Society for Business and Management Dynamics. Business Management Dynamics Vol.1, No.4, Oct 2011, pp.63-72
|
|
- Bennett Holmes
- 6 years ago
- Views:
Transcription
1 Busess Maagemet Dyamcs Vol, No4, Oct 20, pp63-72 Solvg Sgle Mache Schedulg Problem Wth Commo Due Date Nord Haj Mohamad ad Fatmah Sad 2 Abstract he paper addresses a -job sgle mache schedulg problem wth commo due date to mmze the sum of total vetory ad pealty costs arless ad tardess are cosdered harmful to proftablty arless causes vetory carryg costs ad possble loss of product qualty, whle tardess causes loss of customer goodwll ad damage reputato as well as delay of paymet hus the schedulg problem of mmzg the total sum of earless ad tardess wth a commo due date o a sgle mache s mportat ad a compettve task the delvery of goods producto plats, ad t s kow to be NP-hard A smple, easy to uderstad/mplemet heurstc algorthm whch ca be performed maually for small problems ad computatoally feasble for large problems s preseted ad llustrated wth umercal example Key words: obs schedulg problem, due date, earless, tardess Avalable ole wwwbmdyamcscom ISSN: INRODUCION I today s complex dustral world, may busess problems that eed to be solved or optmzed are schedulg problems A maufacturg frm producg multple products, each requrg may dfferet processes ad mache facltes for completo, must fd a way to successfully maage resources the most effcet way possble he decso maker s faced wth a problem of desgg a producto or job schedule that promotes o tme delvery ad mmzes objectves such as the flow tme or completo tme of a product Out of these terests emerged a area of study kow as the schedulg problem A frequetly occurrg schedulg problem s oe of processg a gve umber of jobs or tasks o a specfed umber of maches or facltes hs class of problem also referred to by may as dspatchg or sequecg s categorzed as NP-hard he desre to process the jobs a specfc order to acheve some objectve fucto s what creates a problem that remas largely usolved he actual stuatos that gve rse to schedulg problems are wde ad vared hs cludes, for example, sgle mache schedulg problem, multple mache schedulg problem ad mapower schedulg problem A geeral jobs m maches schedulg problem ca be stated as follows Gve jobs to be processed o m maches the same techologcal order, the processg tme of job o mache j beg t j ( =, 2,, ; j =, 2,, m), t s desred to fd the order (schedule or sequece) whch these jobs should be processed o each of the m maches so as to optmze (mmze or maxmze) a well defed measure of some objectves (such as producto cost, umber of late jobs, etc) hs problem, geeral, gves (!) m possble schedules ve for problems as small as = m = 5, the umber of possble schedules s so large that a drect eumerato s ecoomcally mpossble (For = 5, m = 3, we have,728,000 possble schedules, whereas for = m = 5, we have x 0 0 possble schedules) However, for a smplfed verso where t s assumed that the order (or sequece) whch these jobs are processed o each mache s the same, the umber of feasble schedules reduces to! Sgle mache schedulg problems bear complex computatos ad the aalyss of such problems s mportat for a better uderstadg of the problem Amog sgle mache problems, those related to earless ad tardess s more mportat Completg jobs or tasks earler tha ther due dates should be dscouraged as much as completg jobs or tasks later tha ther due dates I real world, sce a customer expects to receve the product o a specfc date, schedulg based o the due date s also a mportat task the producto plag arless leads to vetory ad mateace carryg costs whle tardess leads to customer s dssatsfacto ad losg goodwll ad reputato Isttute of Mathematcal Sceces, Faculty of Scece, Uversty of Malaya, Lembah Pata, Kuala Lumpur, Malaysaelephoe : Fax: mal : ordhm@umedumy 2 Faculty of coomcs ad Admstrato, Uversty of Malaya, Kuala Lumpur, Malaysa fatmahs@umedumy
2 Busess Maagemet Dyamcs Vol, No4, Oct 20, pp63-72 LIRAUR RVIW ob schedulg or sequecg has a wde varety of applcatos, from desgg the product flow ad order a maufacturg faclty to modelg queues servce dustres It s deed a useful subject that s stll beg actvely researched Most researchers focused o specal cases whch stuatoal restrctos are mposed Moore (968) desged a algorthm that sequeces the jobs for the sgle-mache problem to mmze the umber of tardess jobs, whereas Gupta (969) proposed a geeral algorthm for the x m flowshop schedulg problem Brucker et al (999) showed that complex schedulg problems lke geeral shop problem ca be reduced to sgle-mache problem wth postve ad egatve tme-lags betwee jobs, solvable by a brach ad boud algorthm Schedulg multmache problems cosderg both earless ad tardess pealtes was surveyed by Lauff ad Werer (2004) whch corporated the just--tme (I) producto phlosophy A comprehesve revew of some earless ad tardess models ca be foud Baker ad Scudder (990) where seve dfferet objectve fuctos assocated wth the mmzato of varatos of job completo tmes from ther respectve due dates were detfed, cludg cases of olear pealtes avakkol-moghaddam et al (2005) cosder the commo due date problem wth the objectve of mmzg the sum of maxmum earless ad tardess costs usg a dle sert algorthm ad llustrated the effcecy of the proposed algorthm to 020 problems wth dfferet job szes A lear programmg approach to solve a fuzzy sgle mache schedulg problem s proposed by Kamalabad et al (2007) hs approach s applcable to just-tme systems, whch may frms face the eed to complete jobs as close as possble to ther due dates A recet study by Gupta (20) proposes a heurstc algorthm for small system wth dstct due dates uder fuzzy evromet I addto, the teger programmg