.9) whch s a cosa er. The rae a reco of chae of TC I wh respec o S epe o he values of paraeers use he uercal exaple. If we pu values of,, a fro Table 0 a Table equao (3.9), he we ca observe ha for u chae S, TC I wll crease by $ Effec of h o TC I The veor s hol cos h s a pora paraeer evelop he oel. The effec of h o oal syse cos TC I s expresse aheacally equao (3.0), TC I, (3.0) h P whch s also a cosa er. The rae a reco of chae of TC I wh respec o h epe o he values of paraeers use he uercal exaple. If we pu values of,, a fro Table 0 a Table (presee laer), he we ca wre, ha for every u crease (ecrease) h, TC I wll crease (ecrease) by $ Effec of P/ o TC I The rao bewee prouco rae a oal yearly ea P/ s o oly a pora paraeer o eere he oal cos; bu also, cojuao wh he veor hol cos h, he rao eeres whch oel o choose for a parcular scearo ao he hree. The effec of P/ o oal syse cos, equao (3.), TC I s expresse aheacally 4

33 TC h I A A A h. (3.) The effec of P/ over TC s represee by Fure 3.3 us equaos (3.) I a P [ 0,5]. A suy s perfore wh respec o P/ where he paraeer values are he sae excep for he values of P/, whch chaes fro o 5, a he resuls are presee Table 3. Fure 3.3 llusraes ha P/ rao affecs TC I chaes raply up o., a beyo hs po, TC I becoes less sesve o a chae P/. We ca also wre ha TC I creases as he rao creases. Table 3 suarzes he chae he chae P/ rao. 3.8 Moel II: Ies Are Shppe I Every Pero TC I for As escrbe above, a veor hols es a shps he every pero. I he seco oel we are assu all bach szes a lo are equal a here are baches a lo. Fure 3.4 shows he prouco a veory flow for he seco oel. I Fure 3., G P, G, a, are he e sees, whch represe he prouco e for a bach clu rasporao e ea, cosupo e for h buyer a h rasporao e for buyer, respecvely. I Fure 3.4, rale (MNO) represes he eal veory for he veor. The rale (ABC) represes he eal veory for he h buyer whe he e s receve a he e of prevous veory. The area (XYZ) represes he veory bul up he veor s warehouse ue o cosa prouco of es. 5

34 Table 3: Effec of P/ ( A) rao o A TC I A TC I TC I A A Fure 3.3: Effec of P/ ( A) rao o TC I 6

35 P A BATCH SIZE O G Y P X Z Q T R S Q B C BUYER VENOR M G/P N ( G P ) NEW LOT TIME Fure 3.4: Iveory flow Moel II I he be of he cycle, he veor sars prouco a a fe rae a veory buls up a a cosa rae. I Fure 3.4, he slope MO s he rae of accuula veory ur prouco. As soo as prouco of he frs bach s coplee, es are shppe o each buyer, a veory of veor s eplee. A hs po, he veor bes prouc he ex bach. Sce we assue P > o avo shorae, veory bes bul up for he veor beyo G as G / P < G /, as show Fure 3.4. Whe a buyer s veory rops o rasporao e ea, ew es are shppe, a he veor s veory s reuce by G. Ths process s repeae ul he ere lo s prouce. Ies are shppe o all buyers a he sae e, bu buyers wll receve es afer e sce shpp e for each buyer vares. Ies are realy avalable o cosue as soo as he buyer receves he, a he buyer cosues he es a a cosa rae. I Fure 3.4, slope AC s he cosupo rae. Sce buyer cosues es a 7

36 cosa rae, o-ha veory bes ecreas cosaly. Whe he ew es arrve, veory rops o zero. Hece, hey buyer oes o have o hol ew es for exra e, ule he frs oel. Noce ha he cycle propels he e so ha ew es he veor s warehouse ulply. Sce he prouco s couous ul he ere lo s prouce, he veory level also rses wh e. Fure 3.4 proves hs oo wh he area of rale (QRS), whch s ouble ha of rale (XYZ) Averae Iveory Calculao Ths seco erves he averae veory Moel II for he ere supply cha syse coa a sle veor a ulple buyers. Aa, he ea s eersc a prohbs baclo a shorae; he prouco rae s presuably reaer ha he ea rae o avo shorae. The oal syse veory cosss of buyer s veory, a veor s veory, I. (a) Buyer s Averae Iveory Calculao Each buyer receves G aou of e each shpe a aes G aou of e o cosue. So each buyer s averae veory for he frs bach s ( G ). If h here are baches a lo, averae veory,, for buyer per cycle ca be expresse as, I b G. (3.) I b We eere ha G, a upo subsu Equao (3.) a splfy, we oba 8

37 9 b I. (3.3) Sce here are uber of cycles per year, he he averae veory, per year ca be expresse as I year G I year. (3.4) We eere, so subsu ( G ) Equao (3.4), we e, G G G I year ) ( ) (. (3.5) Upo splfcao hs resuls, G G G G I year, (3.6) Therefore, averae veory, for all ( buyers) buyers per year ca be expresse as I b b G G I. (3.7) (b) Veor s Averae Iveory Calculao The veor prouces es baches a a fe rae a hols he ul he prouco of he frs bach s fshe, a he, he shps every / pero. Reeber, o save o orer cos, buyers oly place orer he be of he lo a every bach s auoacally shppe afer a / pero. If each bach sze s G a requres G/P peros o prouce, he averae veory ur he frs bach s P G. Sce he seco bach s shppe afer was prouce, he veor hols hs bach for ) ( P G pero of e. Therefore, he averae veory ur he seco bach s [ ) ( P G G P G ]. The hr shpe also reas for a whle before s shppe a acually reas relavely

38 loer (wce) ha he seco bach. I Fure 3.4, we ca see ha QS s ouble YZ, whch represes he es ha prevous baches are rea he warehouse. Ths coues o occur ul he ere lo s exhause. If here are baches a lo, he h bach wll have o rea warehouse for ( )( G P) aou of e. Therefore, he oal averae veory for he veor, per cycle, ca be expresse as G G v G{ 3... ( ) }. (3.8) P P I cycle If each bach sze s G a here are baches a lo, he a coplee cycle, G aou of es are prouce. So, he oal uber of cycles a year s. Therefore, averae veory for he veor per year ca be expresse as I v G G G{ 3... ( ) }, (3.9) ' P P Q where he lo sze Q ' G. We efe G, by subsuo expresso ers of G, so G G G I v G{ 3... ( ) }, (3.30) G P P hs, afer splfcao, yels I v ( ) G ( ) ( ) G P G. (3.3) P 3.8. Toal Averae Iveory Calculao I a erae supply cha syse he oal averae veory s calculae by a averae yearly veores for all buyers us equao (3.7) a veor equao (3.3). Therefore, he yearly averae veory of he syse ca be wre as follows: 30

39 G I ( ) G G. P G G P (3.3) 3.9 Toal Cos for Moel II I he curre a prevous secos, veores for he veor a all of he buyers are calculae uer assupos of he seco oel. Usually, he oal cos of he syse cosss of he ajor coss, such as (a) orer cos, (b) seup cos, (c) veory hol cos, a () rasporao cos. The oal syse cos coss of hese four elees ca be calculae Orer Cos Each e a buyer places a orer wh he veor, he curs a cos, whch ay be relae o paperwor, elephoe calls, ec. We assue each buyer places a orer before he cycle sars. Hece, each buyer wll place G uber of orers a year. Therefore, buyers a year wll place G uber of orers. The cos of plac oe orer for he h buyer s. Therefore, oal orer cos A for all buyers ca be expresse as a A G a, (3.33) where s he ea rae (us/year), s he uber of baches a lo a G s bach sze Seup Cos Each e he veor sars a ew lo, prouco requres a seup such as cha e, se raw aerals, ec. If he aufacur process requres seup for every ew lo, he he oal uber of seup requre s G. Thus, he oal seup cos ( ) ca be S calculae as 3

