Managing Cycle Inventories. Matching Supply and Demand

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Managing Cycle Inventories. Matching Supply and Demand"

Transcription

1 Managng Cycle Inventores Matchng Supply and Demand 1

2 Outlne Why to hold cycle nventores? Economes of scale to reduce fxed costs per unt. Jont fxed costs for multple products Long term quantty dscounts Short term quantty dscounts: Promotons 2

3 Role of Inventory n the Supply Chan Overstockng: Amount avalable exceeds demand Lqudaton, Obsolescence, Holdng Understockng: Demand exceeds amount avalable Lost margn and future sales Goal: Matchng supply and demand 3

4 Batch or Lot sze Batch = Lot = quantty of products bought / produced together But not smultaneously, snce producton can not be smultaneous Q: Lot sze. R: Demand per tme. Consder sales at a Jean s retaler wth demand of 10 jeans per day and an order sze of 100 jeans. Q=100. R=10/day. Inventory Q R 0 Order Q/R Order Cycle Order Tme 4

5 Demand affected by vsblty Demand s hgher when the nventory s hgher and s smaller when the nventory s smaller. When I am buyng coffee, t s often not fresh. Why? Fresh coffee s consumed fast but stale coffee s not. Or because: Inventory Coffee becomes stale Store owner does not prepare new coffee Expects that coffee wll fnsh n the next 2 hours Hours I arrve at the coffee shop 5

6 Batch or Lot sze Cycle nventory=average nventory held durng the cycle =Q/2=50 jean pars Average flow tme Remember Lttle s law =(Average nventory)/(average flow rate)=(q/2)/r=5 days Long flow tmes make a company vulnerable to product / technology changes Lower cycle nventory decreases workng (operatng) captal needs and space requrements for nventory Then, why not to set Q as low as possble? 6

7 Why to order n lots? Fxed orderng cost: S Increase the lot sze to decrease the fxed orderng cost per unt Materal cost per unt: C Holdng cost: Cost of carryng 1 unt n the nventory: H H=h.C h: carryng $1 n the nventory > nterest rate Lot sze s chosen by tradng off holdng costs aganst fxed orderng costs (and sometmes materal costs). Where to shop from: Fxed cost (drvng) Materal cost Convenence store low HIGH Sam s club HIGH low 7

8 Economc Order Quantty - EOQ Annual TC = carryng + cost Annual orderng cost + Purchasng cost Q 2 hc R TC = + Q S + CR Total cost s smple functon of the lot sze Q. Note that we can drop the last term, t s not affected by the choce of Q. 8

9 Cost Mnmzaton Goal Annual Cost The Total-Cost Curve s U-Shaped Q R TC = hc + S + CR 2 Q Holdng costs Orderng Costs Q (optmal order quantty) Order Quantty (Q) 9

10 Dervng the EOQ Usng calculus, we take the dervatve of the total cost functon and set the dervatve equal to zero and solve for Q. Total cost curve s convex.e. curvature s upward so we obtan the mnmzer. EOQ 2RS EOQ 2S = T = = n= R = hc R RhC EOQ RhC 2S T: Reorder nterval length = EOQ/R. n: Orderng frequency: number of orders per unt tme = R/EOQ. The total cost curve reaches ts mnmum where the nventory carryng and orderng costs are equal. Total cost( Q = EOQ ) = 2RShC 10

11 EOQ example Demand, R = 12,000 computers per year. Unt cost, C = $500 Holdng cost, h = 0.2. Fxed cost, S = $4,000/order. Fnd EOQ, Cycle Inventory, Average Flow Tme, Optmal Reorder Interval and Optmal Orderng Frequency. Q = , say 980 computers Cycle nventory = Q/2 = 490 unts Average Flow Tme = Q/(2R) = 0.49 month Optmal Reorder nterval, T = year = 0.98 month Optmal orderng frequency, n=12.24 orders per year. 11

12 Key Ponts from Batchng In decdng the optmal lot sze the trade off s between setup (order) cost and holdng cost. If demand ncreases by a factor of 4, t s optmal to ncrease batch sze by a factor of 2 and produce (order) twce as often. Cycle nventory (n unts) doubles. Cycle nventory (n days of demand) halves. If lot sze s to be reduced, one has to reduce fxed order cost. To reduce lot sze by a factor of 2, order cost has to be reduced by a factor of 4. Ths s what JIT strves to do. 12

13 Strateges for reducng fxed costs In producton Standardzaton / dedcated Smplfcaton Set up out of the producton lne In delvery Thrd party logstcs Aggregatng multple products n a sngle order» Temporal, geographc aggregaton Varous truck szes, dffcult to manage 13

14 Example: Lot Szng wth Multple Products Demand per year R L = 12,000; R M = 1,200; R H = 120 Common transportaton cost per delvery, S = $4,000 Product specfc order cost per product n each delvery s L = $1,000; s M = $1,000; s H = $1,000 Holdng cost, h = 0.2 Unt cost C L = $1,000; C M = $1,000; C H = $1,000 14

15 Delvery Optons No Aggregaton: Each product ordered separately Complete Aggregaton: All products delvered on each truck Talored Aggregaton: Selected subsets of products on each truck 15

16 No Aggregaton: Order each product ndependently Ltepro Medpro Heavypro Demand per year 12,000 1, Fxed cost / order $5,000 $5,000 $5,000 Optmal order sze 1, Order frequency 11.0 / year 3.5 / year 1.1 / year Annual cost $109,544 $34,642 $0,954 Total cost = $155,140 16

17 Complete Aggregaton: Order all products jontly Total orderng cost S*=S+s L +s M +s H = $7,000 n: common orderng frequency Annual orderng cost = n S* Total holdng cost: Total cost: TC ( n) n * = L Rn hc R M n hc R H n hc L + M * h = S n + n R C + R C + 2 R C ( ) ( + + ) h R C R C R C 2 S L L M M H H L L M M H H * H 17

18 Complete Aggregaton: Order all products jontly Ltepro Medpro Heavypro Demand per year 12,000 1, Order frequency 9.75/year 9.75/year 9.75/year Optmal order sze 1, Annual holdng cost $61,512 $6,151 $615 Annual order cost = 9.75 $7,000 = $68,250 Annual total cost = $136,528 Orderng hgh and low volume tems at the same frequency cannot be a good dea. 18

