# 3_R_50 - ############# var x{i in 1. .W, j in 1. .I}; var...

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# Index Bounds ############# param W; # waste sent from city i (2) param I; # incenerator j (2) param L; # landfill k (2) p # Scalar Parameters ############# param reduces_waste; # inceneration reduces each ton of waste (0.2) param transport_cost; # cost per mile to transport a ton of material (\$3) p # Vector Parameters ############# param Waste_produced{i in 1. .W}; #waste in city i (500, 400) param capacity{j in 1. .I}; #capacity of incenerator j (500, 600) param inc_cost{j in 1. .I}; #incineration j cost (40, 30) p # Matrix Parameters ############# param Distance_City{i in 1. .W, j in 1. .I}; # distance from city i to incenerator j matrix (30, 5; 36, 42) param Distance_Landfill{j in 1. .I, k in 1. .L}; # distance from incen j to landfil k matrix (5, 8; 9, 6) m # Decision Variables
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Unformatted text preview: ############# var x{i in 1. .W, j in 1. .I}; var y{j in 1. .I, k in 1. .L}; v # Objective Function ############# minimize distance: (sum{i in 1. .W, j in 1..I}x[i,j])*inc_cost+transport_cost*(Distance_City*sum{i in 1. .W, j in 1. .I}x[i,j] +Distance_Landfill*sum{j in 1. .I, k in 1. .L}y[j,k]); + # Constraints ############# subject to waste_produced_per_day: (sum{i in 1. .W, j in 1..I}x[i,j])=waste_produced; subject to incin_capacities: (sum{i in 1. .W, j in 1. .I}x[i,j])=capacity; subject to waste_reduction: reduces_waste*(sum{i in 1. .W, j in 1. .I}x[i,j])=sum{j in 1. .I, k in 1. .L}y[j,k]; subject to nonnegX{i in 1. .W, j in 1. .I}: x[i, j] &gt;= 0; subject to nonnegY{j in 1. .I, k in 1. .L}: y[i, j] &gt;= 0;...
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## This note was uploaded on 01/21/2010 for the course IEOR 162 taught by Professor Zhang during the Fall '07 term at University of California, Berkeley.

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