9.2.24 - model set BAKERY; #Evansville, Indianapolis, South...

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Sheet1 Page 1 model set BAKERY set CUSTOMER param cost {i in BAKERY, j in CUSTOMER} param build_cost {i in BAKERY} param demand {j in CUSTOMER} param max_loaves param discount param min_baked var w binary var bakeries {i in BAKERY} binary var loaves {i in BAKERY, j in CUSTOMER} >= 0, integer minimize total_cost: sum{i in BAKERY}build_cost[i]*bakeries[i] + sum{i in BAKERY, j in CUSTOMER}loaves[i,j]*cost[i,j]*discou n subject to demanded {j in CUSTOMER}: sum{i in BAKERY}loaves[i,j] = demand[j] subject to baked_lim {i in BAKERY}: sum{j in CUSTOMER}loaves[i,j] <= max_loaves*bakeries[i] subject to minproductforEV: sum{j in CUSTOMER}loaves["EV",j] >= min_baked*w subject to minproductforSB: sum{j in CUSTOMER}loaves["SB",j] >= min_baked*(1-w) #w=0 if South Bend produces at least 800,000 loaves data set BAKERY:= EV IN SB set CUSTOMER:=customer1 customer2 customer3 param cost: customer1 customer2customer3:= EV 0.160.34 0.26 IN 0.40.3 0.35 SB 0.450.45 0.23 param: build_cost:= EV5000000 IN4000000 SB4500000
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This note was uploaded on 01/25/2010 for the course IEOR 162 taught by Professor Zhang during the Fall '07 term at University of California, Berkeley.

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9.2.24 - model set BAKERY; #Evansville, Indianapolis, South...

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