40 S S, (3.34) G where s he ea rae (us/year), s he uber of baches a cycle, G s bach sze, a S s seup cos per lo Iveory Carry Cos Whle he bach s be prouce, veory buls up. Thus, he veor curs he veory carry cos ul prouco s coplee a es are shppe. Slarly, each buyer receves es a hols he ul all of he es are cosue. Therefore, each buyer also curs he e carry cos, a he oal syse veory carry cos ca be calculae. (a) Iveory Carry Cos for Buyers Fro equao (3.7) we ow he averae yearly veory for all buyers. If h s he carry cos for h buyer, veory hol cos for all buyers per year ca be calculae as H b G h h h G, (3.35) where s he yearly ea of h buyer, G s bach sze, s ea rae (us/year), s uber of baches a lo, a P s prouco capacy (us/year). (b) Iveory Carry Cos for Veor Fro equao (3.3) we ow he averae yearly veory for he veor. If h s he carry cos for veor, veory hol cos ( calculae as h ) for he veor per year ca be I v Gh ( ) Gh ( ) h P ( ) Gh. (3.36) P 3

41 (c) Toal Syse Carry Cos Calculao Toal syse s carry cos cosss of he buyer s carry cos per year a veor s carry cos. Equaos (3.35) a (3.36) represe all buyers carry cos a veor s carry cos, respecvely. Hece, he oal syse s carry cos H ca be expresse as Trasporao Cos G H h h G Gh ( ) h G G. P P h (3.37) Every e he veor ses a shpe o a buyer, he buyer curs rasporao cos. I realy, he capacy of a rasporao vehcle s le. Aa, eve f a vehcle s parally flle, he buyer has o pay he ere prce of full loa. Aoher facor s rasporao cos for oe shpe ca be ffere ao buyers epe o her saces fro he veor. If q s he carry capacy of a vehcle a s he shpe sze, he recev buyer has o pay for C G q uber of vehcles. If he cos/vehcle s for h buyer, a here are uber of baches a cycle, he, rasporao cos per cycle ( T cycle ) o he h buyer ca be calculae as, T cycle G q C. (3.38) Sce here are h uber of cycles a year, rasporao cos o buyer per year ca be calculae as, T G G year C C. (3.39) G q G q 33

42 Equa G G, where G s bach sze (es/bach) a s yearly ea (es/year). Sce here are buyers he syse, oal rasporao cos T for all buyers a year ca be expresse as T G G q C. (3.40) Toal Syse Cos The oal syse cos TC II, cosss of orer cos, seup cos, veory carry cos, a rasporao cos. Hece, he oal syse cos for Moel I ca be calculae by a equaos (3.33), (3.34), (3.37), a (3.40) as TC II G G a S G G h G q C G h h Gh ( ) h G P G. P (3.4) 3.0 Soluo Mehooloy A hs po, s ecessary o uersa he aure of he oal syse cos TC II fuco for opzao purposes. If he oal fuco TC II s covex, he, * TC II 0, whch allows for opaly a opal bach quay evaluao. By loo a G G q C equao (3.4), s clear ha TC II s o a covex fuco ve reas cosa. A opal bach quay * ca be calculae by a rec search eho wh a bouary of (,0) a (, ). Slarly, a opal uber of baches * ca also be calculae by a rec search eho wh a bouary of (,0) a (, ) us equao (3.4). 34

43 Exaple 3. Orer shppe afer every / pero Assue, P 300 us/year, 300 us/year, S $50, h., a q 40. aa s ve Table 4. For eer, he u oal cos s $377. (for, a 506), presee Table 4. Necessary u coverso s perfore pror o soluo. For eale resul see Appex. I Fure 3.5, a sue cu occurs he plo because a ha po he uber of vehcle requre chaes for soe buyers. Buyer Table 4: aa for sle veor 9 buyers proble (ays) a h C 3. Sesvy Aalyss for Moel II Table 5: Suary of resuls TC II Sesvy aalyss has bee perfore hs seco o es he robusess of he propose oel, whch s subjec o chae ve paraeers, seup cos, veor hol cos, a prouco o ea rao. 35

44 3.. Effec of S o TCII The veor seup cos plays a ajor role eer oal cos of he syse, a also affecs he opal bach sze. So, s pora o uersa he effec of chae S o TC. The effec of S o oal syse cos TC s expresse equao (3.4), II TC II S G II, (3.4) whch s a posve cosa er. I ca be wre ha TC II creases (ecreases) wh a crease (ecrease) seup cos S. If we plee values of, a fro Table 0 a Table (show laer) for a u chae S, he wll crease by $.. TC II TC TC Fure 3.5: Graphcal represeao of TC II 36

45 3.. Effec of h o TCII The veor s hol cos s a pora paraeer eer he oal cos. Also, o a exe, he veor s hol cos overs whch oel shoul be chose ao he hree presee. I s pora o uersa how veor hol cos h affecs TC II. The effec of h o TC II oal syse cos s expresse equao (3.43), TC h II G ( ) G P G, (3.43) P whch s also a posve, cosa er. I ca be wre ha TC II creases (ecreases) wh a crease (ecrease) hol cos h. By ser values of,, P, a G fro Table 0 a Table (show laer), here wll be a u chae h a wll crease by $ Effec of P/ o TCII The rao bewee prouco rae a oal yearly ea o oly eeres he oal cos, bu also, eeres, cojuao wh veor hol cos, whch oel o choose for a parcular scearo ao he hree presee. Hece, s crucal o uersa he effec of P/ o oal syse cos TC. The effec of P/ o oal syse cos TC, s expresse equao (3.44). II TC II II TCII A Gh A ( )h G A. (3.44) The effec of P/ over TC II s represee by Fure 3.6 us equaos (3.44) a P [,5]. A suy s perfore wh respec o P/ where he paraeer values are he sae excep for he values of P/, whch shfs fro o 5; he resuls are presee Table 6. Fure 3.6 reveals ha P/ rao affecs TC II a chaes raply up o.0. 37

46 Beyo hs po, TC II becoes less sesve o chae P/. We ca also wre ha TCII creases as he rao ecreases. Table 6 suarzes he chae TCII for he chae P/ rao. 3. Moel III: Ies Shppe Uequal Baches As escrbe above, he veor hols es ul he orer s place a he shps es o buyers. I he hr oel, we assue ha bach szes a lo are uequal, ha hey crease by a facor (where P ) a ha here are baches a lo. Fure 3.7 shows he prouco a veory for he hr oel. A lo s prouce wh bach szes of,,,,. I Fure 3.7, ( ) P ( ),, a, are he e sees, whch represe he bach prouco e, clu rasporao e ea, cosupo e for h buyer a rasporao e for h buyer, respecvely. I Fure 3.7, rales MNO, OPQ a QRS represe veores for he veor for subseque baches. Trales ABC, CE a EFH represe veores for he h buyer whe he e s receve eaely a he e of prevous veory. I he be of he cycle, he veor bes prouco a a fe rae a veory buls up a a cosa rae. I Fure 3.7 he slope MN s he rae of veory bul-up ur prouco. As soo as prouco of he frs bach s coplee, es are shppe o each buyer, a veor veory reuces o zero. A hs po, he veor bes prouc he ex bach of sze. Reeber, we assue P > o avo shorae. Ule he frs wo oels, he hr oel s bach sze creases wh a ulplcao facor of. Hece, o avo shorae, 38