method for solvg problems wth small sze of jobs was rased by Bskup ad Feldma (200) Roco ad Kawamura (200) proposed a brach-ad-boud algorthm for solvg sgle mache earless ad tardess schedulg problem hey troduced lower bouds ad prug that explot propertes of the problem Feldma ad Bskup (2003) studed sgle mache schedulg problems usg three meta-heurstcs approaches (evolutoary search, smulated aealg ad threshold acceptg) he applcato of these meta-heurstcs was demostrated by solvg 40 bechmark problems wth up to 000 jobs Several meta-heurstc algorthms for solvg sgle mache schedulg problems were aalyzed by Abtah ad aghavfard (2008) I ths paper, we cosder a smplfed verso of schedulg jobs wth commo due date o a sgle mache wth the objectve of mmzg the sum of total earless ad tardess pealtes Commo due date problems are relevat may real-lfe stuatos; for stace, f a customer orders a budle of goods whch has to be delvered at a specfed tme, f a frm has stalled a weekly bulk delvery to the wholesaler or a assembly evromet whch the compoets of a product should all be ready at the same tme to avod stagg delays (Yag ad Hsu, 200) Amog the poeers studyg commo due date problems were Kaet (98) ad Pawalker et al (982) All the jobs cosdered have a commo due date he objectve was to fd a optmal commo due date ad a optmal schedule whch mmzes the total earless, tardess ad due date costs Sce the, the problems have bee studed uder dfferet evromets Cheg et al (2004) studed a sgle mache due date assgmet schedulg wth deteroratg jobs hey provded some propertes ad a algorthm to solve the problem O(log) tme Later, Kuo ad Yag (2008) gave a cocse aalyss of the problem ad provded a smpler algorthm for the problem Comprehesve survey o ths topc s provded by Cheg ad Gupta (989), Baker ad Scudder (990) ad Gordo et al (2002) SAMN OF H PROBLM Oe of the most mportat objectves schedulg problem wth due dates s to mmze the sum of the earless ad tardess of jobs hs coforms to the I system (Ow ad Morto, 989) arless ad tardess cause pealtes creasg vetory cost ad losg customers respectvely arly jobs (completed before due dates) te up captal, crease the vetory level, take up scarce floor space, cause losses owg to deterorato, ad geerally dcate sub-optmal resource allocato ad utlzato, whereas late or tardy jobs (completed after due dates) result pealtes, such as loss of customers goodwll ad damaged reputato
3 Busess Maagemet Dyamcs Vol, No4, Oct 20, pp63-72 here are may real lfe problems that resemble sgle mache schedulg problem A typcal example s the laudry servce where orders (of dfferet szes) from customers arrve early morg, ad due dates are determed by pck-up tmes, ad pck-ups are made by customers If a due date (pck-up) s mssed, a specal delvery servce eeds to be bought by the laudry operator, the cost of whch s depedet of the tardess Other examples clude a sgle cotractor schedulg multple buldg/housg projects wth due date completo tmes ad producto of maufacturg goods wth dfferet processg tmes to meet delvery s deadles he geeral sgle-mache problem wth commo due date ca thus be formally stated as follows Gve jobs to be processed o a sgle mache, the processg tme of job beg t, =, 2,, It s assumed that all jobs are ready for processg at tme zero ad have the same commo due date (deadle) Also, o more tha oe job ca be processed at ay pot of tme ach job requres exactly oe operato ad ts processg tme p s kow If a job s completed before the due date, ts earless s gve by = d c where c s the completo tme of job Coversely, f a job s completed after the desred date, ts tardess s gve by = c d ach job has ts ow ut earless pealty α ad ut tardess pealty β he problem s to fd the order (schedule) whch these jobs should be processed so as to mmze the sum of total earless ad tardess costs It s also assumed that the due date s less tha the total processg tmes, a problem ofte referred to as restrcted due date problem A due date s called urestrctve f ts optmal value has to be calculated or f t s gve value does ot fluece the optmal schedule (Roco ad Kawamura, 200) If the gve due date s greater tha or equal to the sum of processg tmes of all jobs avalable, the problem s urestrctve (Feldma ad Bskup, 2003) Hall ad Poser (99) showed that ths schedulg problem s NP-hard eve wth α = β Ghosh ad Wells (994) addressed the urestrctve case whch α = β = for all jobs that ca be solved by a polyomal algorthm of O(log) complexty he restrctve due date problem s NP-hard eve wth α = β = (Hall et al 99) Due to ts complexty, may authors addressed ths problem usg heurstc ad metaheurstc approaches (Feldma ad Bskup, 2003; Ho et al 2005; Lao ad Cheg, 2007) he problem ca be mathematcally formulated as mmze : F = max( d c,0) max( c d,0)} () where c, =,2,,, s the completo tme of job, d s the commo due date, ad α ad β are the ut pealty costs assocated wth earless ad tardess respectvely o llustrate the problem, we cosder a smple 2-job ad 3-job examples wth α = α ad β = β, for all =,2,, xample (case = 2) Let ad 2 be two jobs wth processg tmes 3 ad 5 days respectvely Further, let the commo due date, d = 6 I other words, both jobs must be delvered o day 6 he problem ca be represeted as able able (case = 2, wth due date, d = 6) 2 t 3 5 here exst two schedules: S =, 2} ad S 2 = 2, } Schedule S mples job s processed frst, followed by job 2, whereas schedule S 2 mples the opposte, job 2 s processed frst, followed by job
4 Busess Maagemet Dyamcs Vol, No4, Oct 20, pp63-72 For schedule S =, 2}, we have 2 t 3 5 c d 3 6 = 3, 8 6 = 2 herefore, total earless ad tardess costs, F = 3α + 2β For schedule S 2 = 2, }, we have 2 t 5 3 c d 5 6 =, 8 6 = 2 herefore, total earless ad tardess costs, F 2 = α + 2β hus, F* = mmum (F, F 2) = F 2 = α + 2β for all α ad β he optmal decso s to schedule job 2 frst (whch s completed oe day before the due date, gvg a earless cost of α) followed by job (wth a delay of two days ad tardess cost of 2β) xample 2 (case = 3) Let, 2 ad 3 be three jobs wth processg tmes 3, 4 ad 6 days respectvely Further, let the commo due date, d = 9 I other words, all jobs must be delvered o day 9 he problem ca be represeted as able 2 able 2 (case = 3, wth due date, d = 9) 2 3 t here exst sx schedules: S =, 2, 3}, S 2 =, 3, 2}, S 3 = 2,, 3}, S 4 = 2, 3, }, S 5 = 3,, 2}, S 6 = 3, 2, } Schedule S mples job s processed frst, followed by job 2, ad fally job 3, whereas schedule S 2 mples job s processed frst, followed by job 3, ad fally job 2 All other schedules should be terpreted accordgly For schedule S =, 2, 3}, we have 2 3 t c d 3 9 = 6, 7 9 = 2, 3 9 = 4 herefore, total earless ad tardess costs, F = 8α + 4β For schedule S 2 =, 3, 2}, we have 3 2 t c d 3 9 = 6, 9 9 = 0, 3 9 = 4 herefore, total earless ad tardess costs, F 2 = 6α + 4β
5 Busess Maagemet Dyamcs Vol, No4, Oct 20, pp63-72 For schedule S 3 = 2,, 3}, we have 2 3 t c d 4 9 = 5, 7 9 = 2, 3 9 = 4 herefore, total earless ad tardess costs, F 3 = 7α + 4β For schedule S 4 = 2, 3, }, we have 2 3 t c d 4 9 = 5, 0 9 =, 3 9 = 4 herefore, total earless ad tardess costs, F 4 = 5α + 5β For schedule S 5 = 3,, 2}, we have 3 2 t c d 6 9 = 3, 9 9 = 0, 3 9 = 4 herefore, total earless ad tardess costs, F 5 = 3α + 4β For schedule S 6 = 3, 2, }, we have 3 2 t c d 6 9 = 3, 0 9 =, 3 9 = 4 herefore, total earless ad tardess costs, F 6 = 3α + 5β hus, F* = mmum (F, F 2, F 3, F 4, F 5, F 6) = F 5 = 3α + 4β for all α ad β he optmal decso s to schedule job 3 frst (whch s completed three days before the due date, gvg a earless cost of 3α) followed by job (whch s completed o tme), ad lastly by job 2 (wth a delay of four days ad tardess cost of 4β) From both examples, we observe that the optmal schedule appears to be depedet of the umercal values of α ad β hus wthout loss of geeralty ad for ease of computato, we ca cosder a case wth α = β = there are! schedules for jobs For 0 jobs, there are 0! = 3,628,800 possble schedules whch are ot maually feasble hus a algorthm capable of reducg the umber of eumeratos s much desred A HURISIC ALGORIHM Below, we preset a heurstc algorthm for solvg -job sgle mache schedulg problem wth commo due date he algorthm s smple to uderstad ad easy to mplemet Problem Statemet: Gve jobs to be processed o a sgle mache, the processg tme of job beg t, =, 2,, It s assumed that all jobs are ready for processg at tme zero ad have the same commo due date (deadle), D he problem s to fd the order (schedule) whch these
6 Busess Maagemet Dyamcs Vol, No4, Oct 20, pp63-72 jobs should be processed so as to mmze the sum of total earless ad tardess costs he commo due date, D s assumed to be less tha the total processg tme Step 0: Sort ad umber the jobs o-creasg order of processg tme t, ( =,2,, ) such that t t2 t t t t I geeral, we ca represet the jobs tabular form, t t t 2 t - t t + t Compute t total processg tme, 0 D, ad D Itroduce two empty sets, 0, S 0 ad S 0 Step : Cosder job wth processg tme t max( t,,2,, ) Set 0, ad 0 If, set S S 0 } ad S S0 If, set S S 0 } ad S S0 Step : Cosder job wth processg tme t, ( < < ) If prevous job, S ad t If prevous job, S t ad If, set S S } ad S S If, set S S } ad S S Iterato termates whe all jobs have bee assged to ether Schedulg decso Check (ad sort f ecessary) so that: S = jobs o-creasg order of processg tmes}, S = jobs o-decreasg order of processg tmes} S or he optmal schedule, S* = S, S } I other words, jobs are schedule accordg to ther sequece S, followed by S Note that the procedure oly volves eumeratos (teratos) as compared to! possble schedules We llustrate the above algorthm by cosderg a 0-job schedulg problem Illustratve example Cosder a 0-job schedulg problem gve by the table below (sorted o-creasg order of processg tmes), wth commo due date, D = 45 S Step 0: Compute t
7 total processg tme, t 02, 0 D , ad D 45 Itroduce two empty sets, 0 S 0 ad S 0 Step : Cosder job wth processg tme t 20 Set 57, ad S 0 0 S } = } ad S S0 = } Step 2: Cosder job 2 wth processg tme t 2 = 8 Prevous job, S compute t 37 ad S2 S 2} 2} ad S 2 S } Step 3: Cosder job 3 wth processg tme t 3 = 5 Prevous job, S compute 37, ad t S S }, } ad S S } Step 4: Cosder job 4 wth processg tme t 4 = 0 Prevous job, S compute t ad 27 S S }, } ad S S, } Step 5: Cosder job 5 wth processg tme t 5 = 9 Prevous job, S compute ad t S S },, } ad S S, } Busess Maagemet Dyamcs Vol, No4, Oct 20, pp Step 6: Cosder job 6 wth processg tme t 6 = 8 Prevous job, S compute t ad 7 S S },, } ad S S,, } Step 7: Cosder job 7 wth processg tme t 7 = 7 Prevous job, S compute ad t S S },,, } ad S S,, } Step 8: Cosder job 8 wth processg tme t 8 = 6 Prevous job, 7 S7 compute t7 ad
8 8 8 S8 S7 8} 2, 4, 6, 8}, ad S S,,, } Step 9: Cosder job 9 wth processg tme t 9 = 5 Prevous job, 8 S8 compute ad t S9 S8 9}, 3, 5, 7, 9}, ad S S,,, } Step 0: Cosder job 0 wth processg tme t 0 = 4 Prevous job, 9 S9 compute t9 ad S0 S9 0} 2, 4, 6, 8, 0}, ad S S,,,, } d of terato sce all jobs have bee assged to ether S 0 or S 0 Busess Maagemet Dyamcs Vol, No4, Oct 20, pp63-72 Observe that jobs S,,,, } are o-creasg order (as requred), but jobs , 3, 5, 7, 9} 0 9, 7, 5, 3, } S are ot the requred o-decreasg order hus, rewrte S whch s ow the requred o-decreasg order he optmal schedule s, therefore, gve by whch ca be tabulated as S* = S, S },,,, ;,,, }, , t c c D gvg, F* c D 92 (pealty days) For comparatve purposes, we compute a few selected schedules such as S,,,,,,,,, }, t c c D gvg F c D 320 (pealty days),
