Winston p. 313

Сентябрь 14, 2022

Содержание

2. Winston p. 313, # 2 Dual: min 3y1 + 2y2 + y3 subject to y1 +
3. Winston p. 815, #11 Find the floor and ceiling of the value of this game. Does
4. Winston p. 815, #11 min -1 -10 -10 -1 -10 max 20 20 -1 7 2
5. Schrage #4 The next 5 slides show the crop recourse homework problem I assigned, plus the
6. Schrage #4 (Formulate Only)
7. Schrage Handout, #4 Indices s = season {wet,dry} c = crops {corn, sorg, bean} Data YIELDcs
8. Schrage Handout, #4 (cont’d)
9. Schrage #4 (Sample MPL Code) TITLE RecourseCrops; INDEX s := (wet,dry); { seasons } c :=
10. Schrage #4 (Sample MPL code) DECISION VARIABLES live; { units of livestock to raise } pcrop[c];
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Слайд 2

Winston p. 313, # 2
Dual:
min 3y1 + 2y2 + y3
subject to
y1

+ y3 ≥ -2
y1+y2 ≥ -1
y1 + y2 + y3 ≥ 1
y1 ≥ 0 , y2 ≤ 0, y3 unrestricted

Слайд 3

Winston p. 815, #11
Find the floor and ceiling of the value

of this game. Does it have a saddle point? If so, what's the value of the game?

Слайд 4

Winston p. 815, #11
min
-1
-10
-10
-1
-10
max 20 20 -1 7 2 20
This game

has a saddle point; floor = value = ceiling = -1

Слайд 5

Schrage #4
The next 5 slides show the crop recourse homework problem

I assigned, plus the MPL code
Modify the code to compute the worst-case probabilities of a wet or dry season
Hint: you only have to add one variable, modify the objective function, and add one set of constraints; the other constraints in the model stay the same
What are the worst-case probabilities? How does the overall expected cost change?

Слайд 6

Schrage #4 (Formulate Only)

Слайд 7

Schrage Handout, #4
Indices
s = season {wet,dry}
c = crops {corn, sorg, bean}
Data
YIELDcs

= yield/acre (bushels) of crop c in season s
PROBs = probablility of season s
SPRICEs = sale price ($) per bushel of crop c
PCOST = production cost/acre ($) for crops
LCROPc = bushels of crop c required per “unit” of livestock
LPROFIT = profit/unit ($) of livestock
MCOSTc = cost/bushel ($) of crop c on open market
ACRES = total acreage available for planting
MAXCROPc = maximum bushels of crop c that can be bought on the open market
Variables
live = units of livestock to raise
pcropc = acres of crop c to plant
bcropcs = bushels of crop c bought under scenario s
csoldcs = bushels of crop c sold in scenario s

Слайд 8

Schrage Handout, #4 (cont’d)

Слайд 9

Schrage #4 (Sample MPL Code)
TITLE
RecourseCrops;
INDEX
s := (wet,dry); { seasons

}
c := (corn,sorghum,beans); { crops }
DATA
PROB[s] := (.6,.4); { probability of season }
TACRES := 1000; { total acreage }
YIELD[c,s] := (100,45,
43,35,
45,33); { yield per acre of c in season s }
SPRICE[c] := (2,4,4); { sale price/bushel of c in dollars }
PCOST := 100; { production cost per acre planted }
LCROP[c] := (100,0,0); { bushels of c required/unit of livestock raised }
LPROFIT := 215; { profit per unit of livestock raised }
MCOST[c] := (2.2,0,0); { cost to buy crop on open market }
MAXBUY[c] := (1000000,0,0); { maximum bushels of crop c that can be bought }

Слайд 10

Schrage #4 (Sample MPL code)
DECISION VARIABLES
live; { units of livestock

to raise }
pcrop[c]; { number of acres of c to plant }
bcrop[c,s]; { bushels of c to buy on market under scenario s }
csold[c,s]; { bushels of c grown and sold (excess of livestock requirements ) }
MODEL
MAX totexpcost = LPROFIT*live + { livestock profit }
SUM(c,s: PROB[s]*SPRICE[c]*csold[c,s]) - { expected crop sales profit }
PCOST*SUM(c: pcrop[c]) - { planting cost }
SUM(c,s: PROB[s]*MCOST[c]*bcrop[c,s]); { expected corn bought }
SUBJECT TO
acres: { acreage constraints}
SUM(c: pcrop[c]) < TACRES;
balance[c,s]:
YIELD[c,s]*pcrop[c] + bcrop[c] - LCROP[c]*live = csold[c,s];
BOUNDS
bcrop[c] < MAXBUY[c]; WE HAVE TO DO THIS TO DISALLOW BEAN AND SORGHUM BUYS
END