47 A Fure 3.6: Effec of P/ ( A) rao o TC II Table 6: Effec of P/ ( A) rao o A TC II A TC II / P /. Whe he buyer s veory falls o he rasporao e ea, ew es are shppe, a he veor s veory s reuce o zero. Ths s repeae ul he ere lo s prouce. Ies are shppe o all buyers a he sae e, bu buyers wll receve es afer e sce shpp es vary ao 39

48 buyers. Ies are realy avalable o cosue as soo as he buyer receves he, a buyer cosues he es a a cosa rae. I Fure 3.7, slope BC s he cosupo rae. Sce he buyer cosues es a a cosa rae, o-ha veory bes o ecrease cosaly. Whe he ew es arrve, veory rops o zero. Hece, he buyer s o requre o hol ew es for exra e, ule he frs oel. Ule he oher wo oels, hs oel, eher he veor or he buyers ee o hol a ew bach s warehouse whle he prevous bach s be cosue. 3.. Averae Iveory Calculao Ths seco erves he averae veory Moel III for he ere supply cha syse coa a sle veor a ulple buyers. Slar o he oher wo oels, he ea s eersc a o baclo or shorae s allowe, he prouco rae has o be reaer ha he ea rae. The oal syse veory cosss of buyer s veory I b a veor s veory. (a) Buyer s Averae Iveory Calculao I Each buyer receves aou of es for he frs shpe, followe by,,, es. Each buyer aes ( ) e o cosue he bach fully so each buyer s averae veory for he frs bach s ( ). For he seco bach, he averae veory s ( ) ). Slarly, for he h bach, he averae veory s ( ). If here are h baches a lo, he averae veory ( ) for buyer per cycle ca be expresse as I b I b [( ) ( ) ( )... ( ) ].. (3.45) 40

49 4 Fure 3.7: Iveory flow Moel III Upo splfcao hs leas o b I. (3.46) We ow, a subsu equao (3.46), we e, BUYER A B C E F H M N O P Q R S TIME BATCH SIZE ) ( ) ( ) ( ) ( ) ( ) ( P ) ( P ) ( P ) ( VENOR NEW LOT NEW LOT

50 4 b I. (3.47) Assue he su of all baches a lo equals, so he lo sze s ' Q G Q '. (3.48) Therefore, here wll be uber of los a year. Hece, he averae veory for buyer per year ca be expresse as ' / Q year I h ' Q I year. (3.49) If here are buyers he syse, he averae veory for all buyers per year ca be expresse as b Q I '. (3.50) (b) Veor s Averae Iveory Calculao The veor prouces bach es a a fe rae wh a bach of szes,,,...,. The veor hols es ul prouco s fshe a shps as soo as he bach prouco s coplee. Sce bach szes are uequal, we ca calculae he wor--process (WIP) veory for he veor. Hece, he WIP veory per cycle ca be expresse as: P P P P WIP cycle... ) (. (3.5)

51 43 If he lo sze s a here are uber of cycles a year, he averae WIP veory (WIP ) per year ca be expresse as ' Q year ' / Q year P Q WIP ', (3.5) Where P/, s yearly ea (e/year), s he u bach sze, s uber of baches a lo, s lo sze (e/lo) a P s prouco rae (e/year). ' Q 3.. Toal Averae Iveory Calculao I a erae supply cha syse, he oal averae veory s calculae by a averae yearly veores of all buyers a veors us equaos (3.50) a (3.5). Therefore, yearly averae veory I of he syse ca be wre as P Q I '. (3.53) 3.3 Toal Cos for Moel III I he curre a prevous secos, veores for he veor a all buyers are calculae as he seco oel. Usually, he oal cos of he syse cosss of ajor coss: (a) orer cos, (b) seup cos, (c) veory hol cos, a () rasporao cos. The oal syse cos coss of hese coss ca be calculae Orer Cos Each e a buyer places a orer o he veor, he buyer curs a cos, whch ay coss of paperwor, elephoe calls, ec. Assue each buyer places a orer before he cycle sars. Hece, each buyer wll place uber of orers a year. Therefore, buyers a year wll place uber of orers. The cos of plac oe orer for h ' / Q ' / Q

52 buyer s. Therefore, oal orer cos A for all buyers ca be expresse as a A a, (3.54) ' Q where s he ea rae (us/year), s he uber of baches a cycle, a ' Q s lo sze (es/lo) Seup Cos Each e he veor bes prouco of a ew lo, he curs a seup cos for cha e, se raw aerals, ec. If he aufacur process requres seup for every ew lo, he oal uber of seup requres s ca be calculae as, ' / Q. Hece, he oal seup cos S S S, (3.55) ' Q where s he ea rae (us/year), s he uber of baches a cycle, ' Q s lo sze (es/lo) a S seup cos per lo ($/lo) Iveory Carry Cos Whle he bach s be prouce, veory buls up. Thus, he veor curs a veory carry cos ul prouco s coplee, a he he shps he es. Slarly, each buyer receves es, hol he ul all es are cosue. Therefore, each buyer also curs a carry cos. The oal syse veory carry cos ca be calculae. (a) Iveory Carry Cos for Buyers Fro equao (3.50), we ow he averae yearly veory for all buyers. If h s he carry cos for h buyer, he veory hol cos ca be calculae as h b for all buyers per year 44

53 45 b h h h Q h ', (3.56) where s he yearly ea of h buyer, s bach sze, s ea rae (us/year), s uber of baches a lo a P s prouco capacy (us/year). (b) Iveory Carry Cos for Veor Fro equao (3.5), we ow he averae yearly veory for he veor. If s he carry cos for he veor, he veory hol cos for he veor per year ca be calculae as follows: h h v v P h Q h '. (3.57) (c) Toal Syse Carry Cos Calculao Toal syse s carry cos cosss of he buyer s carry cos per year a he veor s carry cos. Equaos (3.56) a (3.57) represe he carry coss for all of he buyers as well as for he veor, respecvely. Hece, he oal syse s carry cos H ca be expresse as h P h h P h h P h Q H '. (3.58) Trasporao Cos Every e he veor ses a shpe o a buyer, he buyer curs rasporao cos. I realy, capacy of a rasporao vehcle s le. Aa, eve f a vehcle s parally flle, he buyer has o pay accor o he full-loa prce. Aoher h o coser s he rasporao cos for oe shpe because ay vary ao ffere buyers epe o her saces fro he veor. If s carry capacy of a vehcle q

International Journal of Engineering Technology, Management and Applied Sciences. January 2017, Volume 5, Issue 1, ISSN

International Journal of Engineering Technology, Management and Applied Sciences.   January 2017, Volume 5, Issue 1, ISSN ecs a eaco echas of Cerc Iae Graf Coolyerzao of Ehyl Acrylae oo Sou Sal of Parally Carboxyehylae Sou Alae J. H. Trve*.. Prajaa P. G. Deare of Chesry Gujara Iusral esearch a Develoe Aecy (GIDA) Sarar Pael

More information

Continuous Time Markov Chains

Continuous Time Markov Chains Couous me Markov chas have seay sae probably soluos f a oly f hey are ergoc, us lke scree me Markov chas. Fg he seay sae probably vecor for a couous me Markov cha s o more ffcul ha s he scree me case,

More information

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations Joural of aheacs ad copuer Scece (4 39-38 Soluo of Ipulsve Dffereal Equaos wh Boudary Codos Ters of Iegral Equaos Arcle hsory: Receved Ocober 3 Acceped February 4 Avalable ole July 4 ohse Rabba Depare

More information

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1) Aoucemes Reags o E-reserves Proec roosal ue oay Parameer Esmao Bomercs CSE 9-a Lecure 6 CSE9a Fall 6 CSE9a Fall 6 Paer Classfcao Chaer 3: Mamum-Lelhoo & Bayesa Parameer Esmao ar All maerals hese sles were

More information

Stat 6863-Handout 5 Fundamentals of Interest July 2010, Maurice A. Geraghty

Stat 6863-Handout 5 Fundamentals of Interest July 2010, Maurice A. Geraghty S 6863-Hou 5 Fuels of Ieres July 00, Murce A. Gerghy The pror hous resse beef cl occurreces, ous, ol cls e-ulero s ro rbles. The fl copoe of he curl oel oles he ecooc ssupos such s re of reur o sses flo.

More information

International Journal of Scientific & Engineering Research, Volume 3, Issue 10, October ISSN

International Journal of Scientific & Engineering Research, Volume 3, Issue 10, October ISSN Ieraoal Joural of cefc & Egeerg Research, Volue, Issue 0, Ocober-0 The eady-ae oluo Of eral hael Wh Feedback Ad Reegg oeced Wh o-eral Queug Processes Wh Reegg Ad Balkg ayabr gh* ad Dr a gh** *Assoc Prof

More information

Reliability Analysis of Sparsely Connected Consecutive-k Systems: GERT Approach

Reliability Analysis of Sparsely Connected Consecutive-k Systems: GERT Approach Relably Aalyss of Sparsely Coece Cosecuve- Sysems: GERT Approach Pooa Moha RMSI Pv. L Noa-2131 poalovely@yahoo.com Mau Agarwal Deparme of Operaoal Research Uversy of Delh Delh-117, Ia Agarwal_maulaa@yahoo.com

More information

Midterm Exam. Tuesday, September hour, 15 minutes

Midterm Exam. Tuesday, September hour, 15 minutes Ecoomcs of Growh, ECON560 Sa Fracsco Sae Uvers Mchael Bar Fall 203 Mderm Exam Tuesda, Sepember 24 hour, 5 mues Name: Isrucos. Ths s closed boo, closed oes exam. 2. No calculaors of a d are allowed. 3.

More information

Mathematical Formulation

Mathematical Formulation Mahemacal Formulao The purpose of a fe fferece equao s o appromae he paral ffereal equao (PE) whle maag he physcal meag. Eample PE: p c k FEs are usually formulae by Taylor Seres Epaso abou a po a eglecg

More information

The Properties of Probability of Normal Chain

The Properties of Probability of Normal Chain I. J. Coep. Mah. Sceces Vol. 8 23 o. 9 433-439 HIKARI Ld www.-hkar.co The Properes of Proaly of Noral Cha L Che School of Maheacs ad Sascs Zheghou Noral Uversy Zheghou Cy Hea Provce 4544 Cha cluu6697@sa.co

More information

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall 8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model

More information

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF EDA/DIT6 Real-Tme Sysems, Chalmers/GU, 0/0 ecure # Updaed February, 0 Real-Tme Sysems Specfcao Problem: Assume a sysem wh asks accordg o he fgure below The mg properes of he asks are gve he able Ivesgae

More information

Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse

Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse P a g e Vol Issue7Ver,oveber Global Joural of Scece Froer Research Asypoc Behavor of Soluos of olear Delay Dffereal Equaos Wh Ipulse Zhag xog GJSFR Classfcao - F FOR 3 Absrac Ths paper sudes he asypoc

More information

ES 330 Electronics II Homework 03 (Fall 2017 Due Wednesday, September 20, 2017)

ES 330 Electronics II Homework 03 (Fall 2017 Due Wednesday, September 20, 2017) Pae1 Nae Soluios ES 330 Elecroics II Hoework 03 (Fall 017 ue Wedesday, Sepeber 0, 017 Proble 1 You are ive a NMOS aplifier wih drai load resisor R = 0 k. The volae (R appeari across resisor R = 1.5 vols

More information

AML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending

AML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending CUIC SLINE CURVES Cubc Sples Marx formulao Normalsed cubc sples Alerae ed codos arabolc bledg AML7 CAD LECTURE CUIC SLINE The ame sple comes from he physcal srume sple drafsme use o produce curves A geeral

More information

Reliability Analysis. Basic Reliability Measures

Reliability Analysis. Basic Reliability Measures elably /6/ elably Aaly Perae faul Πelably decay Teporary faul ΠOfe Seady ae characerzao Deg faul Πelably growh durg eg & debuggg A pace hule Challeger Lauch, 986 Ocober 6, Bac elably Meaure elably:

More information

Convexity Preserving C 2 Rational Quadratic Trigonometric Spline

Convexity Preserving C 2 Rational Quadratic Trigonometric Spline Ieraoal Joural of Scefc a Researc Publcaos, Volume 3, Issue 3, Marc 3 ISSN 5-353 Covexy Preservg C Raoal Quarac Trgoomerc Sple Mrula Dube, Pree Twar Deparme of Maemacs a Compuer Scece, R. D. Uversy, Jabalpur,

More information

Turbo Coded MIMO Multiplexing with Iterative Adaptive Soft Parallel Interference Cancellation

Turbo Coded MIMO Multiplexing with Iterative Adaptive Soft Parallel Interference Cancellation Turbo Coe MIMO Mulplexg wh Ierave Aapve Sof Parallel Ierferece Cacellao Akor akaja, eepshkha Garg, a Fuyuk Aach ep. of Elecrcal a Coucaos Egeerg Tohoku Uversy, Sea, Japa akaja@oble.ece.ohoku.ac.jp Absrac

More information

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period.

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period. ublc Affars 974 Meze D. Ch Fall Socal Sceces 748 Uversy of Wscos-Madso Sock rces, News ad he Effce Markes Hypohess (rev d //) The rese Value Model Approach o Asse rcg The exbook expresses he sock prce

More information

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters Leas Squares Fg LSQF wh a complcaed fuco Theeampleswehavelookedasofarhavebeelearheparameers ha we have bee rg o deerme e.g. slope, ercep. For he case where he fuco s lear he parameers we ca fd a aalc soluo

More information

Partial Molar Properties of solutions

Partial Molar Properties of solutions Paral Molar Properes of soluos A soluo s a homogeeous mxure; ha s, a soluo s a oephase sysem wh more ha oe compoe. A homogeeous mxures of wo or more compoes he gas, lqud or sold phase The properes of a

More information

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period.

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period. coomcs 435 Meze. Ch Fall 07 Socal Sceces 748 Uversy of Wscos-Madso Sock rces, News ad he ffce Markes Hypohess The rese Value Model Approach o Asse rcg The exbook expresses he sock prce as he prese dscoued

More information

Integral Form of Popoviciu Inequality for Convex Function

Integral Form of Popoviciu Inequality for Convex Function Procees of e Paksa Acaey of Sceces: A. Pyscal a ozaoal Sceces 53 3: 339 348 206 oyr Paksa Acaey of Sceces ISSN: 258-4245 r 258-4253 ole Paksa Acaey of Sceces Researc Arcle Ieral For of Pooc Ieqaly for

More information

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Reliability Analysis

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Reliability Analysis Probably /4/6 CS 5 elably Aaly Yahwa K. Malaya Colorado Sae very Ocober 4, 6 elably Aaly: Oule elably eaure: elably, avalably, Tra. elably, T M MTTF ad (, MTBF Bac Cae Sgle u wh perae falure, falure rae

More information

(1) Cov(, ) E[( E( ))( E( ))]

(1) Cov(, ) E[( E( ))( E( ))] Impac of Auocorrelao o OLS Esmaes ECON 3033/Evas Cosder a smple bvarae me-seres model of he form: y 0 x The four key assumpos abou ε hs model are ) E(ε ) = E[ε x ]=0 ) Var(ε ) =Var(ε x ) = ) Cov(ε, ε )

More information

Optimal Control and Hamiltonian System

Optimal Control and Hamiltonian System Pure ad Appled Maheacs Joural 206; 5(3: 77-8 hp://www.scecepublshggroup.co//pa do: 0.648/.pa.2060503.3 ISSN: 2326-9790 (Pr; ISSN: 2326-982 (Ole Opal Corol ad Haloa Syse Esoh Shedrack Massawe Depare of

More information

Determination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction

Determination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction refeed Soluos for R&D o Desg Deermao of oe Equao arameers Soluos for R&D o Desg December 4, 0 refeed orporao Yosho Kumagae refeed Iroduco hyscal propery daa s exremely mpora for performg process desg ad

More information

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS Vol.7 No.4 (200) p73-78 Joural of Maageme Scece & Sascal Decso IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS TIANXIANG YAO AND ZAIWU GONG College of Ecoomcs &

More information

Cyclone. Anti-cyclone

Cyclone. Anti-cyclone Adveco Cycloe A-cycloe Lorez (963) Low dmesoal aracors. Uclear f hey are a good aalogy o he rue clmae sysem, bu hey have some appealg characerscs. Dscusso Is he al codo balaced? Is here a al adjusme

More information

The ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3.

The ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3. C. Trael me cures for mulple reflecors The ray pahs ad rael mes for mulple layers ca be compued usg ray-racg, as demosraed Lab. MATLAB scrp reflec_layers_.m performs smple ray racg. (m) ref(ms) ref(ms)

More information

Practice Problems - Week #4 Higher-Order DEs, Applications Solutions

Practice Problems - Week #4 Higher-Order DEs, Applications Solutions Pracice Probles - Wee #4 Higher-Orer DEs, Applicaions Soluions 1. Solve he iniial value proble where y y = 0, y0 = 0, y 0 = 1, an y 0 =. r r = rr 1 = rr 1r + 1, so he general soluion is C 1 + C e x + C

More information

The Poisson Process Properties of the Poisson Process

The Poisson Process Properties of the Poisson Process Posso Processes Summary The Posso Process Properes of he Posso Process Ierarrval mes Memoryless propery ad he resdual lfeme paradox Superposo of Posso processes Radom seleco of Posso Pos Bulk Arrvals ad

More information

A Second Kind Chebyshev Polynomial Approach for the Wave Equation Subject to an Integral Conservation Condition

A Second Kind Chebyshev Polynomial Approach for the Wave Equation Subject to an Integral Conservation Condition SSN 76-7659 Eglad K Joural of forao ad Copug Scece Vol 7 No 3 pp 63-7 A Secod Kd Chebyshev olyoal Approach for he Wave Equao Subec o a egral Coservao Codo Soayeh Nea ad Yadollah rdokha Depare of aheacs

More information

THEORETICAL AUTOCORRELATIONS. ) if often denoted by γ. Note that

THEORETICAL AUTOCORRELATIONS. ) if often denoted by γ. Note that THEORETICAL AUTOCORRELATIONS Cov( y, y ) E( y E( y))( y E( y)) ρ = = Var( y) E( y E( y)) =,, L ρ = and Cov( y, y ) s ofen denoed by whle Var( y ) f ofen denoed by γ. Noe ha γ = γ and ρ = ρ and because

More information

FALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below.

FALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below. Jorge A. Ramírez HOMEWORK NO. 6 - SOLUTION Problem 1.: Use he Sorage-Idcao Mehod o roue he Ipu hydrograph abulaed below. Tme (h) Ipu Hydrograph (m 3 /s) Tme (h) Ipu Hydrograph (m 3 /s) 0 0 90 450 6 50

More information

Quantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state)

Quantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state) Pro. O. B. Wrgh, Auum Quaum Mechacs II Lecure Tme-depede perurbao heory Tme-depede perurbao heory (degeerae or o-degeerae sarg sae) Cosder a sgle parcle whch, s uperurbed codo wh Hamloa H, ca exs a superposo

More information

Solution to Some Open Problems on E-super Vertex Magic Total Labeling of Graphs

Solution to Some Open Problems on E-super Vertex Magic Total Labeling of Graphs Aalable a hp://paed/aa Appl Appl Mah ISS: 9-9466 Vol 0 Isse (Deceber 0) pp 04- Applcaos ad Appled Maheacs: A Ieraoal Joral (AAM) Solo o Soe Ope Probles o E-sper Verex Magc Toal Labelg o Graphs G Marh MS

More information

Homework 2 Solutions

Homework 2 Solutions Mah 308 Differenial Equaions Fall 2002 & 2. See he las page. Hoework 2 Soluions 3a). Newon s secon law of oion says ha a = F, an we know a =, so we have = F. One par of he force is graviy, g. However,

More information

Computer Life (CPL) ISSN: Research on IOWHA Operator Based on Vector Angle Cosine

Computer Life (CPL) ISSN: Research on IOWHA Operator Based on Vector Angle Cosine Copuer Lfe (CPL) ISS: 1819-4818 Delverg Qualy Scece o he World Research o IOWHA Operaor Based o Vecor Agle Cose Megg Xao a, Cheg L b Shagha Uversy of Egeerg Scece, Shagha 0160, Cha a x18065415@163.co,

More information

A Fuzzy Weight Representation for Inner Dependence Method AHP

A Fuzzy Weight Representation for Inner Dependence Method AHP A Fuzzy Wegh Represeao for Ier Depeece Meho AHP Sh-ch Ohsh 2 Taahro Yamao Heyu Ima 2 Faculy of Egeerg, Hoa-Gaue Uversy Sapporo, 0640926 JAPAN 2 Grauae School of Iformao Scece a Techology, Hoao Uversy Sapporo,

More information

Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models

Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models Ieraoal Bomerc Coferece 22/8/3, Kobe JAPAN Survval Predco Based o Compoud Covarae uder Co Proporoal Hazard Models PLoS ONE 7. do:.37/oural.poe.47627. hp://d.plos.org/.37/oural.poe.47627 Takesh Emura Graduae

More information

SOLVING OF WAITING LINES MODELS IN THE AIRPORT USING QUEUING THEORY MODEL AND LINEAR PROGRAMMING THE PRACTICE CASE : A.I.M.H.B

SOLVING OF WAITING LINES MODELS IN THE AIRPORT USING QUEUING THEORY MODEL AND LINEAR PROGRAMMING THE PRACTICE CASE : A.I.M.H.B SOLVIG OF WAITIG LIES MODELS I THE AIRPORT USIG QUEUIG THEORY MODEL AD LIEAR PROGRAMMIG THE PRACTICE CASE : A.I.M.H.B Houa Mehr, Taoufk Djeel, Hche Kaou To ce hs verso: Houa Mehr, Taoufk Djeel, Hche Kaou.