9 Busess Maagemet Dyamcs Vol, No4, Oct 20, pp63-72 S S, S },,,,,,,,, }, t c c D gvg F2 c D 342 (pealty days), S,,,,,,,,, }, t c c D gvg F3 c D 268 (pealty days) As ca be see, all the above schedules gve the sum of total earless ad tardess costs more tha 92 (pealty days) CONCLUSION I ths paper we have preseted a smple heurstc algorthm outlg the procedure for mmzg the sum of total earless ad tardess costs a -job sgle mache schedulg problem wth commo due date he algorthm volves oly teratos ad s computatoally ecoomcal for large problems ad maually feasble for small problems We llustrate for a 0-job problem However, t s assumed that the ut earless ad tardess costs are costat for all jobs What s preseted here s just the tp of a large ceberg Future research may focus o studyg smlar models mult-mache evromet ad try to detfy easly solvable specal cases he world of job-mache schedulg s almost edless For the last ffty years, more tha 200 papers o varous aspects of the problem have bee publshed the operatoal research ad maagemet scece lteratures he advet of just--tme system ad developmet supply cha maagemet, teret ad e-commerce has created ew ad complex schedulg problems to the exstg problems that we have just begu to uderstad RFRNCS Abtah, SM, ad aghavfard, M (2008) valuatg meta-heurstc algorthms for solvg restrcted sgle mache schedulg problems: a comparatve aalyss World Appled Sceces oural, 4(): Baker, KR ad Scudder, GD (990) Sequecg wth earless ad tardess pealtes: A revew Operatos Research, 38(): Bskup, D ad Feldma, M (200) Bechmarks for schedulg o a sgle-mache restrctve ad urestrctve commo due dates Computers ad Operatos Research, 28(8): Brucker, P, Hlbg, ad Hurk, (999) A brach ad boud algorthm for a sgle-mache schedulg problem wth postve ad egatve tme-lags Dscrete Appled Mathematcs, 94(): 77 99
10 Busess Maagemet Dyamcs Vol, No4, Oct 20, pp63-72 Cheg, C ad Gupta, MC (989) A survey of schedulg research volvg due-date determato decsos uropea oural of Operatoal Research, 38(2): Cheg, C, Kag, L ad Ng, C (2004) Due-date assgmet ad sgle mache schedulg wth deteroratg jobs oural of the Operatoal Research Socety, 55(2): Feldma, M ad Bskup, D (2003) Sgle-mache schedulg for mmzg earless ad tardess pealtes by meta-heurstc approaches Computers ad Idustral geerg, 44(2): Ghosh, PDB ad Wells, C (994) Solvg a geeralzed model for CON due date assgmet ad sequecg Iteratoal oural of Producto coomcs, 34(2): Gordo, VS, Proth, M ad Chu, C (2002) A survey of the state-of-art of commo due date assgmet ad schedulg research uropea oural of Operatoal Research, 39(): 25 Gupta, ND (969) A geeral algorthm for the x m flowshop schedulg problem he Iteratoal oural of Producto Research, 7: Gupta, S (20) Sgle mache schedulg wth dstct due dates uder fuzzy evromet Iteratoal oural of terprse Computg ad Busess Systems, (2): 9 Hall, NG ad Poser, M (99) arless-tardess schedulg problems, I: weghted devato of completo tmes about a commo due date Operatos Research, 39(5): Hall, NG, Kubak, GW ad Seth, SP (99) arless-tardess schedulg problems, II: weghted devato of completo tmes about a restrctve commo due date Operatos Research, 39(5): Ho, CM, Roco, DP ad Medes, AB (2005) Mmzg earless ad tardess pealtes a sgle-mache problem wth a commo due date uropea oural of Operatoal Research, 60(): Kamalabad, IN, Mrzae, AH ad avad, B (2007) A possblty lear programmg approach to solve a fuzzy sgle mache schedulg problem oural of Idustral ad Systems geerg, (2): 6 29 Kaet, (98) Mmzg the average devato of job completo tmes about a commo due date Naval Research Logstcs Quarterly, 28(4): Kuo, WH ad Yag, DL (2008) A ote o due-date assgmet ad sgle-mache schedulg wth deteroratg jobs oural of the Operatoal Research Socety, 59: Lauff, V ad Werer, F (2004) Schedulg wth commo due date, earless ad tardess pealtes for mult-mache problems: a survey Mathematcal ad Computer Modellg, 40(5): Lao, C ad Cheg, CC (2007) A varable eghborhood search for mmzg sgle mache weghted earless ad tardess wth commo due date Computers ad Idustral geerg, 52(4): Moore, M (968) A job, oe mache sequecg algorthm for mmzg the umber of late jobs Maagemet Scece, 5(): Ow, PS ad Morto, (989)he sgle mache early/tardy problems Maagemet Scece, 35(2): 77 9 Pawalker, S, Smth, M ad Sedma, A (982) Commo due-date assgmet to mmze total pealty for the oe mache schedulg problem Operatos Research, 30(2): Raco, DP ad Kawamura, MS (200) he sgle mache earless ad tardess schedulg problem: lower bouds ad a brach-ad-boud algorthm Computatoal ad Appled Mathematcs, 29(2): avakkol-moghaddam, R, Mosleh, G, Vase, M ad Azaro, A (2005)Optmal schedulg for a sgle mache to mmze the sum of maxmum earless ad tardess cosderg dle sert Appled Mathematcs ad Computato, 67(2): Yag, S ad Hsu, C (200) Sgle-mache schedulg wth due-date assgmet ad agg effect uder a deteroratg mateace actvty cosderato Iteratoal oural of Iformato ad Maagemet Sceces, 2(2):77 95
Solving Constrained Flow-Shop Scheduling. Problems with Three Machines
It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632
More informationMulti Objective Fuzzy Inventory Model with. Demand Dependent Unit Cost and Lead Time. Constraints A Karush Kuhn Tucker Conditions.
It. Joural of Math. Aalyss, Vol. 8, 204, o. 4, 87-93 HIKARI Ltd, www.m-hkar.com http://dx.do.org/0.2988/jma.204.30252 Mult Objectve Fuzzy Ivetory Model wth Demad Depedet Ut Cost ad Lead Tme Costrats A
More informationFunctions of Random Variables
Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,
More informationKeywords Specially structured flow shop scheduling. Rental policy, Processing time, weightage of jobs, Set up, Job block.