19 Talored Aggregaton: Orderng Selected Subsets Example Orders may look lke (L,M); (L,H); (L,M); (L,H). Most frequently ordered product: L M and H are ordered n every other delvery. We can assocate fxed order cost S wth product L because t s ordered every tme there s an order. Products other than L are assocated only wth ther ncremental order costs (s values). An Algorthm: Step 1: Identfy most frequently ordered product Step 2: Identfy frequency of other products as a relatve multple Step 3: Recalculate orderng frequency of most frequently ordered product Step 4: Identfy orderng frequency of all products 19

20 Talored Aggregaton: Orderng Selected Subsets s the generc ndex for products, s L, M or H. Step 1: Fnd most frequently ordered tem: n = hc R 2( S + s ) The frequency of the most frequently ordered tem wll be modfed later. Ths s an approxmate computaton. Step 2: Relatve order frequency of other tems, m n hc R n = m = s 2 n m are relatve order frequences, they must be ntegers. n = max{ n } 20

21 Talored Aggregaton: Orderng Selected Subsets Step 3: Recompute the frequency of the most frequently ordered tem. Ths tem s ordered n every order whereas others are ordered n every m orders. The average fxed orderng cost s: n * = S + s m Annual orderng cost Annual holdng cost 2 S + R m hc s m = s = n( S + ) m R 2n / m hc dfferent than (10.8) on p.263 of Chopra 21

22 Talored Aggregaton: Orderng Selected Subsets Step 4: Recompute the orderng frequency n of other products: n n = m Total Annual orderng cost: ns+n H s H +n M s M +n L s L Total Holdng cost: R L n hc R M R L + hc M + 2 2n 2n L M H H hc H 22

23 Step 1: n L = Talored Aggregaton: Orderng Selected Subsets hclrl 2( S + s ) L = 11, n = 3.5, n = 1.1 n = max{ n } = M H 11 Step 2: hcm RM n nm = nh mm = mh sm = 7.7, = 2.4; = 2, = 5 2 nm Item L s ordered most frequently. Every other L order contans one M order. Every 5 L orders contan one H order. At ths step we only now relatve frequences, not the actual frequences. 23

24 Step 3: Step 4: Talored Aggregaton: Orderng Selected Subsets Total orderng cost: n M ns+n H s H +n M s M +n L s L =11.47(4000)+11.47(1000)+5.73(1000)+2.29(1000) Total holdng cost = n m * M = 5.73 R L n h C R M R L + h C M n 2 n L n * = M h C R m s 2 ( S + ) m = H H h C = ( ) (. ) 2 ( ) (. ) 2 ( ) (. ) n H H = n m * H =

25 Talored Aggregaton: Order selected subsets Ltepro Medpro Heavypro Demand per year 12,000 1, Order frequency 11.47/year 5.73/year 2.29/year Optmal order sze Annual holdng cost $52,810 $10,470 $2,630 Annual order cost = $65,370 Total annual cost = $130,650 25

26 Lessons From Aggregaton Informaton technology can decrease product specfc orderng costs. Aggregaton allows frm to lower lot sze wthout ncreasng cost Order frequences wthout aggregaton and wth talored aggregaton» (11; 3.5; 1.1) vs. (11.47; 5.73; 2.29) Complete aggregaton s effectve f product specfc fxed cost s a small fracton of jont fxed cost Talored aggregaton s effectve f product specfc fxed cost s large fracton of jont fxed cost 26

27 Quantty Dscounts Lot sze based All unts Margnal unt Volume based How should buyer react? What are approprate dscountng schemes? 27

28 All-Unt Quantty Dscounts Cost/Unt Total Materal Cost $3 $2.96 $2.92 5,000 10,000 q 1 q 2 Order Quantty 5,000 10,000 Order Quantty 28

29 All-Unt Quantty Dscounts Fnd EOQ for prce n range q to q +1 If q EOQ < q +1,» Canddate n ths range s EOQ, evaluate cost of orderng EOQ If EOQ < q,» Canddate n ths range s q, evaluate cost of orderng q If EOQ q +1,» Canddate n ths range s q +1, evaluate cost of orderng q +1 Fnd mnmum cost over all canddates 29

30 Fndng Q wth all unts dscount Total Cost Q = 1 2RS hc 1 Q = 2 2RS hc 2 Q = 3 2RS hc 3 Quantty 30

31 Fndng Q wth all unts dscount Total Cost Q = 1 2RS hc 1 2 Q = 3 2RS hc 3 Q = 2 2RS hc 2 Quantty 31

32 Fndng Q wth all unts dscount Total Cost 2 1 Quantty 32

33 Margnal Unt Quantty Dscounts Cost/Unt Total Materal Cost c 0 c 1 c 2 $3 $2.96 $2.92 V 2 V 1 5,000 10,000 q 1 q 2 Order Quantty 5,000 10,000 Order Quantty 33

34 Margnal Unt Quantty Dscounts V = C ost of buyng exactly q. V = 0. V = c ( q q ) + c ( q q ) c ( q q ) If q Q q + 1 A nnual order cost = A nnual holdng cost = T otal cost( Q ) Q, E O Q R Q S A nnual m ateral cost = For range, h 2 R Q V Q q c ( V + ( Q q ) c ) 0 ( + ( ) ) R = ( ) Q S c h R Q V q c = 2 = ( + ) 2 R S V q c hc 0 34

35 Margnal Unt Quantty Dscounts V = C ost of buyng exactly q. V = 0. V = c ( q q ) + c ( q q ) c ( q q ) If q Q q + 1 A nnual order cost = A nnual holdng cost = T otal cost( Q ) Q, E O Q R Q S A nnual m ateral cost = For range, h 2 R Q V Q q c ( V + ( Q q ) c ) 0 ( + ( ) ) R = ( ) Q S c h R Q V q c = 2 = ( + ) 2 R S V q c hc 0 35

36 Margnal-Unt Quantty Dscounts Fnd EOQ for prce n range q to q +1 If q EOQ < q +1,» Canddate n ths range s EOQ, evaluate cost of orderng EOQ If EOQ < q,» Canddate n ths range s q, evaluate cost of orderng q If EOQ q +1,» Canddate n ths range s q +1, evaluate cost of orderng q +1 Fnd mnmum cost over all canddates 36

37 Margnal Unt Quantty Dscounts Total cost EOQ 1 EOQ 3 q 1 q 2 Lot sze Compare ths total cost graph wth that of all unt quantty dscounts. Here the cost graph s contnuous whereas that of all unt quantty dscounts has breaks. 37