More information

The Linear Regression Of Weighted Segments

The Linear Regression Of Weighted Segments The Lear Regresso Of Weghed Segmes George Dael Maeescu Absrac. We proposed a regresso model where he depede varable s made o up of pos bu segmes. Ths suao correspods o he markes hroughou he da are observed

More information

Upper Bound For Matrix Operators On Some Sequence Spaces

Upper Bound For Matrix Operators On Some Sequence Spaces Suama Uer Bou formar Oeraors Uer Bou For Mar Oeraors O Some Sequece Saces Suama Dearme of Mahemacs Gaah Maa Uersy Yogyaara 558 INDONESIA Emal: suama@ugmac masomo@yahoocom Isar D alam aer aa susa masalah

More information

4. THE DENSITY MATRIX

4. THE DENSITY MATRIX 4. THE DENSTY MATRX The desy marx or desy operaor s a alerae represeao of he sae of a quaum sysem for whch we have prevously used he wavefuco. Alhough descrbg a quaum sysem wh he desy marx s equvale o

More information

Random Generalized Bi-linear Mixed Variational-like Inequality for Random Fuzzy Mappings Hongxia Dai

Random Generalized Bi-linear Mixed Variational-like Inequality for Random Fuzzy Mappings Hongxia Dai Ro Geeralzed B-lear Mxed Varaoal-lke Iequaly for Ro Fuzzy Mappgs Hogxa Da Depare of Ecooc Maheacs Souhweser Uversy of Face Ecoocs Chegdu 674 P.R.Cha Absrac I h paper we roduce sudy a ew class of ro geeralzed

More information

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits.

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits. ose ad Varably Homewor # (8), aswers Q: Power spera of some smple oses A Posso ose A Posso ose () s a sequee of dela-fuo pulses, eah ourrg depedely, a some rae r (More formally, s a sum of pulses of wdh

More information

For the plane motion of a rigid body, an additional equation is needed to specify the state of rotation of the body.

For the plane motion of a rigid body, an additional equation is needed to specify the state of rotation of the body. The kecs of rgd bodes reas he relaoshps bewee he exeral forces acg o a body ad he correspodg raslaoal ad roaoal moos of he body. he kecs of he parcle, we foud ha wo force equaos of moo were requred o defe

More information

A L A BA M A L A W R E V IE W

A L A BA M A L A W R E V IE W A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N

More information

Dimension Reduction. Curse of dimensionality

Dimension Reduction. Curse of dimensionality Deso Reuco Deso Reuco Curse of esoaly h 5 feaures esos, each quaze o levels, creae 5 possble feaure cobaos, age ho ay saples you ee o esae p? ho o you vsualze he srucure a 5 esoal space? Oher probles ze

More information

1 Widrow-Hoff Algorithm

1 Widrow-Hoff Algorithm COS 511: heoreical Machine Learning Lecurer: Rob Schapire Lecure # 18 Scribe: Shaoqing Yang April 10, 014 1 Widrow-Hoff Algorih Firs le s review he Widrow-Hoff algorih ha was covered fro las lecure: Algorih

More information

Lecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination

Lecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination Lecure 3 Topc : Drbuo, hypohe eg, ad ample ze deermao The Sude - drbuo Coder a repeaed drawg of ample of ze from a ormal drbuo of mea. For each ample, compue,,, ad aoher ac,, where: The ac he devao of

More information

Chapter 2 The Derivative Applied Calculus 107. We ll need a rule for finding the derivative of a product so we don t have to multiply everything out.

Chapter 2 The Derivative Applied Calculus 107. We ll need a rule for finding the derivative of a product so we don t have to multiply everything out. Chaper The Derivaive Applie Calculus 107 Secion 4: Prouc an Quoien Rules The basic rules will le us ackle simple funcions. Bu wha happens if we nee he erivaive of a combinaion of hese funcions? Eample

More information

Solution set Stat 471/Spring 06. Homework 2

Solution set Stat 471/Spring 06. Homework 2 oluo se a 47/prg 06 Homework a Whe he upper ragular elemes are suppressed due o smmer b Le Y Y Y Y A weep o he frs colum o oba: A ˆ b chagg he oao eg ad ec YY weep o he secod colum o oba: Aˆ YY weep o

More information

Analysis of a Stochastic Lotka-Volterra Competitive System with Distributed Delays

Analysis of a Stochastic Lotka-Volterra Competitive System with Distributed Delays Ieraoal Coferece o Appled Maheac Sulao ad Modellg (AMSM 6) Aaly of a Sochac Loa-Volerra Copeve Sye wh Drbued Delay Xagu Da ad Xaou L School of Maheacal Scece of Togre Uvery Togre 5543 PR Cha Correpodg

More information

FORCED VIBRATION of MDOF SYSTEMS

FORCED VIBRATION of MDOF SYSTEMS FORCED VIBRAION of DOF SSES he respose of a N DOF sysem s govered by he marx equao of moo: ] u C] u K] u 1 h al codos u u0 ad u u 0. hs marx equao of moo represes a sysem of N smulaeous equaos u ad s me

More information

Chapter 1 - Free Vibration of Multi-Degree-of-Freedom Systems - I

Chapter 1 - Free Vibration of Multi-Degree-of-Freedom Systems - I CEE49b Chaper - Free Vbrao of M-Degree-of-Freedo Syses - I Free Udaped Vbrao The basc ype of respose of -degree-of-freedo syses s free daped vbrao Aaogos o sge degree of freedo syses he aayss of free vbrao

More information

Normal Random Variable and its discriminant functions

Normal Random Variable and its discriminant functions Noral Rando Varable and s dscrnan funcons Oulne Noral Rando Varable Properes Dscrnan funcons Why Noral Rando Varables? Analycally racable Works well when observaon coes for a corruped snle prooype 3 The

More information

Use of Non-Conventional Measures of Dispersion for Improved Estimation of Population Mean

Use of Non-Conventional Measures of Dispersion for Improved Estimation of Population Mean Amerca Joural of Operaoal esearch 06 6(: 69-75 DOI: 0.59/.aor.06060.0 Use of o-coveoal Measures of Dsperso for Improve Esmao of Populao Mea ubhash Kumar aav.. Mshra * Alok Kumar hukla hak Kumar am agar

More information

Design of observer for one-sided Lipschitz nonlinear systems with interval time-varying delay

Design of observer for one-sided Lipschitz nonlinear systems with interval time-varying delay WSEAS RANSACIONS o SYSES a CONROL Waju Lu Yal Dog Ska Zuo Desg of observer for oe-se Lpscz olear syses w erval e-varyg elay WANJUN LIU YALI DONG SHIKAI ZUO Scool of Scece aj Polyecc Uversy aj 8 CHINA ogyl@vp.sa.co

More information

Exam Supply Chain Management January 17, 2008

Exam Supply Chain Management January 17, 2008 Exam Supply Cha Maageme Jauary 7, 008 IMPORTANT GUIELINES: The exam s closed book. Sudes may use a calculaor. The formularum s aached a he back of he assgme budle. Please wre your aswers o he blak pages

More information

Enhancement of Markov Chain Monte Carlo Convergence Speed in Vehicle Tracking Using Genetic Operator

Enhancement of Markov Chain Monte Carlo Convergence Speed in Vehicle Tracking Using Genetic Operator 0 Fourh Ieraoal Coferece o Copuaoal Iellgece, Moellg a ulao Ehacee of Markov Cha Moe Carlo Covergece pee Vehcle Trackg Usg Geec Operaor We Yeag Kow, We Leog Khog, Y Kwog Ch, Isal aa, Keeh Tze K Teo Moelg,

More information

The algebraic immunity of a class of correlation immune H Boolean functions

The algebraic immunity of a class of correlation immune H Boolean functions Ieraoal Coferece o Advaced Elecroc Scece ad Techology (AEST 06) The algebrac mmuy of a class of correlao mmue H Boolea fucos a Jgla Huag ad Zhuo Wag School of Elecrcal Egeerg Norhwes Uversy for Naoales