Iteratoal Joural of Egeerg Research ad Developmet e-issn: 2278-067X, p-issn: 2278-800X,.jerd.com Volume 3, Issue 5 (August 2012), PP. 72-77 Specally Structured To Stage Flo Shop Schedulg To Mmze the Retal
More informationEstimation of Stress- Strength Reliability model using finite mixture of exponential distributions
Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur
More informationFuzzy Programming Approach for a Multi-objective Single Machine Scheduling Problem with Stochastic Processing Time
Proceedgs of the World Cogress o Egeerg 008 Vol II WCE 008, July - 4, 008, Lodo, U.K. Fuzzy Programmg Approach for a Mult-obectve Sgle Mache Schedulg Problem wth Stochastc Processg Tme Ira Mahdav*, Babak
More informationMixed-Integer Linear Programming Models for Managing Hybrid Flow Shops with Uniform, Non-Identical Multiple Processors
Asa Pacfc Maagemet Revew (2007) 2(2), 95-00 Mxed-Iteger Lear Programmg Models for Maagg Hybrd Flow Shops wth Uform, No-Idetcal Multple Processors Satos D.L. a,* ad Iva Roa b a Departmet of Systems Scece
More informationPTAS for Bin-Packing
CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,
More informationInternational Journal of
Iter. J. Fuzzy Mathematcal Archve Vol. 3, 203, 36-4 ISSN: 2320 3242 (P), 2320 3250 (ole) Publshed o 7 December 203 www.researchmathsc.org Iteratoal Joural of Mult Objectve Fuzzy Ivetory Model Wth Demad
More informationBayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information
Malaysa Joural of Mathematcal Sceces (): 97- (9) Bayes Estmator for Expoetal Dstrbuto wth Exteso of Jeffery Pror Iformato Hadeel Salm Al-Kutub ad Noor Akma Ibrahm Isttute for Mathematcal Research, Uverst
More informationA tighter lower bound on the circuit size of the hardest Boolean functions
Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the
More informationEconometric Methods. Review of Estimation
Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators
More informationFeature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)
CSE 546: Mache Learg Lecture 6 Feature Selecto: Part 2 Istructor: Sham Kakade Greedy Algorthms (cotued from the last lecture) There are varety of greedy algorthms ad umerous amg covetos for these algorthms.
More informationLINEARLY CONSTRAINED MINIMIZATION BY USING NEWTON S METHOD
Jural Karya Asl Loreka Ahl Matematk Vol 8 o 205 Page 084-088 Jural Karya Asl Loreka Ahl Matematk LIEARLY COSTRAIED MIIMIZATIO BY USIG EWTO S METHOD Yosza B Dasrl, a Ismal B Moh 2 Faculty Electrocs a Computer
More informationUnimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods
Malaysa Umodalty Joural Tests of Mathematcal for Global Optmzato Sceces (): of 05 Sgle - 5 Varable (007) Fuctos Usg Statstcal Methods Umodalty Tests for Global Optmzato of Sgle Varable Fuctos Usg Statstcal
More informationSolving Scheduling Deteriorating Jobs with Rate Modifying Activity. Yücel Yılmaz Őztűrkoğlu
Solvg Schedulg Deteroratg Jobs wth Rate Modfyg Actvty by Yücel Yılmaz Őztűrkoğlu A dssertato submtted to the Graduate Faculty of Aubur Uversty partal fulfllmet of the requremets for the Degree of Doctor
More informationA New Family of Transformations for Lifetime Data
Proceedgs of the World Cogress o Egeerg 4 Vol I, WCE 4, July - 4, 4, Lodo, U.K. A New Famly of Trasformatos for Lfetme Data Lakhaa Watthaacheewakul Abstract A famly of trasformatos s the oe of several
More information7.0 Equality Contraints: Lagrange Multipliers
Systes Optzato 7.0 Equalty Cotrats: Lagrage Multplers Cosder the zato of a o-lear fucto subject to equalty costrats: g f() R ( ) 0 ( ) (7.) where the g ( ) are possbly also olear fuctos, ad < otherwse
More informationAnalysis of Lagrange Interpolation Formula
P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal
More informationQ-analogue of a Linear Transformation Preserving Log-concavity
Iteratoal Joural of Algebra, Vol. 1, 2007, o. 2, 87-94 Q-aalogue of a Lear Trasformato Preservg Log-cocavty Daozhog Luo Departmet of Mathematcs, Huaqao Uversty Quazhou, Fua 362021, P. R. Cha ldzblue@163.com
More informationLikewise, properties of the optimal policy for equipment replacement & maintenance problems can be used to reduce the computation.
Whe solvg a vetory repleshmet problem usg a MDP model, kowg that the optmal polcy s of the form (s,s) ca reduce the computatoal burde. That s, f t s optmal to replesh the vetory whe the vetory level s,
More informationSimulation Output Analysis
Smulato Output Aalyss Summary Examples Parameter Estmato Sample Mea ad Varace Pot ad Iterval Estmato ermatg ad o-ermatg Smulato Mea Square Errors Example: Sgle Server Queueg System x(t) S 4 S 4 S 3 S 5
More informationCHAPTER 4 RADICAL EXPRESSIONS
6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube
More informationMA/CSSE 473 Day 27. Dynamic programming
MA/CSSE 473 Day 7 Dyamc Programmg Bomal Coeffcets Warshall's algorthm (Optmal BSTs) Studet questos? Dyamc programmg Used for problems wth recursve solutos ad overlappg subproblems Typcally, we save (memoze)
More informationIdeal multigrades with trigonometric coefficients
Ideal multgrades wth trgoometrc coeffcets Zarathustra Brady December 13, 010 1 The problem A (, k) multgrade s defed as a par of dstct sets of tegers such that (a 1,..., a ; b 1,..., b ) a j = =1 for all
More informationLecture 16: Backpropogation Algorithm Neural Networks with smooth activation functions
CO-511: Learg Theory prg 2017 Lecturer: Ro Lv Lecture 16: Bacpropogato Algorthm Dsclamer: These otes have ot bee subected to the usual scruty reserved for formal publcatos. They may be dstrbuted outsde
More informationInvestigating Cellular Automata
Researcher: Taylor Dupuy Advsor: Aaro Wootto Semester: Fall 4 Ivestgatg Cellular Automata A Overvew of Cellular Automata: Cellular Automata are smple computer programs that geerate rows of black ad whte
More informationCommon due-window assignment and scheduling on a single machine with past-sequence-dependent set up times and a maintenance activity
Matheatca etera, Vol. 4, 04, o., 65-73 oo due-wdow assget ad schedulg o a sgle ache wth past-sequece-depedet set up tes ad a ateace actvty Q heg ollege of Sceces,East ha Isttute of Techology, Nachag,33003,ha
More informationArithmetic Mean and Geometric Mean
Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,
More information. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1)
Chapter 7 Fuctos o Bouded Varato. Subject: Real Aalyss Level: M.Sc. Source: Syed Gul Shah (Charma, Departmet o Mathematcs, US Sargodha Collected & Composed by: Atq ur Rehma (atq@mathcty.org, http://www.mathcty.org
More informationCAREER: Scheduling of Large-Scale Systems: Probabilistic Analysis and Practical Algorithms for Due Date Quotation