38 Margnal Unt Quantty Dscounts Total cost EOQ 1 EOQ 2 EOQ 3 q 1 q 2 Lot sze 38

39 Why Quantty Dscounts? The lot sze that mnmzes retalers cost does not necessarly mnmze suppler and retaler s cost together. Coordnaton n the supply chan Wll suppler and retaler be wllng to operate wth the same order szes, frequences, prces, etc.? How to ensure ths wllngness? Va contracts. Quantty dscounts gven by a suppler to a retaler can motvate the retaler to order as the suppler wshes. 39

40 Coordnaton for Commodty Products: Suppler and Retaler Coordnaton Consder a suppler S and retaler R par R = 120,000 bottles/year S R = $100, h R = 0.2, C R = $3 S S = $250, h S = 0.2, C S = $2 Retaler s optmal lot sze = 6,324 bottles Retaler s annual orderng and holdng cost = $3,795; If Suppler uses the retaler s lot sze, Suppler s annual orderng and holdng cost = $6,009 Total annual supply chan cost = $9,804 40

41 Coordnaton for Commodty Products What can the suppler do to decrease supply chan costs? Combne the suppler and the retaler Coordnated lot sze: 9,165= 2R( SS + SR) h( C + C ) Retaler cost = $4,059; Suppler cost = $5,106; Supply chan cost = $9,165. $639 less than wthout coordnaton. S R ChooseQ R RetalerCost(Q Coordnaton Savngs by MnmzeRetalerCost(Q), then R ) + SupplerCost(Q = R ) MnmzeRetalerSupplerCost (Q) { RetalerCost(Q ) + SupplerCost(Q )} R Q R - MnmzeRetalerSupplerCost (Q) Q 41

42 Coordnaton va Prcng by the Suppler Effectve prcng schemes All unt quantty dscount» $3 for lots below 9,165» $ for lots of 9,165 or more What s suppler s and retaler s cost wth the all unt quantty dscount scheme? Not the same as before. Who gets the savngs due to coordnaton? Pass some fxed cost to retaler (enough that the retaler rases order sze from 6,324 to 9,165) 42

43 Quantty Dscounts for a Frm wth Market Power (Prce dependent demand) No nventory related costs Demand curve 360,000-60,000p Retaler dscounts to manpulate the demand Retaler chooses the market prce p, manufacturer chooses the sales prce C R to the retaler. Manufacturng cost C M =$2/unt Manufacturer Manufacturer s Prce, C R demand Retaler Market Prce, p demand 43

44 Quantty Dscounts for a Frm wth Market Power Retaler proft=(p-c R )(360,000-60,000p) Manufacturer proft=(c R -C M ) (360,000-60,000p) Note C M =$2 If each optmzes ts own proft: Manufacturer assumes that p= C R Sets C R =$4 to maxmze (C R -2) (360,000-60,000C R ) Retaler takes C R =$4 Sets p=$5 to maxmze (p-4)(360,000-60,000p) Q=60,000. Manufacturer and retaler profts are $120K and $60K respectvely. Total SC proft s $180K. Observe that f p=$4, total SC profts are (4-2)120K=$240K. How to capture =$60K? 44

45 Two Part Tarffs and Volume Dscounts Desgn a two-part tarff that acheves the coordnated soluton. Desgn a volume dscount scheme that acheves the coordnated soluton. Impact of nventory costs Pass on some fxed costs wth above prcng 45

46 Two part tarff to capture all the profts Manufacturer sells each unt at $2 but adds a fxed charge of $180K. Retaler proft=(p-2)(360,000-60,000p)-180,00 Retaler sets p=$4 and obtans a proft of $60K Q=120,000 Manufacturer makes money only from the fxed charge whch s $180K. Total proft s $240K. Manufacturer makes $60K more. Retaler s proft does not change. Does the retaler complan? Splt of profts depend on barganng power Sgnalng strength Other alternatve buyers and sellers Prevous hstory of negotatons; credblty (of threats) Mechansm for conflct resoluton: teratve or at once 46

47 All unts dscount to capture all profts Suppler apples all unt quantty dscount: If 0<Q<120,000, C R =$4 Else C R =$3.5 If Q<120,000, we already worked out that p=$5 and Q=60,000. And the total proft s $180,000. If Q>=120,000, the retaler chooses p=$4.75 whch yelds Q=75,000 and s outsde the range. Then Q=120,000 and p=$4. Retaler proft=(4-3.5)120,000=60,000 Manufacturer proft=(3.5-2)120,000=180,000 Total SC profts are agan $240K. Manufacturer dscounts to manpulate the market demand va retaler s prcng. 47

48 Lessons From Dscountng Schemes Lot sze based dscounts ncrease lot sze and cycle nventory n the supply chan Lot sze based dscounts are justfed to acheve coordnaton for commodty products Volume based dscounts are more effectve n general especally n keepng cycle nventory low End of the horzon panc to get the dscount: Hockey stck phenomenon Volume based dscounts are better over rollng horzon 48

49 Short Term Dscountng Why? To ncrease sales, Ford To push nventory down the SC, Campbell To compete, Peps Leads to a hgh lot sze and cycle nventory because of strong forward buyng 49

50 Weekly Shpments of Chcken Noodle Soup. Forward Buyng Shpments Consumpton Dscountng 50

51 Short Term Promotons Promoton happens only once, Optmal promoton order quantty Q d s a multple of EOQ Quantty Q d EOQ Tme 51

52 Short Term Dscountng C: Normal unt cost d: Short term dscount R: Annual demand h: Cost of holdng $1 per year Q d : Short term (once) order quantty Q d d R C EOQ = + ( C - d ) h C - d Forward buy = Q d - Q* 52

53 Short Term Dscounts: Forward buyng. Ex 10.8 on p.280 Normal order sze, EOQ = 6,324 bottles Normal cost, C = $3 per bottle Dscount per tube, d = $0.15 Annual demand, R = 120,000 Holdng cost, h = 0.2 Q d =38,236 Forward buy =38,236-6,324=31,912 Forward buy s fve tmes the EOQ, ths s a lot of nventory! 53