More information

Fully Fuzzy Linear Systems Solving Using MOLP

Fully Fuzzy Linear Systems Solving Using MOLP World Appled Sceces Joural 12 (12): 2268-2273, 2011 ISSN 1818-4952 IDOSI Publcaos, 2011 Fully Fuzzy Lear Sysems Solvg Usg MOLP Tofgh Allahvraloo ad Nasser Mkaelvad Deparme of Mahemacs, Islamc Azad Uversy,

More information

On the Incompressible Navier-Stokes Equations with Damping *

On the Incompressible Navier-Stokes Equations with Damping * Apple Maheacs 3 4 65-658 hp://xoorg/436/a34489 Publshe Ole Aprl 3 (hp://wwwscrporg/oural/a) O he Icopressble Naver-Sokes Equaos wh Dapg * Weya Zhao Zhbo Zheg # Depare of Maheacs Baosha Uversy Baosha Cha

More information

Outline. Computer Networks: Theory, Modeling, and Analysis. Delay Models. Queuing Theory Framework. Delay Models. Little s Theorem

Outline. Computer Networks: Theory, Modeling, and Analysis. Delay Models. Queuing Theory Framework. Delay Models. Little s Theorem Oule Couer Newors: Theory, Modelg, ad Aalyss Guevara Noubr COM35, lecure 3 Delay Models Lle s Theore The M/M/ queug syse The M/G/ queug syse F, COM35 Couer Newors Lecure 3, F, COM35 Couer Newors Lecure

More information

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions:

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions: Paramerc coug process models Cosder coug processes: N,,..., ha cou he occurreces of a eve of eres for dvduals Iesy processes: Lelhood λ ( ;,,..., N { } λ < Log-lelhood: l( log L( Score fucos: U ( l( log

More information

Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model

Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model . Projec Iroduco Fudameals of Speech Recogo Suggesed Projec The Hdde Markov Model For hs projec, s proposed ha you desg ad mpleme a hdde Markov model (HMM) ha opmally maches he behavor of a se of rag sequeces

More information

Some Different Perspectives on Linear Least Squares

Some Different Perspectives on Linear Least Squares Soe Dfferet Perspectves o Lear Least Squares A stadard proble statstcs s to easure a respose or depedet varable, y, at fed values of oe or ore depedet varables. Soetes there ests a deterstc odel y f (,,

More information

-distributed random variables consisting of n samples each. Determine the asymptotic confidence intervals for

-distributed random variables consisting of n samples each. Determine the asymptotic confidence intervals for Assgme Sepha Brumme Ocober 8h, 003 9 h semeser, 70544 PREFACE I 004, I ed o sped wo semesers o a sudy abroad as a posgraduae exchage sude a he Uversy of Techology Sydey, Ausrala. Each opporuy o ehace my

More information

Modeling of the linear time-variant channel. Sven-Gustav Häggman

Modeling of the linear time-variant channel. Sven-Gustav Häggman Moelg of he lear me-vara chael Sve-Gusav Häggma 2 1. Characerzao of he lear me-vara chael 3 The rasmsso chael (rao pah) of a rao commucao sysem s mos cases a mulpah chael. Whe chages ae place he propagao

More information

Complementary Tree Paired Domination in Graphs

Complementary Tree Paired Domination in Graphs IOSR Joural of Mahemacs (IOSR-JM) e-issn: 2278-5728, p-issn: 239-765X Volume 2, Issue 6 Ver II (Nov - Dec206), PP 26-3 wwwosrjouralsorg Complemeary Tree Pared Domao Graphs A Meeaksh, J Baskar Babujee 2

More information

Delay-Dependent Robust Asymptotically Stable for Linear Time Variant Systems

Delay-Dependent Robust Asymptotically Stable for Linear Time Variant Systems Delay-Depede Robus Asypocally Sable for Lear e Vara Syses D. Behard, Y. Ordoha, S. Sedagha ABSRAC I hs paper, he proble of delay depede robus asypocally sable for ucera lear e-vara syse wh ulple delays

More information

dm dt = 1 V The number of moles in any volume is M = CV, where C = concentration in M/L V = liters. dcv v

dm dt = 1 V The number of moles in any volume is M = CV, where C = concentration in M/L V = liters. dcv v Mg: Pcess Aalyss: Reac ae s defed as whee eac ae elcy lue M les ( ccea) e. dm he ube f les ay lue s M, whee ccea M/L les. he he eac ae beces f a hgeeus eac, ( ) d Usually s csa aqueus eeal pcesses eac,

More information

Redundancy System Fault Sampling Under Imperfect Maintenance

Redundancy System Fault Sampling Under Imperfect Maintenance A publcao of CHEMICAL EGIEERIG TRASACTIOS VOL. 33, 03 Gues Edors: Erco Zo, Pero Barald Copyrgh 03, AIDIC Servz S.r.l., ISB 978-88-95608-4-; ISS 974-979 The Iala Assocao of Chemcal Egeerg Ole a: www.adc./ce

More information

Interval Regression Analysis with Reduced Support Vector Machine

Interval Regression Analysis with Reduced Support Vector Machine Ieraoal DSI / Asa ad Pacfc DSI 007 Full Paper (July, 007) Ierval Regresso Aalyss wh Reduced Suppor Vecor Mache Cha-Hu Huag,), Ha-Yg ao ) ) Isue of Iforao Maagee, Naoal Chao Tug Uversy (leohkko@yahoo.co.w)

More information

X-Ray Notes, Part III

X-Ray Notes, Part III oll 6 X-y oe 3: Pe X-Ry oe, P III oe Deeo Coe oupu o x-y ye h look lke h: We efe ue of que lhly ffee efo h ue y ovk: Co: C ΔS S Sl o oe Ro: SR S Co o oe Ro: CR ΔS C SR Pevouly, we ee he SR fo ye hv pxel

More information

Real-time Classification of Large Data Sets using Binary Knapsack

Real-time Classification of Large Data Sets using Binary Knapsack Real-me Classfcao of Large Daa Ses usg Bary Kapsack Reao Bru bru@ds.uroma. Uversy of Roma La Sapeza AIRO 004-35h ANNUAL CONFERENCE OF THE ITALIAN OPERATIONS RESEARCH Sepember 7-0, 004, Lecce, Ialy Oule

More information

14. Poisson Processes

14. Poisson Processes 4. Posso Processes I Lecure 4 we roduced Posso arrvals as he lmg behavor of Bomal radom varables. Refer o Posso approxmao of Bomal radom varables. From he dscusso here see 4-6-4-8 Lecure 4 " arrvals occur

More information

GENERATOR PARAMETER IDENTIFICATION USING AN EXTENDED PARTICLE SWARM OPTIMISATION METHOD

GENERATOR PARAMETER IDENTIFICATION USING AN EXTENDED PARTICLE SWARM OPTIMISATION METHOD Corol 004, Uversy of Bah, UK, Sepeber 004 ID- GNRAOR PARAMR IDNIFICAION USING AN XNDD PARICL SWARM OPIMISAION MHOD J. S. Hu, C. X. Guo, Y. J. Cao* (College of lecrcal geerg, Zheag Uversy, Hagzhou 3007,

More information

[ m] x = 0.25cos 20 t sin 20 t m

[ m] x = 0.25cos 20 t sin 20 t m . x.si ( 5 s [ ] CHAPER OSCILLAIONS x ax (.( ( 5 6. s s ( ( ( xax. 5.7 s s. x.si [] x. cos s Whe, x a x.5. s 5s.6 s x. x( x cos + si a f ( ( [ ] x.5cos +.59si. ( ( cos α β cosαcos β + siαsi β x Acos φ

More information

Introduction to Numerical Analysis. In this lesson you will be taken through a pair of techniques that will be used to solve the equations of.

Introduction to Numerical Analysis. In this lesson you will be taken through a pair of techniques that will be used to solve the equations of. Inroducion o Nuerical Analysis oion In his lesson you will be aen hrough a pair of echniques ha will be used o solve he equaions of and v dx d a F d for siuaions in which F is well nown, and he iniial

More information

14.02 Principles of Macroeconomics Fall 2005

14.02 Principles of Macroeconomics Fall 2005 14.02 Priciples of Macroecoomics Fall 2005 Quiz 2 Tuesday, November 8, 2005 7:30 PM 9 PM Please, aswer he followig quesios. Wrie your aswers direcly o he quiz. You ca achieve a oal of 100 pois. There are

More information

Anouncements. Conjugate Gradients. Steepest Descent. Outline. Steepest Descent. Steepest Descent

Anouncements. Conjugate Gradients. Steepest Descent. Outline. Steepest Descent. Steepest Descent oucms Couga Gas Mchal Kazha (6.657) Ifomao abou h Sma (6.757) hav b pos ol: hp://www.cs.hu.u/~msha Tch Spcs: o M o Tusay afoo. o Two paps scuss ach w. o Vos fo w s caa paps u by Thusay vg. Oul Rvw of Sps

More information

Efficient Estimators for Population Variance using Auxiliary Information

Efficient Estimators for Population Variance using Auxiliary Information Global Joural of Mahemacal cece: Theor ad Praccal. IN 97-3 Volume 3, Number (), pp. 39-37 Ieraoal Reearch Publcao Houe hp://www.rphoue.com Effce Emaor for Populao Varace ug Aular Iformao ubhah Kumar Yadav

More information

Chapter 8. Simple Linear Regression

Chapter 8. Simple Linear Regression Chaper 8. Smple Lear Regresso Regresso aalyss: regresso aalyss s a sascal mehodology o esmae he relaoshp of a respose varable o a se of predcor varable. whe here s jus oe predcor varable, we wll use smple

More information

Global Financial Management

Global Financial Management - - Global Facal Maaeme Dscou ad Prese alue Techques opyrh 999 by Ers Mau. All rhs reserved. No par of hs lecure may be reproduced whou he permsso of he auhor.. Overvew Las Revso: Sepember 9, 999 I hs

More information

Outline. Queuing Theory Framework. Delay Models. Fundamentals of Computer Networking: Introduction to Queuing Theory. Delay Models.

Outline. Queuing Theory Framework. Delay Models. Fundamentals of Computer Networking: Introduction to Queuing Theory. Delay Models. Oule Fudaeals of Couer Neworg: Iroduco o ueug Theory eadg: Texboo chaer 3. Guevara Noubr CSG5, lecure 3 Delay Models Lle s Theore The M/M/ queug syse The M/G/ queug syse F3, CSG5 Fudaeals of Couer Neworg

More information

Thus the force is proportional but opposite to the displacement away from equilibrium.

Thus the force is proportional but opposite to the displacement away from equilibrium. Chaper 3 : Siple Haronic Moion Hooe s law saes ha he force (F) eered by an ideal spring is proporional o is elongaion l F= l where is he spring consan. Consider a ass hanging on a he spring. In equilibriu

More information

A Hidden Markov Model-based Forecasting Model for Fuzzy Time Series

A Hidden Markov Model-based Forecasting Model for Fuzzy Time Series A Hdde Markov Model-based Forecasg Model for e Seres SHENG-UN LI YI-CHUNG CHENG Isue of Iforao Maagee Naoal Cheg Kug Uversy awa R.O.C. Depare of Idusral ad Iforao Maagee Naoal Cheg Kug Uversy awa R.O.C.

More information

Some probability inequalities for multivariate gamma and normal distributions. Abstract

Some probability inequalities for multivariate gamma and normal distributions. Abstract -- Soe probably equales for ulvarae gaa ad oral dsrbuos Thoas oye Uversy of appled sceces Bge, Berlsrasse 9, D-554 Bge, Geray, e-al: hoas.roye@-ole.de Absrac The Gaussa correlao equaly for ulvarae zero-ea

More information

P a g e 3 6 of R e p o r t P B 4 / 0 9

P a g e 3 6 of R e p o r t P B 4 / 0 9 P a g e 3 6 of R e p o r t P B 4 / 0 9 p r o t e c t h um a n h e a l t h a n d p r o p e r t y fr om t h e d a n g e rs i n h e r e n t i n m i n i n g o p e r a t i o n s s u c h a s a q u a r r y. J

More information

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER 2

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER 2 Joh Rley Novembe ANSWERS O ODD NUMBERED EXERCISES IN CHAPER Seo Eese -: asvy (a) Se y ad y z follows fom asvy ha z Ehe z o z We suppose he lae ad seek a oado he z Se y follows by asvy ha z y Bu hs oads

More information

Research Article Application of Cold Chain Logistics Safety Reliability in Fresh Food Distribution Optimization

Research Article Application of Cold Chain Logistics Safety Reliability in Fresh Food Distribution Optimization Advace Joural of Food Scece ad Tecology 5(3: 356-36, 13 DOI:1.196/afs.5.37 ISSN: 4-4868; e-issn: 4-4876 13 Mawell Scefc Publcao Corp. Subed: Ocober 31, 1 Acceped: Deceber, 1 Publsed: Marc 15, 13 Researc

More information

Linear Minimum Variance Unbiased Estimation of Individual and Population slopes in the presence of Informative Right Censoring

Linear Minimum Variance Unbiased Estimation of Individual and Population slopes in the presence of Informative Right Censoring Ieraoal Joural of Scefc ad Research Pulcaos Volue 4 Issue Ocoer 4 ISSN 5-353 Lear Mu Varace Uased Esao of Idvdual ad Populao slopes he presece of Iforave Rgh Cesorg VswaahaN * RavaaR ** * Depare of Sascs

More information

Fresnel Equations cont.

Fresnel Equations cont. Lecure 12 Chaper 4 Fresel quaos co. Toal eral refleco ad evaesce waves Opcal properes of meals Laer: Famlar aspecs of he eraco of lgh ad maer Fresel quaos r 2 Usg Sell s law, we ca re-wre: r s s r a a

More information

Cyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles

Cyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles Ope Joural of Dsree Mahemas 2017 7 200-217 hp://wwwsrporg/joural/ojdm ISSN Ole: 2161-7643 ISSN Pr: 2161-7635 Cylally Ierval Toal Colorgs of Cyles Mddle Graphs of Cyles Yogqag Zhao 1 Shju Su 2 1 Shool of

More information

Queuing Theory: Memory Buffer Limits on Superscalar Processing

Queuing Theory: Memory Buffer Limits on Superscalar Processing Cle/ Model of I/O Queug Theory: Memory Buffer Lms o Superscalar Processg Cle reques respose Devce Fas CPU s cle for slower I/O servces Buffer sores cle requess ad s a slower server respose rae Laecy Tme

More information

KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne.

KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne. KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS by Peter J. Wlcoxe Ipact Research Cetre, Uversty of Melboure Aprl 1989 Ths paper descrbes a ethod that ca be used to resolve cossteces

More information

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting Appled Mahemacs 4 5 466-477 Publshed Ole February 4 (hp//wwwscrporg/oural/am hp//dxdoorg/436/am45346 The Mea Resdual Lfeme of ( + -ou-of- Sysems Dscree Seg Maryam Torab Sahboom Deparme of Sascs Scece ad

More information