NSF grat umber: DMI-0092854 NSF Program Name: Operatos Research. CAREER: Schedulg of Large-Scale Systems: Probablstc Aalyss ad Practcal Algorthms for Due Date Quotato Phlp Kamsky Uversty of Calfora, Berkeley
More informationDescriptive Statistics
Page Techcal Math II Descrptve Statstcs Descrptve Statstcs Descrptve statstcs s the body of methods used to represet ad summarze sets of data. A descrpto of how a set of measuremets (for eample, people
More informationMultiple Choice Test. Chapter Adequacy of Models for Regression
Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to
More informationAn Optimal Switching Model Considered the Risks of Production, Quality and Due Data for Limited-Cycle with Multiple Periods
A Optmal Swtchg Model Cosdered the Rss of Producto, Qualty ad Due Data for Lmted-Cycle wth Multple Perods Jg Su 1 1 Departmet of Cvl Egeerg ad Systems Maagemet, Nagoya Isttute of Techology, Goso-cho, Showa-u,
More informationSummary of the lecture in Biostatistics
Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the
More informationMinimizing Total Completion Time in a Flow-shop Scheduling Problems with a Single Server
Joural of Aled Mathematcs & Boformatcs vol. o.3 0 33-38 SSN: 79-660 (rt) 79-6939 (ole) Sceress Ltd 0 Mmzg Total omleto Tme a Flow-sho Schedulg Problems wth a Sgle Server Sh lg ad heg xue-guag Abstract
More informationMinimizing Makespan and Total Completion Time Criteria on a Single Machine with Release Dates
Joural of Emergg Treds Egeerg ad Appled Sceces (JETEAS) (): 00-08 Joural Scholarlk of Emergg Research Treds Isttute Egeerg Jourals, ad 200 Appled Sceces (JETEAS) (): 00-08 jeteas.scholarlkresearch.org
More informationMEASURES OF DISPERSION
MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda
More informationComparing Different Estimators of three Parameters for Transmuted Weibull Distribution
Global Joural of Pure ad Appled Mathematcs. ISSN 0973-768 Volume 3, Number 9 (207), pp. 55-528 Research Ida Publcatos http://www.rpublcato.com Comparg Dfferet Estmators of three Parameters for Trasmuted
More informationChapter 14 Logistic Regression Models
Chapter 4 Logstc Regresso Models I the lear regresso model X β + ε, there are two types of varables explaatory varables X, X,, X k ad study varable y These varables ca be measured o a cotuous scale as
More informationA New Method for Decision Making Based on Soft Matrix Theory
Joural of Scetfc esearch & eports 3(5): 0-7, 04; rtcle o. JS.04.5.00 SCIENCEDOMIN teratoal www.scecedoma.org New Method for Decso Mag Based o Soft Matrx Theory Zhmg Zhag * College of Mathematcs ad Computer
More informationCS286.2 Lecture 4: Dinur s Proof of the PCP Theorem
CS86. Lecture 4: Dur s Proof of the PCP Theorem Scrbe: Thom Bohdaowcz Prevously, we have prove a weak verso of the PCP theorem: NP PCP 1,1/ (r = poly, q = O(1)). Wth ths result we have the desred costat
More information1 Onto functions and bijections Applications to Counting
1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of
More informationLecture 1. (Part II) The number of ways of partitioning n distinct objects into k distinct groups containing n 1,
Lecture (Part II) Materals Covered Ths Lecture: Chapter 2 (2.6 --- 2.0) The umber of ways of parttog dstct obects to dstct groups cotag, 2,, obects, respectvely, where each obect appears exactly oe group
More informationLecture 2 - What are component and system reliability and how it can be improved?
Lecture 2 - What are compoet ad system relablty ad how t ca be mproved? Relablty s a measure of the qualty of the product over the log ru. The cocept of relablty s a exteded tme perod over whch the expected
More informationSingle Machine Scheduling with Stochastic Processing Times or Stochastic Due-Dates to Minimize the Number of Early and Tardy Jobs
Iteratoal Joural of Operatos Research Iteratoal Joural of Operatos Research Vol. 3, No. 2, 9 8 (26 Sgle Mache Schedulg wth Stochastc Processg mes Stochastc Due-Dates to Mmze the Number of arly ad ardy
More information2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen.
.5 x 54.5 a. x 7. 786 7 b. The raked observatos are: 7.4, 7.5, 7.7, 7.8, 7.9, 8.0, 8.. Sce the sample sze 7 s odd, the meda s the (+)/ 4 th raked observato, or meda 7.8 c. The cosumer would more lkely
More informationRademacher Complexity. Examples
Algorthmc Foudatos of Learg Lecture 3 Rademacher Complexty. Examples Lecturer: Patrck Rebesch Verso: October 16th 018 3.1 Itroducto I the last lecture we troduced the oto of Rademacher complexty ad showed
More informationExercises for Square-Congruence Modulo n ver 11
Exercses for Square-Cogruece Modulo ver Let ad ab,.. Mark True or False. a. 3S 30 b. 3S 90 c. 3S 3 d. 3S 4 e. 4S f. 5S g. 0S 55 h. 8S 57. 9S 58 j. S 76 k. 6S 304 l. 47S 5347. Fd the equvalece classes duced
More informationDiscrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b
CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package
More informationThe Necessarily Efficient Point Method for Interval Molp Problems
ISS 6-69 Eglad K Joural of Iformato ad omputg Scece Vol. o. 9 pp. - The ecessarly Effcet Pot Method for Iterval Molp Problems Hassa Mshmast eh ad Marzeh Alezhad + Mathematcs Departmet versty of Ssta ad
More informationIdea is to sample from a different distribution that picks points in important regions of the sample space. Want ( ) ( ) ( ) E f X = f x g x dx
Importace Samplg Used for a umber of purposes: Varace reducto Allows for dffcult dstrbutos to be sampled from. Sestvty aalyss Reusg samples to reduce computatoal burde. Idea s to sample from a dfferet
More informationNon-uniform Turán-type problems
Joural of Combatoral Theory, Seres A 111 2005 106 110 wwwelsevercomlocatecta No-uform Turá-type problems DhruvMubay 1, Y Zhao 2 Departmet of Mathematcs, Statstcs, ad Computer Scece, Uversty of Illos at
More informationChapter 5 Properties of a Random Sample
Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample
More informationPart 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))
art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the
More informationSolution of General Dual Fuzzy Linear Systems. Using ABS Algorithm
Appled Mathematcal Sceces, Vol 6, 0, o 4, 63-7 Soluto of Geeral Dual Fuzzy Lear Systems Usg ABS Algorthm M A Farborz Aragh * ad M M ossezadeh Departmet of Mathematcs, Islamc Azad Uversty Cetral ehra Brach,
More informationEECE 301 Signals & Systems
EECE 01 Sgals & Systems Prof. Mark Fowler Note Set #9 Computg D-T Covoluto Readg Assgmet: Secto. of Kame ad Heck 1/ Course Flow Dagram The arrows here show coceptual flow betwee deas. Note the parallel
More informationTRIANGULAR MEMBERSHIP FUNCTIONS FOR SOLVING SINGLE AND MULTIOBJECTIVE FUZZY LINEAR PROGRAMMING PROBLEM.
Abbas Iraq Joural of SceceVol 53No 12012 Pp. 125-129 TRIANGULAR MEMBERSHIP FUNCTIONS FOR SOLVING SINGLE AND MULTIOBJECTIVE FUZZY LINEAR PROGRAMMING PROBLEM. Iraq Tarq Abbas Departemet of Mathematc College
More informationThe internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test
Fal verso The teral structure of atural umbers oe method for the defto of large prme umbers ad a factorzato test Emmaul Maousos APM Isttute for the Advacemet of Physcs ad Mathematcs 3 Poulou str. 53 Athes
More informationLecture 8: Linear Regression
Lecture 8: Lear egresso May 4, GENOME 56, Sprg Goals Develop basc cocepts of lear regresso from a probablstc framework Estmatg parameters ad hypothess testg wth lear models Lear regresso Su I Lee, CSE
More information8.1 Hashing Algorithms
CS787: Advaced Algorthms Scrbe: Mayak Maheshwar, Chrs Hrchs Lecturer: Shuch Chawla Topc: Hashg ad NP-Completeess Date: September 21 2007 Prevously we looked at applcatos of radomzed algorthms, ad bega
More informationA Mean- maximum Deviation Portfolio Optimization Model
A Mea- mamum Devato Portfolo Optmzato Model Wu Jwe Shool of Eoom ad Maagemet, South Cha Normal Uversty Guagzhou 56, Cha Tel: 86-8-99-6 E-mal: wujwe@9om Abstrat The essay maes a thorough ad systemat study
More information{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:
Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed
More information1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers.
PROBLEMS A real umber s represeted appromately by 63, ad we are told that the relatve error s % What s? Note: There are two aswers Ht : Recall that % relatve error s What s the relatve error volved roudg
More informationCSE 5526: Introduction to Neural Networks Linear Regression
CSE 556: Itroducto to Neural Netorks Lear Regresso Part II 1 Problem statemet Part II Problem statemet Part II 3 Lear regresso th oe varable Gve a set of N pars of data , appromate d by a lear fucto
More informationMAX-MIN AND MIN-MAX VALUES OF VARIOUS MEASURES OF FUZZY DIVERGENCE
merca Jr of Mathematcs ad Sceces Vol, No,(Jauary 0) Copyrght Md Reader Publcatos wwwjouralshubcom MX-MIN ND MIN-MX VLUES OF VRIOUS MESURES OF FUZZY DIVERGENCE RKTul Departmet of Mathematcs SSM College
More informationSome Notes on the Probability Space of Statistical Surveys
Metodološk zvezk, Vol. 7, No., 200, 7-2 ome Notes o the Probablty pace of tatstcal urveys George Petrakos Abstract Ths paper troduces a formal presetato of samplg process usg prcples ad cocepts from Probablty
More informationAnalysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems
Char for Network Archtectures ad Servces Prof. Carle Departmet of Computer Scece U Müche Aalyss of System Performace IN2072 Chapter 5 Aalyss of No Markov Systems Dr. Alexader Kle Prof. Dr.-Ig. Georg Carle
More informationCubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem
Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs
More informationDerivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations
Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat
More informationNewton s Power Flow algorithm
Power Egeerg - Egll Beedt Hresso ewto s Power Flow algorthm Power Egeerg - Egll Beedt Hresso The ewto s Method of Power Flow 2 Calculatos. For the referece bus #, we set : V = p.u. ad δ = 0 For all other
More informationGeneralization of the Dissimilarity Measure of Fuzzy Sets
Iteratoal Mathematcal Forum 2 2007 o. 68 3395-3400 Geeralzato of the Dssmlarty Measure of Fuzzy Sets Faramarz Faghh Boformatcs Laboratory Naobotechology Research Ceter vesa Research Isttute CECR Tehra
More informationLecture 3. Sampling, sampling distributions, and parameter estimation
Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called
More informationSTATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1
STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ
More informationhp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations
HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several
More informationMedian as a Weighted Arithmetic Mean of All Sample Observations
Meda as a Weghted Arthmetc Mea of All Sample Observatos SK Mshra Dept. of Ecoomcs NEHU, Shllog (Ida). Itroducto: Iumerably may textbooks Statstcs explctly meto that oe of the weakesses (or propertes) of
More informationA New Measure of Probabilistic Entropy. and its Properties
Appled Mathematcal Sceces, Vol. 4, 200, o. 28, 387-394 A New Measure of Probablstc Etropy ad ts Propertes Rajeesh Kumar Departmet of Mathematcs Kurukshetra Uversty Kurukshetra, Ida rajeesh_kuk@redffmal.com
More informationarxiv:math/ v1 [math.gm] 8 Dec 2005
arxv:math/05272v [math.gm] 8 Dec 2005 A GENERALIZATION OF AN INEQUALITY FROM IMO 2005 NIKOLAI NIKOLOV The preset paper was spred by the thrd problem from the IMO 2005. A specal award was gve to Yure Boreko
More informationAN UPPER BOUND FOR THE PERMANENT VERSUS DETERMINANT PROBLEM BRUNO GRENET
AN UPPER BOUND FOR THE PERMANENT VERSUS DETERMINANT PROBLEM BRUNO GRENET Abstract. The Permaet versus Determat problem s the followg: Gve a matrx X of determates over a feld of characterstc dfferet from
More informationMulti-Item Multi-Objective Inventory Model with Fuzzy Estimated Price dependent Demand, Fuzzy Deterioration and Possible Constraints
