1 1 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Slides by JOHN LOUCKS St. Edward’s University INTRODUCTION TO MANAGEMENT SCIENCE, 13e Anderson Sweeney Williams Martin

2 2 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Chapter 5 Advanced Linear Programming Applications n Data Envelopment Analysis n Revenue Management n Portfolio Models and Asset Allocation n Game Theory

3 3 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Data Envelopment Analysis n Data envelopment analysis (DEA) is an LP application used to determine the relative operating efficiency of units with the same goals and objectives. n DEA creates a fictitious composite unit made up of an optimal weighted average ( W 1, W 2,…) of existing units. n An individual unit, k, can be compared by determining E, the fraction of unit k ’s input resources required by the optimal composite unit. n If E < 1, unit k is less efficient than the composite unit and be deemed relatively inefficient. n If E = 1, there is no evidence that unit k is inefficient, but one cannot conclude that k is absolutely efficient.

4 4 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Data Envelopment Analysis n The DEA Model MIN E s.t.Sum of weights = 1 Weighted composite outputs > Unit k ’s output Weighted composite outputs > Unit k ’s output (for each measured output) Weighted inputs < E [Unit k ’s input] (for each measured input) E, weights > 0 Question : Can we find a combination of units whose output is as much as k unit, but can reduce the input?

5 5 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. n n Input n n Output Data Envelopment Analysis

6 6 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. n n About which Hospital? n n Maximizing or minimizing? n n Constraints? How many? n n Decision variables wg, wu, wc, ws : weights for General, University, County, and State hospitals E : Efficient measure for County hospital wg + wu + wc + ws = 1 Full time physician : 48.14wg wu wc ws >= Medicare patients 285.2wg wu wc ws <= 275.7E Data Envelopment Analysis

7 7 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. n n Formulaiton Data Envelopment Analysis

8 8 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. n n Output Variable Value Reduced Cost E WG WU WC E-01 WS Row Slack or Surplus Dual Price E E E-02 Data Envelopment Analysis

9 9 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. n n 이해하기 Data Envelopment Analysis

10 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. n n General Hospital Min = E; wg + wu + wc + ws = 1; 48.14*wg *wu *wc *ws >= 48.14; 43.10*wg *wu *wc *ws >= 43.10; 253*wg + 148*wu + 175*wc + 160*ws >= 253; 41*wg + 27*wu + 23*wc + 84*ws >= 41; 285.2*wg *wu *wc *ws *E <= 0; 123.8*wg *wu *wc *ws *E <= 0; *wg *wu *wc *ws *E <= 0; Data Envelopment Analysis n n General Hospital Variable Value Reduced Cost E WG WU WC WS Row Slack or Surplus Dual Price E E E

11 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. n n Output 값이 최고든지 input 값이 최소이면 E=1 Data Envelopment Analysis

12 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. 문제점 Inefficient 한 unit 을 찾아낼 수는 있는데 Efficient unit 은 찾기가 어렵다. output 이든 input 이든 무엇 하나라도 제일 잘하면 (output measure 가 최대이거나 input measure 가 최소 ) 설사 다른 부분에서 매우 Inefficient 해도 나타나지 않는다. Data Envelopment Analysis

13 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. n n Fleight legs Fleight Reservation

14 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. n n Fares and Demand forcasts Fleight Reservation

15 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. n n Maximizing or Minimizing? n n Constraints? How many? n n Decision Variables Pittsburg, Newark, Charlotte, Orlando, Myrtle Beach ODIF code : PCQ, PMQ, POQ, PCY, PMY,... n n Objective function Fleight Reservation

16 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. n n Constraints Fleight Reservation

17 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. n n Output Fleight Reservation

18 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. n n Output Fleight Reservation

19 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. n n What is the soluion? n n How much is the optimal revenue? n n Two weeks earlier than the departure, PMQ( from Pittsburg to Myrtle Beach) reservation is 44. Can you reserve one more seat for PMQ when a customer wants to reserve ? dual prices for 1 & 4 are 4 and 179, it costs 183, but revenue increase is 228. Thus, 228 – 179 = 85. Yes. (read the last paragraph on p.231 about bid price) Fleight Reservation

20 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Portfolio Model (p.233) n n Five scenarios (5 previous returns, Year1,..., Year5)

21 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Game theory (p.241) n n Two-person, zero-sum game : 2 parties. gain of one party means the loss of the other. n n Pay-off table gain of one party depending upon the strategies that two parties take. Pay-off table is known to both party. n n Maximin strategy n n Minmax regret strategy

22 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. End of Chapter 5