54 Suppler s Promoton passed through to consumers Demand curve at retaler: 300,000-60,000p Normal suppler prce, C R = $3.00 Retaler proft=(p-3)(300,000-60,000p) Optmal retal prce = $4.00 Customer demand = 60,000 Suppler s promoton dscount = $0.15, C R = $2.85 Retaler proft=(p-2.85)(300,000-60,000p) Optmal retal prce = $3.925 Customer demand = 64,500 Retaler only passes through half the promoton dscount and demand ncreases by only 7.5% 54

55 Avodng Problems wth Promotons Goal s to dscourage retaler from forward buyng n the supply chan Counter measures Sell-through: Scan based promotons» Retaler gets the dscount for the tems sold durng the promoton Customer coupons; Dscounts avalable when the retaler returns the coupons to the suppler. The coupons are handed out to consumers by the suppler. Retaler realzes the dscounts only after the consumer s purchase. 55

56 Strategc Levers to Reduce Lot Szes Wthout Hurtng Costs Cycle Inventory Reducton Reduce transfer and producton lot szes» Aggregate the fxed costs across multple products, supply ponts, or delvery ponts E.g. Talored aggregaton Are quantty dscounts consstent wth manufacturng and logstcs operatons?» Volume dscounts on rollng horzon» Two-part tarff Are trade promotons essental?» Base on sell-thru (to consumer) rather than sell-n (to retaler) 56

57 Inventory Cost Estmaton Holdng cost Cost of captal Spolage cost, semconductor product lose 2% of ther value every week they stay n the nventory Occupancy cost Orderng cost Buyer tme Transportaton cost Recevng/handlng cost Handlng s generally Orderng cost rather than Holdng cost 57

58 Summary EOQ costs and quantty Talored aggregaton to reduce fxed costs Prce dscountng to coordnate the supply chan Short term promotons 58

Inventory Aggregation and Discounting

Inventory Aggregation and Discounting Inventory Aggregaton and Dscountng Matchng Supply and Demand utdallas.edu/~metn 1 Outlne Jont fxed costs for multple products Long term quantty dscounts utdallas.edu/~metn Example: Lot Szng wth Multple

More information

Power-of-Two Policies for Single- Warehouse Multi-Retailer Inventory Systems with Order Frequency Discounts

Power-of-Two Policies for Single- Warehouse Multi-Retailer Inventory Systems with Order Frequency Discounts Power-of-wo Polces for Sngle- Warehouse Mult-Retaler Inventory Systems wth Order Frequency Dscounts José A. Ventura Pennsylvana State Unversty (USA) Yale. Herer echnon Israel Insttute of echnology (Israel)

More information

Inventory - Prerequisite

Inventory - Prerequisite Inventory - Prerequisite Economic Order Quantity 1 Outline Why to hold cycle inventories? Economies of scale to reduce fixed costs per unit. 2 Role of Inventory in the Supply Chain Overstocking: Amount

More information

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy 4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.

More information

Retailers must constantly strive for excellence in operations; extremely narrow profit margins

Retailers 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 information

Coordinating Supply Chains with Simple Pricing Schemes: TheRoleofVendorManagedInventories

Coordinating Supply Chains with Simple Pricing Schemes: TheRoleofVendorManagedInventories Coordnatng Supply Chans wth Smple Prcng Schemes: TheRoleofVendorManagedInventores Fernando Bernsten Fangruo Chen Aw Federgruen The Fuqua School of Busness Graduate School of Busness Graduate School of

More information

Figure 1. Inventory Level vs. Time - EOQ Problem

Figure 1. Inventory Level vs. Time - EOQ Problem IEOR 54 Sprng, 009 rof Leahman otes on Eonom Lot Shedulng and Eonom Rotaton Cyles he Eonom Order Quantty (EOQ) Consder an nventory tem n solaton wth demand rate, holdng ost h per unt per unt tme, and replenshment

More information

Time Value of Money. Types of Interest. Compounding and Discounting Single Sums. Page 1. Ch. 6 - The Time Value of Money. The Time Value of Money

Time Value of Money. Types of Interest. Compounding and Discounting Single Sums. Page 1. Ch. 6 - The Time Value of Money. The Time Value of Money Ch. 6 - The Tme Value of Money Tme Value of Money The Interest Rate Smple Interest Compound Interest Amortzng a Loan FIN21- Ahmed Y, Dasht TIME VALUE OF MONEY OR DISCOUNTED CASH FLOW ANALYSIS Very Important

More information

The Greedy Method. Introduction. 0/1 Knapsack Problem

The 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 information

DEVELOPMENT & IMPLEMENTATION OF SUPPLY CHAIN MANAGEMENT FOCUSING ON PROCUREMENT PROCESSES & SUPPLIERS 1.

DEVELOPMENT & IMPLEMENTATION OF SUPPLY CHAIN MANAGEMENT FOCUSING ON PROCUREMENT PROCESSES & SUPPLIERS 1. 1. Claudu V. KIFOR, 2. Amela BUCUR, 3. Muhammad Arsalan FAROOQ DEVELOPMENT & IMPLEMENTATION OF SUPPLY CHAIN MANAGEMENT FOCUSING ON PROCUREMENT PROCESSES & SUPPLIERS 1. LUCIAN BLAGA UNIVERSITY SIBIU, RESEARCH

More information

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt.

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt. Chapter 9 Revew problems 9.1 Interest rate measurement Example 9.1. Fund A accumulates at a smple nterest rate of 10%. Fund B accumulates at a smple dscount rate of 5%. Fnd the pont n tme at whch the forces

More information

Chapter 7: Answers to Questions and Problems

Chapter 7: Answers to Questions and Problems 19. Based on the nformaton contaned n Table 7-3 of the text, the food and apparel ndustres are most compettve and therefore probably represent the best match for the expertse of these managers. Chapter

More information

reduce competition increase market power cost savings economies of scale and scope cost savings Oliver Williamson: the efficiency defense

reduce competition increase market power cost savings economies of scale and scope cost savings Oliver Williamson: the efficiency defense Mergers Why merge? reduce competton ncrease market power cost savngs economes of scale and scope Why allow mergers? cost savngs Olver Wllamson: the effcency defense Merger wthout cost savngs Before merger:

More information

In some supply chains, materials are ordered periodically according to local information. This paper investigates

In some supply chains, materials are ordered periodically according to local information. This paper investigates MANUFACTURING & SRVIC OPRATIONS MANAGMNT Vol. 12, No. 3, Summer 2010, pp. 430 448 ssn 1523-4614 essn 1526-5498 10 1203 0430 nforms do 10.1287/msom.1090.0277 2010 INFORMS Improvng Supply Chan Performance:

More information

The OC Curve of Attribute Acceptance Plans

The 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 information

Finite Math Chapter 10: Study Guide and Solution to Problems

Finite Math Chapter 10: Study Guide and Solution to Problems Fnte Math Chapter 10: Study Gude and Soluton to Problems Basc Formulas and Concepts 10.1 Interest Basc Concepts Interest A fee a bank pays you for money you depost nto a savngs account. Prncpal P The amount

More information

Lecture 3: Force of Interest, Real Interest Rate, Annuity

Lecture 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 annuty-mmedate, and ts present value Study annuty-due, and

More information

Number 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

Number 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 information

An MILP model for planning of batch plants operating in a campaign-mode

An MILP model for planning of batch plants operating in a campaign-mode An MILP model for plannng of batch plants operatng n a campagn-mode Yanna Fumero Insttuto de Desarrollo y Dseño CONICET UTN yfumero@santafe-concet.gov.ar Gabrela Corsano Insttuto de Desarrollo y Dseño

More information

Feasibility of Using Discriminate Pricing Schemes for Energy Trading in Smart Grid

Feasibility of Using Discriminate Pricing Schemes for Energy Trading in Smart Grid Feasblty of Usng Dscrmnate Prcng Schemes for Energy Tradng n Smart Grd Wayes Tushar, Chau Yuen, Bo Cha, Davd B. Smth, and H. Vncent Poor Sngapore Unversty of Technology and Desgn, Sngapore 138682. Emal:

More information

Trade Adjustment and Productivity in Large Crises. Online Appendix May 2013. Appendix A: Derivation of Equations for Productivity

Trade Adjustment and Productivity in Large Crises. Online Appendix May 2013. Appendix A: Derivation of Equations for Productivity Trade Adjustment Productvty n Large Crses Gta Gopnath Department of Economcs Harvard Unversty NBER Brent Neman Booth School of Busness Unversty of Chcago NBER Onlne Appendx May 2013 Appendx A: Dervaton

More information

Moving Beyond Open Markets for Water Quality Trading: The Gains from Structured Bilateral Trades

Moving Beyond Open Markets for Water Quality Trading: The Gains from Structured Bilateral Trades Movng Beyond Open Markets for Water Qualty Tradng: The Gans from Structured Blateral Trades Tanl Zhao Yukako Sado Rchard N. Bosvert Gregory L. Poe Cornell Unversty EAERE Preconference on Water Economcs

More information

Fault tolerance in cloud technologies presented as a service

Fault 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 information

2008/8. An integrated model for warehouse and inventory planning. Géraldine Strack and Yves Pochet

2008/8. 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 B-1348 Louvan-la-Neuve, Belgum. Tel (32 10) 47 43 04 Fax (32 10) 47 43 01 E-mal: corestat-lbrary@uclouvan.be

More information

What is Candidate Sampling

What 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 information

Unconstrained EOQ Analysis of a Furniture Mall - A Case Study of Bellwether Furniture

Unconstrained EOQ Analysis of a Furniture Mall - A Case Study of Bellwether Furniture Internatonal Journal of Scentfc Research n omputer Scence and Engneerng Research Paper Volume-, Issue-5 ISSN: 30-7639 Unconstraned EOQ Analyss of a Furnture Mall - A ase Study of Bellwether Furnture Manmnder

More information

Omega 39 (2011) 313 322. Contents lists available at ScienceDirect. Omega. journal homepage: www.elsevier.com/locate/omega

Omega 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 information

Solutions to the exam in SF2862, June 2009

Solutions to the exam in SF2862, June 2009 Solutons to the exam n SF86, June 009 Exercse 1. Ths s a determnstc perodc-revew 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 information

An Alternative Way to Measure Private Equity Performance

An 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 information

On the Optimal Control of a Cascade of Hydro-Electric Power Stations

On the Optimal Control of a Cascade of Hydro-Electric Power Stations On the Optmal Control of a Cascade of Hydro-Electrc 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 information

SUPPLIER FINANCING AND STOCK MANAGEMENT. A JOINT VIEW.

SUPPLIER 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: 976-76-10-00

More information

Bond futures. Bond futures contracts are futures contracts that allow investor to buy in the

Bond futures. Bond futures contracts are futures contracts that allow investor to buy in the Bond futures INRODUCION Bond futures contracts are futures contracts that allow nvestor to buy n the future a theoretcal government notonal bond at a gven prce at a specfc date n a gven quantty. Compared

More information

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic

Institute 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 information

Game theory in Oligopoly

Game theory in Oligopoly Game theory n Olgopoly Prof. Marx Boopath, Nkolaj Sujen. Abstract The game theory technques are used to fnd the equlbrum of a market. Game theory refers to the ways n whch strategc nteractons among economc

More information

IS-LM Model 1 C' dy = di

IS-LM Model 1 C' dy = di - odel Solow Assumptons - demand rrelevant n long run; assumes economy s operatng at potental GDP; concerned wth growth - Assumptons - supply s rrelevant n short run; assumes economy s operatng below potental

More information

THE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek

THE 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 information

Lecture 3: Annuity. Study annuities whose payments form a geometric progression or a arithmetic progression.

Lecture 3: Annuity. Study annuities whose payments form a geometric progression or a arithmetic progression. Lecture 3: Annuty Goals: Learn contnuous annuty and perpetuty. Study annutes whose payments form a geometrc progresson or a arthmetc progresson. Dscuss yeld rates. Introduce Amortzaton Suggested Textbook

More information

Week 6 Market Failure due to Externalities

Week 6 Market Failure due to Externalities Week 6 Market Falure due to Externaltes 1. Externaltes n externalty exsts when the acton of one agent unavodably affects the welfare of another agent. The affected agent may be a consumer, gvng rse to

More information

Hedging Interest-Rate Risk with Duration

Hedging Interest-Rate Risk with Duration FIXED-INCOME SECURITIES Chapter 5 Hedgng Interest-Rate Rsk wth Duraton Outlne Prcng and Hedgng Prcng certan cash-flows Interest rate rsk Hedgng prncples Duraton-Based Hedgng Technques Defnton of duraton

More information

Formulating & Solving Integer Problems Chapter 11 289

Formulating & Solving Integer Problems Chapter 11 289 Formulatng & Solvng Integer Problems Chapter 11 289 The Optonal Stop TSP If we drop the requrement that every stop must be vsted, we then get the optonal stop TSP. Ths mght correspond to a ob sequencng

More information

Multiple discount and forward curves

Multiple discount and forward curves Multple dscount and forward curves TopQuants presentaton 21 ovember 2012 Ton Broekhuzen, Head Market Rsk and Basel coordnator, IBC Ths presentaton reflects personal vews and not necessarly the vews of

More information

Production. 2. Y is closed A set is closed if it contains its boundary. We need this for the solution existence in the profit maximization problem.

Production. 2. Y is closed A set is closed if it contains its boundary. We need this for the solution existence in the profit maximization problem. Producer Theory Producton ASSUMPTION 2.1 Propertes of the Producton Set The producton set Y satsfes the followng propertes 1. Y s non-empty If Y s empty, we have nothng to talk about 2. Y s closed A set

More information

Chapter 15 Debt and Taxes

Chapter 15 Debt and Taxes hapter 15 Debt and Taxes 15-1. Pelamed Pharmaceutcals has EBIT of $325 mllon n 2006. In addton, Pelamed has nterest expenses of $125 mllon and a corporate tax rate of 40%. a. What s Pelamed s 2006 net

More information

Marginal Revenue-Based Capacity Management Models and Benchmark 1

Marginal Revenue-Based Capacity Management Models and Benchmark 1 Margnal Revenue-Based Capacty Management Models and Benchmark 1 Qwen Wang 2 Guanghua School of Management, Pekng Unversty Sherry Xaoyun Sun 3 Ctgroup ABSTRACT To effcently meet customer requrements, a

More information

Optimization of network mesh topologies and link capacities for congestion relief

Optimization 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 * E-mal: rwddv@pu.ac.za

More information

PAS: A Packet Accounting System to Limit the Effects of DoS & DDoS. Debish Fesehaye & Klara Naherstedt University of Illinois-Urbana Champaign

PAS: A Packet Accounting System to Limit the Effects of DoS & DDoS. Debish Fesehaye & Klara Naherstedt University of Illinois-Urbana Champaign PAS: A Packet Accountng System to Lmt the Effects of DoS & DDoS Debsh Fesehaye & Klara Naherstedt Unversty of Illnos-Urbana Champagn DoS and DDoS DDoS attacks are ncreasng threats to our dgtal world. Exstng

More information

Construction Rules for Morningstar Canada Target Dividend Index SM

Construction Rules for Morningstar Canada Target Dividend Index SM Constructon Rules for Mornngstar Canada Target Dvdend Index SM Mornngstar Methodology Paper October 2014 Verson 1.2 2014 Mornngstar, Inc. All rghts reserved. The nformaton n ths document s the property

More information

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.

More information

Feature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College

Feature 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 information

Using Series to Analyze Financial Situations: Present Value

Using Series to Analyze Financial Situations: Present Value 2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated

More information

Chapter 4 ECONOMIC DISPATCH AND UNIT COMMITMENT

Chapter 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 information

The Short-term and Long-term Market

The Short-term and Long-term Market A Presentaton on Market Effcences to Northfeld Informaton Servces Annual Conference he Short-term and Long-term Market Effcences en Post Offce Square Boston, MA 0209 www.acadan-asset.com Charles H. Wang,

More information

Simulation and optimization of supply chains: alternative or complementary approaches?

Simulation 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/s00291-007-0118-z

More information

Descriptive Models. Cluster Analysis. Example. General Applications of Clustering. Examples of Clustering Applications

Descriptive Models. Cluster Analysis. Example. General Applications of Clustering. Examples of Clustering Applications CMSC828G Prncples of Data Mnng Lecture #9 Today s Readng: HMS, chapter 9 Today s Lecture: Descrptve Modelng Clusterng Algorthms Descrptve Models model presents the man features of the data, a global summary

More information

Robust Design of Public Storage Warehouses. Yeming (Yale) Gong EMLYON Business School

Robust 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 information

J. Parallel Distrib. Comput. Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers

J. Parallel Distrib. Comput. Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers J. Parallel Dstrb. Comput. 71 (2011) 732 749 Contents lsts avalable at ScenceDrect J. Parallel Dstrb. Comput. ournal homepage: www.elsever.com/locate/pdc Envronment-conscous schedulng of HPC applcatons

More information

Optimal Joint Replenishment, Delivery and Inventory Management Policies for Perishable Products

Optimal 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 CIRRELT-2013-32 Bureaux de Montréal : Bureaux de Québec : Unversté de

More information

Chapter 11 Practice Problems Answers

Chapter 11 Practice Problems Answers Chapter 11 Practce Problems Answers 1. Would you be more wllng to lend to a frend f she put all of her lfe savngs nto her busness than you would f she had not done so? Why? Ths problem s ntended to make

More information

Allocating Collaborative Profit in Less-than-Truckload Carrier Alliance

Allocating Collaborative Profit in Less-than-Truckload Carrier Alliance J. Servce Scence & Management, 2010, 3: 143-149 do:10.4236/jssm.2010.31018 Publshed Onlne March 2010 (http://www.scrp.org/journal/jssm) 143 Allocatng Collaboratve Proft n Less-than-Truckload Carrer Allance

More information

Benefits of Multi-Echelon Inventory Control

Benefits of Multi-Echelon Inventory Control 2012, Lund Lund Unversty Lund Insttute of Technology Dvson of Producton Management Department of Industral Management and Logstcs Benefts of Mult-Echelon Inventory Control A case study at Tetra Pak Author:

More information

Luby s Alg. for Maximal Independent Sets using Pairwise Independence

Luby s Alg. for Maximal Independent Sets using Pairwise Independence Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent

More information

Abstract. Abstract...1. 1. Introduction... 2 2. Two Models... 2 2.1. Classic Optimal Lot Size (OLS) Model 2 2.2. Monopolistic Competition 3

Abstract. Abstract...1. 1. Introduction... 2 2. Two Models... 2 2.1. Classic Optimal Lot Size (OLS) Model 2 2.2. Monopolistic Competition 3 Optmal Lot Sze, Inventores, Prces and JIT Under Monopolstc Competton * Mchael C. Lovell Wesleyan Unversty, Mddletown, CT Mlovell@Wesleyan.edu May 17, 2000 [Prelmnary Draft] Abstract Ths paper attempts

More information

European Journal of Operational Research

European Journal of Operational Research European Journal of Operatonal Research 221 (2012) 317 327 Contents lsts avalable at ScVerse ScenceDrect European Journal of Operatonal Research journal homepage: www.elsever.com/locate/ejor Producton,

More information

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12 14 The Ch-squared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed

More information

1 Approximation Algorithms

1 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 information

Discriminatory versus Uniform-Price Electricity Auctions with Supply Function Equilibrium

Discriminatory versus Uniform-Price Electricity Auctions with Supply Function Equilibrium Dscrmnatory versus Unform-Prce Electrcty Auctons wth Supply Functon Equlbrum Talat S. Genc a December, 007 Abstract. A goal of ths paper s to compare results for dscrmnatory auctons to results for unform-prce

More information

A method for a robust optimization of joint product and supply chain design

A method for a robust optimization of joint product and supply chain design DOI 10.1007/s10845-014-0908-5 A method for a robust optmzaton of jont product and supply chan desgn Bertrand Baud-Lavgne Samuel Bassetto Bruno Agard Receved: 10 September 2013 / Accepted: 21 March 2014

More information

Chapter 15: Debt and Taxes

Chapter 15: Debt and Taxes Chapter 15: Debt and Taxes-1 Chapter 15: Debt and Taxes I. Basc Ideas 1. Corporate Taxes => nterest expense s tax deductble => as debt ncreases, corporate taxes fall => ncentve to fund the frm wth debt

More information

Project Networks With Mixed-Time Constraints

Project Networks With Mixed-Time Constraints Project Networs Wth Mxed-Tme 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 information

Time Value of Money Module

Time Value of Money Module Tme Value of Money Module O BJECTIVES After readng ths Module, you wll be able to: Understand smple nterest and compound nterest. 2 Compute and use the future value of a sngle sum. 3 Compute and use the

More information

Addendum to: Importing Skill-Biased Technology

Addendum to: Importing Skill-Biased Technology Addendum to: Importng Skll-Based Technology Arel Bursten UCLA and NBER Javer Cravno UCLA August 202 Jonathan Vogel Columba and NBER Abstract Ths Addendum derves the results dscussed n secton 3.3 of our

More information

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

benefit 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 information

Evolution of Internet Infrastructure in the 21 st century: The Role of Private Interconnection Agreements

Evolution of Internet Infrastructure in the 21 st century: The Role of Private Interconnection Agreements Evoluton of Internet Infrastructure n the 21 st century: The Role of Prvate Interconnecton Agreements Rajv Dewan*, Marshall Fremer, and Pavan Gundepud {dewan, fremer, gundepudpa}@ssb.rochester.edu Smon

More information

No 144. Bundling and Joint Marketing by Rival Firms. Thomas D. Jeitschko, Yeonjei Jung, Jaesoo Kim

No 144. Bundling and Joint Marketing by Rival Firms. Thomas D. Jeitschko, Yeonjei Jung, Jaesoo Kim No 144 Bundlng and Jont Marketng by Rval Frms Thomas D. Jetschko, Yeonje Jung, Jaesoo Km May 014 IMPRINT DICE DISCUSSION PAPER Publshed by düsseldorf unversty press (dup) on behalf of Henrch Hene Unverstät

More information

SPEE Recommended Evaluation Practice #6 Definition of Decline Curve Parameters Background:

SPEE Recommended Evaluation Practice #6 Definition of Decline Curve Parameters Background: SPEE Recommended Evaluaton Practce #6 efnton of eclne Curve Parameters Background: The producton hstores of ol and gas wells can be analyzed to estmate reserves and future ol and gas producton rates and

More information

Online Appendix Supplemental Material for Market Microstructure Invariance: Empirical Hypotheses

Online Appendix Supplemental Material for Market Microstructure Invariance: Empirical Hypotheses Onlne Appendx Supplemental Materal for Market Mcrostructure Invarance: Emprcal Hypotheses Albert S. Kyle Unversty of Maryland akyle@rhsmth.umd.edu Anna A. Obzhaeva New Economc School aobzhaeva@nes.ru Table

More information

Credit Limit Optimization (CLO) for Credit Cards

Credit 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 information

Section 5.4 Annuities, Present Value, and Amortization

Section 5.4 Annuities, Present Value, and Amortization Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today

More information

The Pricing Strategy of the Manufacturer with Dual Channel under Multiple Competitions

The Pricing Strategy of the Manufacturer with Dual Channel under Multiple Competitions Internatonal Journal of u-and e-servce, Scence and Technology Vol.7, No.4 (04), pp.3-4 http://dx.do.org/0.457/junnesst.04.7.4. The Prcng Strategy of the Manufacturer wth Dual Channel under Multple Compettons

More information

Calculating the high frequency transmission line parameters of power cables

Calculating the high frequency transmission line parameters of power cables < ' Calculatng the hgh frequency transmsson lne parameters of power cables Authors: Dr. John Dcknson, Laboratory Servces Manager, N 0 RW E B Communcatons Mr. Peter J. Ncholson, Project Assgnment Manager,

More information

ECONOMICS OF PLANT ENERGY SAVINGS PROJECTS IN A CHANGING MARKET Douglas C White Emerson Process Management

ECONOMICS OF PLANT ENERGY SAVINGS PROJECTS IN A CHANGING MARKET Douglas C White Emerson Process Management ECONOMICS OF PLANT ENERGY SAVINGS PROJECTS IN A CHANGING MARKET Douglas C Whte Emerson Process Management Abstract Energy prces have exhbted sgnfcant volatlty n recent years. For example, natural gas prces

More information

FORCED CONVECTION HEAT TRANSFER IN A DOUBLE PIPE HEAT EXCHANGER

FORCED CONVECTION HEAT TRANSFER IN A DOUBLE PIPE HEAT EXCHANGER FORCED CONVECION HEA RANSFER IN A DOUBLE PIPE HEA EXCHANGER Dr. J. Mchael Doster Department of Nuclear Engneerng Box 7909 North Carolna State Unversty Ralegh, NC 27695-7909 Introducton he convectve heat

More information

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy Fnancal Tme Seres Analyss Patrck McSharry patrck@mcsharry.net www.mcsharry.net Trnty Term 2014 Mathematcal Insttute Unversty of Oxford Course outlne 1. Data analyss, probablty, correlatons, vsualsaton

