Mixed-Integer linear programming
Revision as of 16:44, 30 March 2015 by Kevin Dunn (talk | contribs)
Class date(s): | 25 March 2015 | ||||
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Resources
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Date | