An MILP model for planning of batch plants operating in a campaignmode


 Suzanna Nash
 3 years ago
 Views:
Transcription
1 An MILP model for plannng of batch plants operatng n a campagnmode Yanna Fumero Insttuto de Desarrollo y Dseño CONICET UTN Gabrela Corsano Insttuto de Desarrollo y Dseño CONICET UTN santafeconcet.gov.ar Jorge M. Montagna Insttuto de Desarrollo y Dseño CONICET UTN santafeconcet.gov.ar Abstract A mxed nteger lnear programmng for the detaled producton plannng of multproduct batch plants s proposed n ths work. New tmng decsons are ncorporated to the model takng nto account that an operaton mode based n campagns s adopted. For plants operatng n a regular fashon along a tme horzon, ths operaton mode assures a more effcent producton management. In addton, sequencedependent changeover tmes and dfferent unt szes for parallel unts n each stage are consdered. Gven the plant confguraton and unt szes, the total amount of each product to be produced and the product recpes, the proposed model determnes the number of batches that compose the producton campagn and ther szes, the assgnment and sequencng of batches n each unt, and the tmng of batches n each unt n order to mnmze the campagn cycle tme. The proposed model provdes a useful tool for solvng the optmal campagn plannng of nstalled facltes. Keywords: multproduct batch plants; producton campagn; plannng; schedulng; MILP model. 1 Introducton Multproduct batch plants are characterzed by ther flexblty to manufacture multple products usng the same equpment. These plants consst of a collecton of processng unts where batches of the varous products are produced by executng a set of operatons. These operatons can be characterzed by a processng tme and they do not nvolve both smultaneous feed and removal of products from the unt durng ths processng tme. Unts that perform the same operaton are grouped n a producton stage, and they can operate n parallel mode (n phase or out of phase). In multproduct batch plants, every product follows the same sequence through all the processng stages (Voudours and Grossmann, 1992). Assumng a gven plant,.e., ts confguraton and the unt szes are known, dfferent producton problems can be posed dependng of the contemplated scenaro. In partcular, when products demands can be accurately forecasted durng a relatvely long tme horzon due to a stable context, more effcent management and control of the producton resources can be attaned f the plant s operated n a perodc
2 or cyclc way,.e. n a campagnmode. In ths case, the campagn conssts of several batches of dfferent products that are gong to be manufactured and the same pattern s repeated at a constant frequency over a tme horzon. Ths campagnbased operaton mode has several advantages, for example, more standardzed producton durng certan perods of tme, easer and proftable operatons decsons, more effcent operaton control, and adequate nventory levels wthout generatng excessve costs and mnmzng the possblty of stockouts. Under ths context, a cyclc schedulng problem must be addressed. Ths type of schedulng s used for products manufacturng wth relatvely constant demand durng a plannng horzon, whch lead to a more regular producton mode and t s more approprate for a maketostock producton polcy. From the computatonal pont of vew, the cyclc schedulng allows reducng the sze of the overall schedulng problem, whch s often ntractable. On the other hand, from the modelng pont of vew, one of the man dfferences between cyclc schedulng based on mxed product campagns (MPCs) and shortterm schedulng s the adopted objectve functon. Whle the most of approaches for shortterm schedulng dealt wth makespan mnmzaton, tardness or earlness, the most approprate performance measure for the schedulng problem usng MPCs of cyclc repetton s the mnmzaton of the campagn cycle tme (Fumero et al., 2012). Takng n mnd that n a plannng context the campagn wll be repeated over the tme horzon, consecutve campagns have to be overlapped n order to reduce dle tmes between them as much as possble. Accordng to Maravelas (2012), the schedulng problem n the context of batch process nvolves the followng decsons: () selecton and szng of batches to be carred out; () assgnment of batches to process unts; () sequencng of batches on unts; and (v) tmng of batches. Takng nto account the combnatoral nature of the problem, most of the exstng approaches n the process systems engneerng lterature consder a specal case of the problem, where the number and sze of batches s fxed,.e. the lotszng problem s solved frst and then obtaned batches are used as nputs n the schedulng model. The schedulng problem usng MPCs was scarcely addressed n the lterature. Besdes the paper of Fumero et al. (2012), Brewar and Grossmann (1989) developed slotbased formulatons MILP for schedulng of multproduct batch plants usng producton campagns, consderng dfferent transfer polces (unlmted ntermedate storage, UIS, and zero wat, ZW) and where the number and sze of batches are data problem. They determned the optmal campagn cycle tme, for smple plants ncludng only one unt per processng stage. In Fumero et al. (2011) two MILP models for the smultaneous desgn and schedulng of a multstage batch plant are proposed. The parallel unts are consdered dentcal and no changeover tmes are taken nto account. The rest of the papers that menton the use of campagns, do not refer to the determnaton of batches and ts cyclc sequencng, as t s managed n ths work. In ths work, the detaled plannng problem of multstage batch plants wth an operaton based on MPCs s addressed usng a MILP model. It s assumed that the plant manager must produce known demands usng a cyclc campagn durng a tme horzon. Nondentcal parallel unts, ZW transfer polcy and sequencedependent changeover tmes are consdered. Gven the plant confguraton and unt szes, the total amount of each product to be produced n the campagn and the product recpes, the approach determnes the number of batches that compose the producton campagn and ther szes, the batches assgnment to unts, the sequencng of batches n each unt for each stage, and the ntal and fnal tmes of the batches processed n each unt n order to mnmze the campagn cycle tme. Wth the am of reducng the combnatoral complexty assocated to the schedulng decsons, addtonal constrants are consdered n order to elmnate equvalent symmetrc solutons. Then, the schedulng approach through MPCs consderng sequencedependent changeover tmes for multstage batch plants wth nondentcal parallel unts s effcently solved. 2 Problem descrpton
3 The problem addressed n ths artcle deals wth a multproduct batch plant where J denotes the set of processng stages that compose the plant and K the set of all unts n the plant. K j represents the set of nondentcal parallel batch unts that operate outofphase n stage j, so K = K 1 K 2 K J. A set I of products must be manufactured n the plant followng the same sequence of stages. The total amount requred of each product n the campagn, Q ( I), whch allows mantanng adequate stocks levels takng nto account the estmated demands, s a model parameter. Q can be fulflled wth one or more batches, therefore an ndex b s ntroduced to denote the bth batch requred to meet producton of the correspondng product. In each stage, there are not restrctons about parallel unt szes and, therefore, dfferent unt szes are admtted. Then, V k s used to denote the sze of unt k. The processng tme of each batch of product n unt k, t k, and the sze factor SF j that denotes the requred capacty of unts n stage j to produce one mass unt of fnal product, are problem data. Consderng the demand of product, the nondentcal parallel unt szes for each stage, the equpment utlzaton mnmum rate for product at each unt, denoted by α k, and the sze factors of product n each stage, the mnmum and maxmum numbers of batches requred to fulfll the demand of product can be calculated n order to ensure soluton optmalty. Thus, the mnmum and maxmum numbers of batches of product for the campagn are calculated, respectvely, as followng: LOW Q UP Q NBC = max and NBC = mn B B max mn where B = mn max and B = max mn α k are the maxmum and mnmum j J k K j SF j J k K j j SFj feasble batch szes for product. The upper bound for the number of batches of each product n the UP NBC campagn allows to propose a set of generc batches assocated to that product, IB, where IB =. Intermedate storage tanks are not allowed. Therefore, takng nto account the confguraton of the plant, there s no batch splttng or mxng,.e. each batch s treated as a dscrete entty throughout the whole process. It s assumed that a batch cannot wat n a unt after fnshng ts processng. Therefore, the ZW transfer polcy between stages s adopted,.e., after beng processed n stage j, a batch b s mmedately transferred to the next stage j+1. Besdes, batch transfer tmes between unts are assumed very small compared to process operaton tmes and, consequently, they are ncluded n the processng tmes. Sequencedependent changeover tmes, c k, are consdered between consecutve batches processed n the same unt k, even of the same product. Ths transton tme corresponds to the preparaton or cleanng of the equpment to perform the followng batch processng. It s necessary for varous reasons: ensure products qualty, mantan the equpment, safety reasons, etc. For schedulng decsons, an asynchronous slotbased contnuoustme representaton has been used. The slots correspond to tme ntervals of varable length where batches wll be assgned. In each slot l of a specfc unt k at most one batch b of product can be processed and, f no product s assgned to slot l, ts length wll be zero. The number of slots that must be postulated for unt k of stage j, denoted by L kj, can be approxmated consderng the estmaton on the maxmum number of batches of each product at the campagn. Then, the number of slots postulated for all unts of each stage s the same and t s gven by: UP L NBC k, j = I Although ths value s an overestmaton, a major approxmaton cannot be proposed takng nto account that the parallel unts are dfferent and, on the other hand, the number and szes of batches to be scheduled are optmzaton varables, unlke the most of schedulng approaches presented n the lterature where they are consdered as parameters. However, the lower bound on the number of batches of product at the
4 campagn, NBC LOW, strongly reduces the number of possble combnatons and consequently mproves the computatonal performance of the model. As prevously stated, the problem conssts of solvng smultaneously two decson levels often addressed sequentally. Through a holstc approach, the selecton and szng of batches of each product, the assgnment of batches to unts n each stage, the producton sequence of assgned batches n each unt and ntal and fnal processng tmes for batches that compose the campagn n each processng unt are jontly determned. 3 Mathematcal formulaton 3.1 Batches selecton and szng constrants The number of batches of product that must be manufactured n the campagn s a model varable. Then, a bnary varable z b s ntroduced, whch takes value 1 f batch b of product s selected to satsfy the demand requrements of that product and 0 otherwse. Let B b be the sze of batch b of product and Q the demand of product that must be fulflled, then: Q B I (1) = b IB b Takng nto account that the sze of unt k denoted by V k and the sze factor SF j are model parameters, f batch b of product s processed n unt k of stage j the followng nequaltes lmt the sze B b of batch b between the mnmum and maxmum processng capactes of unt k: α k Bb I,b IB,k {unts of stage j used to process batch b} (2) SF SF j j where α k s the mnmum flled rate requred to process product n unt k. Due to the unts selected to process the batches of each product are optmzaton varables and ther szes are dfferent, Eq. (2) must be expressed through a varable that ndcates ths selecton, as t wll see later. Besdes, wthout loss the generalty and n order to reduce the number of alternatve solutons, the selecton of batches of a same product as well as the assgned szes to them are made n ascendng and descendng numercal order, respectvely, that s: zb+ 1 zb I,b IB,b + 1 IB (3) Bb+ 1 Bb I,b IB,b + 1 IB (4) 3.2 Assgnment and Sequencng constrants Selected batches must be assgned, n each stage, to specfc slots n the unts. Then, the bnary varable Y bkl s ntroduced, whch takes value 1 f batch b s assgned to slot l n unt k and 0 otherwse. Although ths varable s enough for formulatng the schedulng problem, the bnary varable X kl, whch specfes the slots set utlzed n unt k for processng batches, wll be also used n order to reduce the search space and, therefore, to mprove the computatonal performance. Logcal relatons can be defned among bnary varables z b, X kl and Y bkl. In fact, f slot l of unt k s not utlzed, then none of the proposed batches s processed n t. Moreover, f slot l of unt k s utlzed, then only one of the proposed batches s processed n t. Then, the followng constrant s mposed: Y = X j J,k K, 1 l L (5) I b IB bkl kl j On the other hand, f batch b of product s selected (.e. z b = 1), then ths batch s processed, n each stage j, n only one slot of some of the avalable unts at the stage. Ths condton s guaranteed by:
5 Y bkl k K 1 l L j = z b j J, I,b IB Wthout loss of generalty and n order to reduce the search space, t s assumed that slots of each unt are consecutvely used n ascendng numercal order. Hence, the slots of zero length take place at the end of each unt. Eq. (7) establshes that for each unt k, slot l+1 s only used f slot l has been already allocated: X kl X kl+ 1, j J,k K j, 1 l L (7) Fnally, varable Y bkl allow correctly expressng the nequaltes posed n (2) as: α k Ybkl Bb I,b I, 1 l L (8) SF B b j SF j + M (1 Y ) I,b IB 1 bkl (9) 1 l L where scalar M 1 s a suffcently large number Tmng constrants Nonnegatve contnuous varables, TI kl and TF kl, are used to represent the ntal and fnal processng tmes, respectvely, of the proposed slots n each unt k. When slot l s not the last slot used n unt k of stage j for processng one batch, that s, f Y b kl+1 take value 1 for some b, fnal processng tme TF kl of slot l n unt k s constraned by: TFkl TI kl + (tk + c'k )Y bkl Yb'kl + 1 j J,k K j, 1 l < L (10) = I ' I b IB b' IB ' b b' A nonnegatve varable YY blb l k s defned to elmnate the blnear products, whch takes value 1 f Y bkl = 1 and Y b' kl + 1 = 1, and 0 otherwse, so (10) s represent usng BgM expressons: TFkl TIkl ( tk + c' k ) YY blb' l + 1 k M 2( X kl + 1 ) j J,k K j, 1 l < L (11a) 1 I ' I b IB b' IB' b b' 1 TFkl + TIkl + ( tk + c' k ) YY blb' l + 1 k M 2( X kl + 1 1) j J,k K j, l < L (11b) I ' I b IB b' IB' b b' On the other hand, when the sequence of slots used n unt k s 1, 2,... l,.e. slot l s the last slot used at unt k of stage j to process some batch, takng nto account that the campagn can be cyclcal repeated over a tme horzon, the fnal processng tme TF kl s calculated consderng the changeover tme requred for processng the batch assgned to slot 1 n unt k of stage j. Constrants analogous to (11a) and (11b) are posed for ths case. Constrants to avod the overlappng between the processng tmes of dfferent slots n a unt as well as to match the ntal tmes of empty slots wth the fnal tme of the prevous slot are added to the formulaton. In order to assure ZW transfer polcy, constrants of BgM type are ncluded, dependng f slot l s or s not the last slot used at unt k for processng one batch. Due to space reasons, ths set of constrants s not provded n ths manuscrpt, but nterested readers can request t to authors. Fnally, takng nto account that slots of each unt are used n ascendng numercal order, the expresson for the cycle tme of the campagn, CT, s gven by: CT TFkL TI k1, j J,k K j (12) 3.4. Objectve functon The problem goal s to mnmze the cycle tme of the producton campagn that fulflls the demands requrements, subject to prevous constrants. (6)
6 4 Example The consdered batch plant conssts of three stages wth nondentcal parallel unts wth known szes that operate outofphase, as s llustrated n Fgure 1. Avalable unts at each stage are denoted by the sets: K 1 = {1}, K 2 = {2, 3}, and K 3 = {4, 5}, respectvely. Products A, B, and C have to be processed through all stages before beng converted nto fnal products. The requred amounts n the campagn are Q A = 10500, Q B = 6000 and Q C = Data on processng tmes and sze factors of each product are shown n Table 1, whle the sequencedependent changeover tmes are gven n Table 2. Consderng the nondentcal parallel unt szes at each stage, the sze factors for each product n each stage and assumng that the equpment utlzaton mnmum rate s 0.50 for all products and equpment tems, the mnmum feasble batch szes for products A, B and C are: B mn = 0. 5max 5714 kg, 5000 kg,5000 kg = 2857 { } kg { 6666 kg, 4285kg,5555kg} 3333kg { 5714 kg, 4615 kg, 4545 kg} 2857 kg A, B mn B = 0. 5max =, B mn C = 0. 5max =. Stage 1 Stage 2 Stage L 4200 L 3000 L 3000 L 2500 L Fgure 1. Plant structure Table 1. Processng tmes and sze factors of products Processng tme: t k (h) Sze factor: SF j (L/kg) Product Stage 1 Stage 2 Stage 3 Stage 1 Stage 2 Stage k = 1 k = 2, 3 k = 4, 5 A B C Table 2. Sequencedependent changeover tmes Product Sequencedependent changeover tme: c k (h) Stage 1 Stage 2 Stage 3 k = 1 k = 2, 3 k = 4, 5 A B C A B C A B C A B C
7 Then, consderng the campagn demands for all products, the maxmum number of batches of each product at the campagn s four, two and four for products A, B and C, respectvely. Thus, the sets of proposed batches are {b 1, b 2, b 3, b 4 }, {b 5, b 6 }, and {b 7, b 8, b 9, b 10 } for products A, B and C, respectvely, and consequently a total of ten batches must be postulated to guarantee the global optmalty of the soluton. Also, the maxmum feasble batch szes for all products allow determnng the mnmum number of batches of every product at the campagn. In ths case, the maxmum feasble batch szes for all products are: B max A B max B = mn = mn { 5714 kg, 7000 kg, 6000 kg} = 5714 kg { 6666 kg, 6000 kg, 6666 kg} = 6000 kg { 5714 kg, 6461 kg, 5454 kg} = 5454 kg B max C = mn then, the requred mnmum number of batches for products A and C s two, whle for product B s one. The model under these assumptons comprses constrants, 9167 contnuous varables and 555 bnary varables. It was mplemented and solved usng GAMS, va CPLEX 12.5 solver, n CPU seconds wth a 0% of optmalty gap. The optmal campagn cycle tme s equal to 70.4 hours and t nvolves two batches of product A (b 1, b 2 ), one of B (b 5 ), and two of C (b 7, b8),.e. the demands of all products are fulfll wth the mnmum number of batches. The optmal producton sequence obtaned n each batch unt for the dfferent stages, consderng sequencedependent changeover tmes, s llustrated n the Gantt chart of Fgure 2. Takng nto account that the optmal campagn s cyclcally repeated over a tme horzon, the changeover tmes between products processed n the last and frst slot of each unt must be ncluded n the optmzaton n order to acheve the accurate overlap of successve campagns. For ths example, as t can be seen from Fgure 2, changeover tmes between pars of campagns are: c AC1 = 0.3 h for unt of stage 1; c AC2 = c AC3 = 0.4 h for unts of stage 2; and c AC4 = c AC5 = 0.6 h for unts of stage 3. Unts Campagn cycle tme = 70.4 h Stage 1 1 b7 b5 b8 b1 b2 Stage b7 b5 b8 b1 b2 Stage b7 b5 b8 b1 b Product A Product B Product C Changeovers tmes Tme (h) Fgure 2. Gantt chart of the producton campagn Batches b 1 and b 2 satsfy the total requred demand of product A wth szes of 5500 kg and 5000 kg, respectvely; batch b 5 wth sze equal to 6000 kg s only selected to accomplsh the campagn demand of product B; whle batches b 7 and b 8 are requred to acheve the producton of C wth szes equal to 4955 kg and 4545 kg, respectvely. The capactes used n each unt of the dfferent stages for processng the selected batches are resumed n Table 3. The batches that reach the maxmum capactes are hghlghted n boxes shaded n gray. Batch b 2 of product A s processed n unts 1, 3 and 5 and ts sze s the maxmum possble to be processed n unts 3 and 5 of stages 2 and 3, respectvely. Then, batch b 1 fulflls the requred amount of that product occupyng approxmately 96%, 79% and 92% of the capacty of unts 1, 2 and 4, respectvely. Batch b 5 of product B s processed n unts 1, 2 and 4 and ts sze s the maxmum
8 possble to be processed n unt 2 of stage 2. On the other hand, two batches of product C are processed for meetng ts demand. Batch b 8 s processed n unts 1, 3 and 5 usng 80%, 98.5% and 100% of ther capactes, respectvely; whle batch b 7 fulflls the requred amount of that product n the campagn. Table 3. Capactes used n each unt of each stage Stage 1 Stage 2 Stage 3 Product Batch k = 1 k = 2 k = 3 k = 4 k = 5 A b b B b C b b Conclusons In ths work, the optmal producton plannng of multstage batch plants wth nondentcal parallel unts that operate n campagnmode s faced. Schedulng s modeled accordng to campagnbased operaton mode n such way that the campagn cycle tme mnmzaton s an approprate optmzaton crteron. Sequencedependent changeover tmes are consdered for each ordered par of products n each unt of the dfferent stages. Takng nto account the complexty of the smultaneous nvolved decsons, some addtonal constrants that elmnate equvalent symmetrc solutons mantanng the model generalty are consdered, n order to reduce the search space and therefore mprove the computatonal performance. Also, varous equatons are reformulated n order to keep the problem lnear and assure the global optmalty of the soluton. Through the example the capabltes of the proposed formulaton are shown. Wth the proposed formulaton, an nterestng problem has been solved. Many tmes, n madetostock contexts, the campagnbased operaton mode s an approprate alternatve that allows takng advantage of the avalable resources wth an ordered producton management. The proposed model smultaneously solves lotszng and schedulng problems n reasonable computng tme. Thus, ths approach can be appled n real producton systems that operate n campagnmode takng nto account the assumed suppostons as far as dfferent unt szes, changeovers, etc. References 1. C. T. Maravelas. General framework and modelng approach classfcaton for chemcal producton schedulng. AIChE Journal 2012, 58(6): , D. Brewar and I. E. Grossmann. Incorporatng schedulng n the optmal desgn of multproduct batch plants. Computers and Chemcal Engneerng, 13, , V.T. Voudours, I.E. Grossmann. MxedInteger Lnear Programmng Reformulatons for Batch Process Desgn wth Dscrete Equpment Szes. Ind. Eng. Chem. Res. 1992,31, Y. Fumero, G. Corsano, J. M. Montagna. Schedulng of multstage multproduct batch plants operatng n a campagnmode. Industral Engneerng and Chemcal Research, 5: , Y. Fumero, G. Corsano, J. M. Montagna. Detaled Desgn of Multproduct Batch Plants Consderng Prodcton Schedulng. Industral Engneerng and Chemcal Research, 50 (10), , 2011.
Project Networks With MixedTime Constraints
Project Networs Wth MxedTme Constrants L Caccetta and B Wattananon Western Australan Centre of Excellence n Industral Optmsaton (WACEIO) Curtn Unversty of Technology GPO Box U1987 Perth Western Australa
More informationPowerofTwo Policies for Single Warehouse MultiRetailer Inventory Systems with Order Frequency Discounts
Powerofwo Polces for Sngle Warehouse MultRetaler Inventory Systems wth Order Frequency Dscounts José A. Ventura Pennsylvana State Unversty (USA) Yale. Herer echnon Israel Insttute of echnology (Israel)
More information2008/8. An integrated model for warehouse and inventory planning. Géraldine Strack and Yves Pochet
2008/8 An ntegrated model for warehouse and nventory plannng Géraldne Strack and Yves Pochet CORE Voe du Roman Pays 34 B1348 LouvanlaNeuve, Belgum. Tel (32 10) 47 43 04 Fax (32 10) 47 43 01 Emal: corestatlbrary@uclouvan.be
More informationbenefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).
REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or
More informationOptimization of network mesh topologies and link capacities for congestion relief
Optmzaton of networ mesh topologes and ln capactes for congeston relef D. de Vllers * J.M. Hattngh School of Computer, Statstcal and Mathematcal Scences Potchefstroom Unversty for CHE * Emal: rwddv@pu.ac.za
More information1 Approximation Algorithms
CME 305: Dscrete Mathematcs and Algorthms 1 Approxmaton Algorthms In lght of the apparent ntractablty of the problems we beleve not to le n P, t makes sense to pursue deas other than complete solutons
More informationThe Greedy Method. Introduction. 0/1 Knapsack Problem
The Greedy Method Introducton We have completed data structures. We now are gong to look at algorthm desgn methods. Often we are lookng at optmzaton problems whose performance s exponental. For an optmzaton
More informationModule 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..
More informationAPPLICATION OF COMPUTER PROGRAMMING IN OPTIMIZATION OF TECHNOLOGICAL OBJECTIVES OF COLD ROLLING
Journal Journal of Chemcal of Chemcal Technology and and Metallurgy, 50, 6, 50, 2015, 6, 2015 638643 APPLICATION OF COMPUTER PROGRAMMING IN OPTIMIZATION OF TECHNOLOGICAL OBJECTIVES OF COLD ROLLING Abdrakhman
More informationAn Alternative Way to Measure Private Equity Performance
An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate
More informationA DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATIONBASED OPTIMIZATION. Michael E. Kuhl Radhamés A. TolentinoPeña
Proceedngs of the 2008 Wnter Smulaton Conference S. J. Mason, R. R. Hll, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATIONBASED OPTIMIZATION
More informationOn the Optimal Control of a Cascade of HydroElectric Power Stations
On the Optmal Control of a Cascade of HydroElectrc Power Statons M.C.M. Guedes a, A.F. Rbero a, G.V. Smrnov b and S. Vlela c a Department of Mathematcs, School of Scences, Unversty of Porto, Portugal;
More informationANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING
ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 6105194390,
More informationMethod for Production Planning and Inventory Control in Oil
Memors of the Faculty of Engneerng, Okayama Unversty, Vol.41, pp.2030, January, 2007 Method for Producton Plannng and Inventory Control n Ol Refnery TakujImamura,MasamKonshandJunIma Dvson of Electronc
More informationActivity Scheduling for CostTime Investment Optimization in Project Management
PROJECT MANAGEMENT 4 th Internatonal Conference on Industral Engneerng and Industral Management XIV Congreso de Ingenería de Organzacón Donosta San Sebastán, September 8 th 10 th 010 Actvty Schedulng
More informationSUPPLIER FINANCING AND STOCK MANAGEMENT. A JOINT VIEW.
SUPPLIER FINANCING AND STOCK MANAGEMENT. A JOINT VIEW. Lucía Isabel García Cebrán Departamento de Economía y Dreccón de Empresas Unversdad de Zaragoza Gran Vía, 2 50.005 Zaragoza (Span) Phone: 976761000
More informationA method for a robust optimization of joint product and supply chain design
DOI 10.1007/s1084501409085 A method for a robust optmzaton of jont product and supply chan desgn Bertrand BaudLavgne Samuel Bassetto Bruno Agard Receved: 10 September 2013 / Accepted: 21 March 2014
More informationThe Development of Web Log Mining Based on ImproveKMeans Clustering Analysis
The Development of Web Log Mnng Based on ImproveKMeans Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.
More information行 政 院 國 家 科 學 委 員 會 補 助 專 題 研 究 計 畫 成 果 報 告 期 中 進 度 報 告
行 政 院 國 家 科 學 委 員 會 補 助 專 題 研 究 計 畫 成 果 報 告 期 中 進 度 報 告 畫 類 別 : 個 別 型 計 畫 半 導 體 產 業 大 型 廠 房 之 設 施 規 劃 計 畫 編 號 :NSC 962628E009026MY3 執 行 期 間 : 2007 年 8 月 1 日 至 2010 年 7 月 31 日 計 畫 主 持 人 : 巫 木 誠 共 同
More informationStochastic Inventory Management for Tactical Process Planning under Uncertainties: MINLP Models and Algorithms
Stochastc Inventory Management for Tactcal Process Plannng under Uncertantes: MINLP Models and Algorthms Fengq You, Ignaco E. Grossmann Department of Chemcal Engneerng, Carnege Mellon Unversty Pttsburgh,
More informationPreventive Maintenance and Replacement Scheduling: Models and Algorithms
Preventve Mantenance and Replacement Schedulng: Models and Algorthms By Kamran S. Moghaddam B.S. Unversty of Tehran 200 M.S. Tehran Polytechnc 2003 A Dssertaton Proposal Submtted to the Faculty of the
More informationAN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE
AN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE YuL Huang Industral Engneerng Department New Mexco State Unversty Las Cruces, New Mexco 88003, U.S.A. Abstract Patent
More informationOmega 39 (2011) 313 322. Contents lists available at ScienceDirect. Omega. journal homepage: www.elsevier.com/locate/omega
Omega 39 (2011) 313 322 Contents lsts avalable at ScenceDrect Omega journal homepage: www.elsever.com/locate/omega Supply chan confguraton for dffuson of new products: An ntegrated optmzaton approach Mehd
More informationJoint Scheduling of Processing and Shuffle Phases in MapReduce Systems
Jont Schedulng of Processng and Shuffle Phases n MapReduce Systems Fangfe Chen, Mural Kodalam, T. V. Lakshman Department of Computer Scence and Engneerng, The Penn State Unversty Bell Laboratores, AlcatelLucent
More informationChapter 4 ECONOMIC DISPATCH AND UNIT COMMITMENT
Chapter 4 ECOOMIC DISATCH AD UIT COMMITMET ITRODUCTIO A power system has several power plants. Each power plant has several generatng unts. At any pont of tme, the total load n the system s met by the
More informationFeature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College
Feature selecton for ntruson detecton Slobodan Petrovć NISlab, Gjøvk Unversty College Contents The feature selecton problem Intruson detecton Traffc features relevant for IDS The CFS measure The mrmr measure
More informationAnts Can Schedule Software Projects
Ants Can Schedule Software Proects Broderck Crawford 1,2, Rcardo Soto 1,3, Frankln Johnson 4, and Erc Monfroy 5 1 Pontfca Unversdad Católca de Valparaíso, Chle FrstName.Name@ucv.cl 2 Unversdad Fns Terrae,
More informationInstitute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic
Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange
More informationA Binary Particle Swarm Optimization Algorithm for Lot Sizing Problem
Journal o Economc and Socal Research 5 (2), 2 A Bnary Partcle Swarm Optmzaton Algorthm or Lot Szng Problem M. Fath Taşgetren & YunCha Lang Abstract. Ths paper presents a bnary partcle swarm optmzaton
More informationThe OC Curve of Attribute Acceptance Plans
The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4
More informationFault tolerance in cloud technologies presented as a service
Internatonal Scentfc Conference Computer Scence 2015 Pavel Dzhunev, PhD student Fault tolerance n cloud technologes presented as a servce INTRODUCTION Improvements n technques for vrtualzaton and performance
More informationEnabling P2P Oneview Multiparty Video Conferencing
Enablng P2P Onevew Multparty Vdeo Conferencng Yongxang Zhao, Yong Lu, Changja Chen, and JanYn Zhang Abstract MultParty Vdeo Conferencng (MPVC) facltates realtme group nteracton between users. Whle P2P
More informationSimulation and optimization of supply chains: alternative or complementary approaches?
Smulaton and optmzaton of supply chans: alternatve or complementary approaches? Chrstan Almeder Margaretha Preusser Rchard F. Hartl Orgnally publshed n: OR Spectrum (2009) 31:95 119 DOI 10.1007/s002910070118z
More informationPeriod and Deadline Selection for Schedulability in RealTime Systems
Perod and Deadlne Selecton for Schedulablty n RealTme Systems Thdapat Chantem, Xaofeng Wang, M.D. Lemmon, and X. Sharon Hu Department of Computer Scence and Engneerng, Department of Electrcal Engneerng
More informationResearch Article Enhanced TwoStep Method via Relaxed Order of αsatisfactory Degrees for Fuzzy Multiobjective Optimization
Hndaw Publshng Corporaton Mathematcal Problems n Engneerng Artcle ID 867836 pages http://dxdoorg/055/204/867836 Research Artcle Enhanced TwoStep Method va Relaxed Order of αsatsfactory Degrees for Fuzzy
More informationCommunication Networks II Contents
8 / 1  Communcaton Networs II (Görg)  www.comnets.unbremen.de Communcaton Networs II Contents 1 Fundamentals of probablty theory 2 Traffc n communcaton networs 3 Stochastc & Marovan Processes (SP
More informationHeuristic Static LoadBalancing Algorithm Applied to CESM
Heurstc Statc LoadBalancng Algorthm Appled to CESM 1 Yur Alexeev, 1 Sher Mckelson, 1 Sven Leyffer, 1 Robert Jacob, 2 Anthony Crag 1 Argonne Natonal Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439,
More informationSoftware project management with GAs
Informaton Scences 177 (27) 238 241 www.elsever.com/locate/ns Software project management wth GAs Enrque Alba *, J. Francsco Chcano Unversty of Málaga, Grupo GISUM, Departamento de Lenguajes y Cencas de
More informationA hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm
Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(7):18841889 Research Artcle ISSN : 09757384 CODEN(USA) : JCPRC5 A hybrd global optmzaton algorthm based on parallel
More informationNathalie Perrier Bruno Agard Pierre Baptiste JeanMarc Frayret André Langevin Robert Pellerin Diane Riopel Martin Trépanier.
A Survey of Models and Algorthms for Emergency Response Logstcs n Electrc Dstrbuton Systems  Part I: Relablty Plannng wth Fault Consderatons Nathale Perrer Bruno Agard Perre Baptste JeanMarc Frayret
More informationCalculation of Sampling Weights
Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a twostage stratfed cluster desgn. 1 The frst stage conssted of a sample
More information1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP)
6.3 /  Communcaton Networks II (Görg) SS20  www.comnets.unbremen.de Communcaton Networks II Contents. Fundamentals of probablty theory 2. Emergence of communcaton traffc 3. Stochastc & Markovan Processes
More informationOptimal Joint Replenishment, Delivery and Inventory Management Policies for Perishable Products
Optmal Jont Replenshment, Delvery and Inventory Management Polces for Pershable Products Leandro C. Coelho Glbert Laporte May 2013 CIRRELT201332 Bureaux de Montréal : Bureaux de Québec : Unversté de
More informationAn Integrated Approach for Maintenance and Delivery Scheduling in Military Supply Chains
An Integrated Approach for Mantenance and Delvery Schedulng n Mltary Supply Chans Dmtry Tsadkovch 1*, Eugene Levner 2, Hanan Tell 2 and Frank Werner 3 2 1 Bar Ilan Unversty, Department of Management, Ramat
More informationScatter search approach for solving a home care nurses routing and scheduling problem
Scatter search approach for solvng a home care nurses routng and schedulng problem Bouazza Elbenan 1, Jacques A. Ferland 2 and Vvane Gascon 3* 1 Département de mathématque et nformatque, Faculté des scences,
More informationWhat is Candidate Sampling
What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble
More informationMultiPeriod Resource Allocation for Estimating Project Costs in Competitive Bidding
Department of Industral Engneerng and Management Techncall Report No. 20146 MultPerod Resource Allocaton for Estmatng Project Costs n Compettve dng Yuch Takano, Nobuak Ish, and Masaak Murak September,
More informationAPPLICATION OF PROBE DATA COLLECTED VIA INFRARED BEACONS TO TRAFFIC MANEGEMENT
APPLICATION OF PROBE DATA COLLECTED VIA INFRARED BEACONS TO TRAFFIC MANEGEMENT Toshhko Oda (1), Kochro Iwaoka (2) (1), (2) Infrastructure Systems Busness Unt, Panasonc System Networks Co., Ltd. Saedocho
More informationLogistic Regression. Lecture 4: More classifiers and classes. Logistic regression. Adaboost. Optimization. Multiple class classification
Lecture 4: More classfers and classes C4B Machne Learnng Hlary 20 A. Zsserman Logstc regresson Loss functons revsted Adaboost Loss functons revsted Optmzaton Multple class classfcaton Logstc Regresson
More informationNONLINEAR OPTIMIZATION FOR PROJECT SCHEDULING AND RESOURCE ALLOCATION UNDER UNCERTAINTY
NONLINEAR OPTIMIZATION FOR PROJECT SCHEDULING AND RESOURCE ALLOCATION UNDER UNCERTAINTY A Dssertaton Presented to the Faculty of the Graduate School of Cornell Unversty In Partal Fulfllment of the Requrements
More informationAlternate Approximation of Concave Cost Functions for
Alternate Approxmaton of Concave Cost Functons for Process Desgn and Supply Chan Optmzaton Problems Dego C. Cafaro * and Ignaco E. Grossmann INTEC (UNL CONICET), Güemes 3450, 3000 Santa Fe, ARGENTINA Department
More informationRobust Design of Public Storage Warehouses. Yeming (Yale) Gong EMLYON Business School
Robust Desgn of Publc Storage Warehouses Yemng (Yale) Gong EMLYON Busness School Rene de Koster Rotterdam school of management, Erasmus Unversty Abstract We apply robust optmzaton and revenue management
More informationImplementation of Deutsch's Algorithm Using Mathcad
Implementaton of Deutsch's Algorthm Usng Mathcad Frank Roux The followng s a Mathcad mplementaton of Davd Deutsch's quantum computer prototype as presented on pages  n "Machnes, Logc and Quantum Physcs"
More informationData Broadcast on a MultiSystem Heterogeneous Overlayed Wireless Network *
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 24, 819840 (2008) Data Broadcast on a MultSystem Heterogeneous Overlayed Wreless Network * Department of Computer Scence Natonal Chao Tung Unversty Hsnchu,
More informationA multistart local search heuristic for ship scheduling a computational study
Computers & Operatons Research 34 (2007) 900 917 www.elsever.com/locate/cor A multstart local search heurstc for shp schedulng a computatonal study Ger BrZnmo a,b,, Marelle Chrstansen b, Kjetl Fagerholt
More informationSolutions to the exam in SF2862, June 2009
Solutons to the exam n SF86, June 009 Exercse 1. Ths s a determnstc perodcrevew nventory model. Let n = the number of consdered wees,.e. n = 4 n ths exercse, and r = the demand at wee,.e. r 1 = r = r
More informationRESEARCH ON DUALSHAKER SINE VIBRATION CONTROL. Yaoqi FENG 1, Hanping QIU 1. China Academy of Space Technology (CAST) yaoqi.feng@yahoo.
ICSV4 Carns Australa 9 July, 007 RESEARCH ON DUALSHAKER SINE VIBRATION CONTROL Yaoq FENG, Hanpng QIU Dynamc Test Laboratory, BISEE Chna Academy of Space Technology (CAST) yaoq.feng@yahoo.com Abstract
More informationSupport Vector Machines
Support Vector Machnes Max Wellng Department of Computer Scence Unversty of Toronto 10 Kng s College Road Toronto, M5S 3G5 Canada wellng@cs.toronto.edu Abstract Ths s a note to explan support vector machnes.
More informationDynamic Constrained Economic/Emission Dispatch Scheduling Using Neural Network
Dynamc Constraned Economc/Emsson Dspatch Schedulng Usng Neural Network Fard BENHAMIDA 1, Rachd BELHACHEM 1 1 Department of Electrcal Engneerng, IRECOM Laboratory, Unversty of Djllal Labes, 220 00, Sd Bel
More information"Research Note" APPLICATION OF CHARGE SIMULATION METHOD TO ELECTRIC FIELD CALCULATION IN THE POWER CABLES *
Iranan Journal of Scence & Technology, Transacton B, Engneerng, ol. 30, No. B6, 789794 rnted n The Islamc Republc of Iran, 006 Shraz Unversty "Research Note" ALICATION OF CHARGE SIMULATION METHOD TO ELECTRIC
More informationLogical Development Of Vogel s Approximation Method (LDVAM): An Approach To Find Basic Feasible Solution Of Transportation Problem
INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME, ISSUE, FEBRUARY ISSN 77866 Logcal Development Of Vogel s Approxmaton Method (LD An Approach To Fnd Basc Feasble Soluton Of Transportaton
More informationA Simple Approach to Clustering in Excel
A Smple Approach to Clusterng n Excel Aravnd H Center for Computatonal Engneerng and Networng Amrta Vshwa Vdyapeetham, Combatore, Inda C Rajgopal Center for Computatonal Engneerng and Networng Amrta Vshwa
More informationDEFINING %COMPLETE IN MICROSOFT PROJECT
CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMISP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,
More informationExtending Probabilistic Dynamic Epistemic Logic
Extendng Probablstc Dynamc Epstemc Logc Joshua Sack May 29, 2008 Probablty Space Defnton A probablty space s a tuple (S, A, µ), where 1 S s a set called the sample space. 2 A P(S) s a σalgebra: a set
More informationResearch Article A Time Scheduling Model of Logistics Service Supply Chain with Mass Customized Logistics Service
Hndaw Publshng Corporaton Dscrete Dynamcs n Nature and Socety Volume 01, Artcle ID 48978, 18 pages do:10.1155/01/48978 Research Artcle A Tme Schedulng Model of Logstcs Servce Supply Chan wth Mass Customzed
More informationAn efficient constraint handling methodology for multiobjective evolutionary algorithms
Rev. Fac. Ing. Unv. Antoqua N. 49. pp. 141150. Septembre, 009 An effcent constrant handlng methodology for multobjectve evolutonary algorthms Una metodología efcente para manejo de restrccones en algortmos
More informationEfficient Striping Techniques for Variable Bit Rate Continuous Media File Servers æ
Effcent Strpng Technques for Varable Bt Rate Contnuous Meda Fle Servers æ Prashant J. Shenoy Harrck M. Vn Department of Computer Scence, Department of Computer Scences, Unversty of Massachusetts at Amherst
More informationA GENERAL APPROACH FOR SECURITY MONITORING AND PREVENTIVE CONTROL OF NETWORKS WITH LARGE WIND POWER PRODUCTION
A GENERAL APPROACH FOR SECURITY MONITORING AND PREVENTIVE CONTROL OF NETWORKS WITH LARGE WIND POWER PRODUCTION Helena Vasconcelos INESC Porto hvasconcelos@nescportopt J N Fdalgo INESC Porto and FEUP jfdalgo@nescportopt
More informationDynamic Resource Allocation and Power Management in Virtualized Data Centers
Dynamc Resource Allocaton and Power Management n Vrtualzed Data Centers Rahul Urgaonkar, Ulas C. Kozat, Ken Igarash, Mchael J. Neely urgaonka@usc.edu, {kozat, garash}@docomolabsusa.com, mjneely@usc.edu
More informationTHE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek
HE DISRIBUION OF LOAN PORFOLIO VALUE * Oldrch Alfons Vascek he amount of captal necessary to support a portfolo of debt securtes depends on the probablty dstrbuton of the portfolo loss. Consder a portfolo
More informationgreatest common divisor
4. GCD 1 The greatest common dvsor of two ntegers a and b (not both zero) s the largest nteger whch s a common factor of both a and b. We denote ths number by gcd(a, b), or smply (a, b) when there s no
More informationCredit Limit Optimization (CLO) for Credit Cards
Credt Lmt Optmzaton (CLO) for Credt Cards Vay S. Desa CSCC IX, Ednburgh September 8, 2005 Copyrght 2003, SAS Insttute Inc. All rghts reserved. SAS Propretary Agenda Background Tradtonal approaches to credt
More informationAn ILP Formulation for Task Mapping and Scheduling on Multicore Architectures
An ILP Formulaton for Task Mappng and Schedulng on Multcore Archtectures Yng Y, We Han, Xn Zhao, Ahmet T. Erdogan and Tughrul Arslan Unversty of Ednburgh, The Kng's Buldngs, Mayfeld Road, Ednburgh, EH9
More informationMany etailers providing attended home delivery, especially egrocers, offer narrow delivery time slots to
Vol. 45, No. 3, August 2011, pp. 435 449 ssn 00411655 essn 15265447 11 4503 0435 do 10.1287/trsc.1100.0346 2011 INFORMS Tme Slot Management n Attended Home Delvery Nels Agatz Department of Decson and
More informationFragility Based Rehabilitation Decision Analysis
.171. Fraglty Based Rehabltaton Decson Analyss Cagdas Kafal Graduate Student, School of Cvl and Envronmental Engneerng, Cornell Unversty Research Supervsor: rcea Grgoru, Professor Summary A method s presented
More informationConsidering manufacturing cost and scheduling performance on a CNC turning machine
European Journal of Operatonal Research 77 (2007) 325 343 Producton, Manufacturng and Logstcs Consderng manufacturng cost and schedulng performance on a CNC turnng machne Snan Gurel, M Selm Akturk * Department
More informationRisk Model of LongTerm Production Scheduling in Open Pit Gold Mining
Rsk Model of LongTerm Producton Schedulng n Open Pt Gold Mnng R Halatchev 1 and P Lever 2 ABSTRACT Open pt gold mnng s an mportant sector of the Australan mnng ndustry. It uses large amounts of nvestments,
More informationRate Monotonic (RM) Disadvantages of cyclic. TDDB47 Real Time Systems. Lecture 2: RM & EDF. Prioritybased scheduling. States of a process
Dsadvantages of cyclc TDDB47 Real Tme Systems Manual scheduler constructon Cannot deal wth any runtme changes What happens f we add a task to the set? RealTme Systems Laboratory Department of Computer
More informationPOLYSA: A Polynomial Algorithm for Nonbinary Constraint Satisfaction Problems with and
POLYSA: A Polynomal Algorthm for Nonbnary Constrant Satsfacton Problems wth and Mguel A. Saldo, Federco Barber Dpto. Sstemas Informátcos y Computacón Unversdad Poltécnca de Valenca, Camno de Vera s/n
More informationA Performance Analysis of View Maintenance Techniques for Data Warehouses
A Performance Analyss of Vew Mantenance Technques for Data Warehouses Xng Wang Dell Computer Corporaton Round Roc, Texas Le Gruenwald The nversty of Olahoma School of Computer Scence orman, OK 739 Guangtao
More informationConversion between the vector and raster data structures using Fuzzy Geographical Entities
Converson between the vector and raster data structures usng Fuzzy Geographcal Enttes Cdála Fonte Department of Mathematcs Faculty of Scences and Technology Unversty of Combra, Apartado 38, 3 454 Combra,
More informationGENETIC ALGORITHM FOR PROJECT SCHEDULING AND RESOURCE ALLOCATION UNDER UNCERTAINTY
Int. J. Mech. Eng. & Rob. Res. 03 Fady Safwat et al., 03 Research Paper ISS 78 049 www.jmerr.com Vol., o. 3, July 03 03 IJMERR. All Rghts Reserved GEETIC ALGORITHM FOR PROJECT SCHEDULIG AD RESOURCE ALLOCATIO
More informationINSTITUT FÜR INFORMATIK
INSTITUT FÜR INFORMATIK Schedulng jobs on unform processors revsted Klaus Jansen Chrstna Robene Bercht Nr. 1109 November 2011 ISSN 21926247 CHRISTIANALBRECHTSUNIVERSITÄT ZU KIEL Insttut für Informat
More informationOptimization under uncertainty. Antonio J. Conejo The Ohio State University 2014
Optmzaton under uncertant Antono J. Conejo The Oho State Unverst 2014 Contents Stochastc programmng (SP) Robust optmzaton (RO) Power sstem applcatons A. J. Conejo The Oho State Unverst 2 Stochastc Programmng
More informationThe Stochastic Guaranteed Service Model with Recourse for MultiEchelon Warehouse Management
The Stochastc Guaranteed Servce Model wth Recourse for MultEchelon Warehouse Management Jörg Rambau, Konrad Schade 1 Lehrstuhl für Wrtschaftsmathematk Unverstät Bayreuth Bayreuth, Germany Abstract The
More informationNumber of Levels Cumulative Annual operating Income per year construction costs costs ($) ($) ($) 1 600,000 35,000 100,000 2 2,200,000 60,000 350,000
Problem Set 5 Solutons 1 MIT s consderng buldng a new car park near Kendall Square. o unversty funds are avalable (overhead rates are under pressure and the new faclty would have to pay for tself from
More informationConferencing protocols and Petri net analysis
Conferencng protocols and Petr net analyss E. ANTONIDAKIS Department of Electroncs, Technologcal Educatonal Insttute of Crete, GREECE ena@chana.tecrete.gr Abstract: Durng a computer conference, users desre
More informationStudy on Model of Risks Assessment of Standard Operation in Rural Power Network
Study on Model of Rsks Assessment of Standard Operaton n Rural Power Network Qngj L 1, Tao Yang 2 1 Qngj L, College of Informaton and Electrcal Engneerng, Shenyang Agrculture Unversty, Shenyang 110866,
More informationJ. Parallel Distrib. Comput.
J. Parallel Dstrb. Comput. 71 (2011) 62 76 Contents lsts avalable at ScenceDrect J. Parallel Dstrb. Comput. journal homepage: www.elsever.com/locate/jpdc Optmzng server placement n dstrbuted systems n
More informationLecture 3: Force of Interest, Real Interest Rate, Annuity
Lecture 3: Force of Interest, Real Interest Rate, Annuty Goals: Study contnuous compoundng and force of nterest Dscuss real nterest rate Learn annutymmedate, and ts present value Study annutydue, and
More informationSolving Factored MDPs with Continuous and Discrete Variables
Solvng Factored MPs wth Contnuous and screte Varables Carlos Guestrn Berkeley Research Center Intel Corporaton Mlos Hauskrecht epartment of Computer Scence Unversty of Pttsburgh Branslav Kveton Intellgent
More informationRevenue Management for a Multiclass SingleServer Queue via a Fluid Model Analysis
OPERATIONS RESEARCH Vol. 54, No. 5, September October 6, pp. 94 93 ssn 3364X essn 565463 6 545 94 nforms do.87/opre.6.35 6 INFORMS Revenue Management for a Multclass SngleServer Queue va a Flud Model
More informationOptimal Map Reduce Job Capacity Allocation in Cloud Systems
Optmal Map Reduce Job Capacty Allocaton n Cloud Systems Marzeh Malemajd Sharf Unversty of Technology, Iran malemajd@ce.sharf.edu Danlo Ardagna Poltecnco d Mlano, Italy danlo.ardagna@polm.t Mchele Cavotta
More informationIDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS
IDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS Chrs Deeley* Last revsed: September 22, 200 * Chrs Deeley s a Senor Lecturer n the School of Accountng, Charles Sturt Unversty,
More informationCan Auto Liability Insurance Purchases Signal Risk Attitude?
Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? ChuShu L Department of Internatonal Busness, Asa Unversty, Tawan ShengChang
More informationRecurrence. 1 Definitions and main statements
Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.
More informationRetailers must constantly strive for excellence in operations; extremely narrow profit margins
Managng a Retaler s Shelf Space, Inventory, and Transportaton Gerard Cachon 300 SH/DH, The Wharton School, Unversty of Pennsylvana, Phladelpha, Pennsylvana 90 cachon@wharton.upenn.edu http://opm.wharton.upenn.edu/cachon/
More informationOutsourcing inventory management decisions in healthcare: Models and application
European Journal of Operatonal Research 154 (24) 271 29 O.R. Applcatons Outsourcng nventory management decsons n healthcare: Models and applcaton www.elsever.com/locate/dsw Lawrence Ncholson a, Asoo J.
More informationAbstract. 260 Business Intelligence Journal July IDENTIFICATION OF DEMAND THROUGH STATISTICAL DISTRIBUTION MODELING FOR IMPROVED DEMAND FORECASTING
260 Busness Intellgence Journal July IDENTIFICATION OF DEMAND THROUGH STATISTICAL DISTRIBUTION MODELING FOR IMPROVED DEMAND FORECASTING Murphy Choy Mchelle L.F. Cheong School of Informaton Systems, Sngapore
More informationTo manage leave, meeting institutional requirements and treating individual staff members fairly and consistently.
Corporate Polces & Procedures Human Resources  Document CPP216 Leave Management Frst Produced: Current Verson: Past Revsons: Revew Cycle: Apples From: 09/09/09 26/10/12 09/09/09 3 years Immedately Authorsaton:
More information