More information

Awell-known result in the Bayesian inventory management literature is: If lost sales are not observed, the

Awell-known result in the Bayesian inventory management literature is: If lost sales are not observed, the MANUFACTURING & SERVICE OPERATIONS MANAGEMENT Vol. 10, No. 2, Sprng 2008, pp. 236 256 ssn 1523-4614 essn 1526-5498 08 1002 0236 nforms do 10.1287/msom.1070.0165 2008 INFORMS Dynamc Inventory Management

More information

Many e-tailers providing attended home delivery, especially e-grocers, offer narrow delivery time slots to

Many e-tailers providing attended home delivery, especially e-grocers, offer narrow delivery time slots to Vol. 45, No. 3, August 2011, pp. 435 449 ssn 0041-1655 essn 1526-5447 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 information

The Personalization Services Firm: What to Sell, Whom to Sell to and For How Much? *

The Personalization Services Firm: What to Sell, Whom to Sell to and For How Much? * The Personalzaton Servces Frm: What to Sell, Whom to Sell to and For How Much? * oseph Pancras Unversty of Connectcut School of Busness Marketng Department 00 Hllsde Road, Unt 04 Storrs, CT 0669-0 joseph.pancras@busness.uconn.edu

More information

Hosted Voice Self Service Installation Guide

Hosted Voice Self Service Installation Guide Hosted Voce Self Servce Installaton Gude Contact us at 1-877-355-1501 learnmore@elnk.com www.earthlnk.com 2015 EarthLnk. Trademarks are property of ther respectve owners. All rghts reserved. 1071-07629

More information

The Economics of Two-sided Markets 2. Platform competition!

The Economics of Two-sided Markets 2. Platform competition! U. Porto Doctoral Programme n Economcs The Economcs of Two-sded Markets 2. Platform competton! Paul Belleflamme, CORE & LSM! Unversté catholque de Louvan! Aprl 10-13, 2012 Learnng objectves At the end

More information

Supply network formation as a biform game

Supply network formation as a biform game Supply network formaton as a bform game Jean-Claude Hennet*. Sona Mahjoub*,** * LSIS, CNRS-UMR 6168, Unversté Paul Cézanne, Faculté Sant Jérôme, Avenue Escadrlle Normande Némen, 13397 Marselle Cedex 20,

More information

Medium and long term. Equilibrium models approach

Medium and long term. Equilibrium models approach Medum and long term electrcty prces forecastng Equlbrum models approach J. Vllar, A. Campos, C. íaz, Insttuto de Investgacón Tecnológca, Escuela Técnca Superor de Ingenería-ICAI Unversdad ontfca Comllas

More information

The Retail Planning Problem Under Demand Uncertainty

The Retail Planning Problem Under Demand Uncertainty Vol., No. 5, September October 013, pp. 100 113 ISSN 1059-1478 EISSN 1937-5956 13 05 100 DOI 10.1111/j.1937-5956.01.0144.x 013 Producton and Operatons Management Socety The Retal Plannng Problem Under

More information

A) 3.1 B) 3.3 C) 3.5 D) 3.7 E) 3.9 Solution.

A) 3.1 B) 3.3 C) 3.5 D) 3.7 E) 3.9 Solution. ACTS 408 Instructor: Natala A. Humphreys SOLUTION TO HOMEWOR 4 Secton 7: Annutes whose payments follow a geometrc progresson. Secton 8: Annutes whose payments follow an arthmetc progresson. Problem Suppose

More information

Section 5.3 Annuities, Future Value, and Sinking Funds

Section 5.3 Annuities, Future Value, and Sinking Funds Secton 5.3 Annutes, Future Value, and Snkng Funds Ordnary Annutes A sequence of equal payments made at equal perods of tme s called an annuty. The tme between payments s the payment perod, and the tme

More information

A Binary Particle Swarm Optimization Algorithm for Lot Sizing Problem

A 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 & Yun-Cha Lang Abstract. Ths paper presents a bnary partcle swarm optmzaton

More information

Organizational Structure and Coordination in Electronic Retailing: A Competitive Analysis

Organizational Structure and Coordination in Electronic Retailing: A Competitive Analysis Organzatonal Structure and Coordnaton n Electronc Retalng: A Compettve Analyss Abstract Ths study examnes coordnaton ssues that occur between the Marketng department and the Informaton Technology (IT department

More information

Marginal Returns to Education For Teachers

Marginal Returns to Education For Teachers The Onlne Journal of New Horzons n Educaton Volume 4, Issue 3 MargnalReturnstoEducatonForTeachers RamleeIsmal,MarnahAwang ABSTRACT FacultyofManagementand Economcs UnverstPenddkanSultan Idrs ramlee@fpe.ups.edu.my

More information

An Integrated Inventory Model with Controllable Lead time and Setup Cost Reduction for Defective and Non-Defective Items

An Integrated Inventory Model with Controllable Lead time and Setup Cost Reduction for Defective and Non-Defective Items Internatonal Journal of Supply and Operatons Management IJSOM August 14 Volume 1 Issue pp. 19-15 ISSN-Prnt: 383-1359 ISSN-Onlne: 383-55 www.jsom.com An Integrated Inventory Model wth Controllable Lead

More information

Rob Guthrie, Business Initiatives Specialist Office of Renewable Energy & Environmental Exports

Rob Guthrie, Business Initiatives Specialist Office of Renewable Energy & Environmental Exports Fnancng Solar Energy Exports Rob Guthre, Busness Intatves Specalst Offce of Renewable Energy & Envronmental Exports Export-Import Bank of the U.S. Independent, self-fundng fundng agency of the U.S. government

More information

This paper looks into the effects of information transparency on market participants in an online trading

This paper looks into the effects of information transparency on market participants in an online trading Vol. 29, No. 6, November December 2010, pp. 1125 1137 ssn 0732-2399 essn 1526-548X 10 2906 1125 nforms do 10.1287/mksc.1100.0585 2010 INFORMS The Effects of Informaton Transparency on Supplers, Manufacturers,

More information

Traffic State Estimation in the Traffic Management Center of Berlin

Traffic State Estimation in the Traffic Management Center of Berlin Traffc State Estmaton n the Traffc Management Center of Berln Authors: Peter Vortsch, PTV AG, Stumpfstrasse, D-763 Karlsruhe, Germany phone ++49/72/965/35, emal peter.vortsch@ptv.de Peter Möhl, PTV AG,

More information