Advaces Fuzzy Mathematcs. ISSN 0973-533XVolume 11, Number (016), pp. 157-170 Research Ida Publcatos http://www.rpublcato.com Mult-Item Mult-Objectve Ivetory Model wth Fuzzy Estmated Prce depedet Demad,
More informationAnalyzing Fuzzy System Reliability Using Vague Set Theory
Iteratoal Joural of Appled Scece ad Egeerg 2003., : 82-88 Aalyzg Fuzzy System Relablty sg Vague Set Theory Shy-Mg Che Departmet of Computer Scece ad Iformato Egeerg, Natoal Tawa versty of Scece ad Techology,
More informationSolving Interval and Fuzzy Multi Objective. Linear Programming Problem. by Necessarily Efficiency Points
Iteratoal Mathematcal Forum, 3, 2008, o. 3, 99-06 Solvg Iterval ad Fuzzy Mult Obectve ear Programmg Problem by Necessarly Effcecy Pots Hassa Mshmast Neh ad Marzeh Aleghad Mathematcs Departmet, Faculty
More informationECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity
ECONOMETRIC THEORY MODULE VIII Lecture - 6 Heteroskedastcty Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur . Breusch Paga test Ths test ca be appled whe the replcated data
More informationCIS 800/002 The Algorithmic Foundations of Data Privacy October 13, Lecture 9. Database Update Algorithms: Multiplicative Weights
CIS 800/002 The Algorthmc Foudatos of Data Prvacy October 13, 2011 Lecturer: Aaro Roth Lecture 9 Scrbe: Aaro Roth Database Update Algorthms: Multplcatve Weghts We ll recall aga) some deftos from last tme:
More informationBeam Warming Second-Order Upwind Method
Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet
More informationOptimal Strategy Analysis of an N-policy M/E k /1 Queueing System with Server Breakdowns and Multiple Vacations
Iteratoal Joural of Scetfc ad Research ublcatos, Volume 3, Issue, ovember 3 ISS 5-353 Optmal Strategy Aalyss of a -polcy M/E / Queueg System wth Server Breadows ad Multple Vacatos.Jayachtra*, Dr.A.James
More informationf f... f 1 n n (ii) Median : It is the value of the middle-most observation(s).
CHAPTER STATISTICS Pots to Remember :. Facts or fgures, collected wth a defte pupose, are called Data.. Statstcs s the area of study dealg wth the collecto, presetato, aalyss ad terpretato of data.. The
More informationC.11 Bang-bang Control
Itroucto to Cotrol heory Iclug Optmal Cotrol Nguye a e -.5 C. Bag-bag Cotrol. Itroucto hs chapter eals wth the cotrol wth restrctos: s boue a mght well be possble to have scotutes. o llustrate some of
More informationCan we take the Mysticism Out of the Pearson Coefficient of Linear Correlation?
Ca we tae the Mstcsm Out of the Pearso Coeffcet of Lear Correlato? Itroducto As the ttle of ths tutoral dcates, our purpose s to egeder a clear uderstadg of the Pearso coeffcet of lear correlato studets
More informationA Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming
Aerca Joural of Operatos Research, 4, 4, 33-339 Publshed Ole Noveber 4 ScRes http://wwwscrporg/oural/aor http://ddoorg/436/aor4463 A Pealty Fucto Algorth wth Obectve Paraeters ad Costrat Pealty Paraeter
More informationMultivariate Transformation of Variables and Maximum Likelihood Estimation
Marquette Uversty Multvarate Trasformato of Varables ad Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Assocate Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 03 by Marquette Uversty
More informationChapter 4 Multiple Random Variables
Revew for the prevous lecture: Theorems ad Examples: How to obta the pmf (pdf) of U = g (, Y) ad V = g (, Y) Chapter 4 Multple Radom Varables Chapter 44 Herarchcal Models ad Mxture Dstrbutos Examples:
More information10.1 Approximation Algorithms
290 0. Approxmato Algorthms Let us exame a problem, where we are gve A groud set U wth m elemets A collecto of subsets of the groud set = {,, } s.t. t s a cover of U: = U The am s to fd a subcover, = U,
More informationPinaki Mitra Dept. of CSE IIT Guwahati
Pak Mtra Dept. of CSE IIT Guwahat Hero s Problem HIGHWAY FACILITY LOCATION Faclty Hgh Way Farm A Farm B Illustrato of the Proof of Hero s Theorem p q s r r l d(p,r) + d(q,r) = d(p,q) p d(p,r ) + d(q,r
More informationA COMPARATIVE STUDY OF THE METHODS OF SOLVING NON-LINEAR PROGRAMMING PROBLEM
DAODIL INTERNATIONAL UNIVERSITY JOURNAL O SCIENCE AND TECHNOLOGY, VOLUME, ISSUE, JANUARY 9 A COMPARATIVE STUDY O THE METHODS O SOLVING NON-LINEAR PROGRAMMING PROBLEM Bmal Chadra Das Departmet of Tetle
More informationECON 5360 Class Notes GMM
ECON 560 Class Notes GMM Geeralzed Method of Momets (GMM) I beg by outlg the classcal method of momets techque (Fsher, 95) ad the proceed to geeralzed method of momets (Hase, 98).. radtoal Method of Momets
More informationbest estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best
Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg
More informationLecture Notes Types of economic variables
Lecture Notes 3 1. Types of ecoomc varables () Cotuous varable takes o a cotuum the sample space, such as all pots o a le or all real umbers Example: GDP, Polluto cocetrato, etc. () Dscrete varables fte
More informationIntroduction to local (nonparametric) density estimation. methods
Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest
More informationBootstrap Method for Testing of Equality of Several Coefficients of Variation
Cloud Publcatos Iteratoal Joural of Advaced Mathematcs ad Statstcs Volume, pp. -6, Artcle ID Sc- Research Artcle Ope Access Bootstrap Method for Testg of Equalty of Several Coeffcets of Varato Dr. Navee
More information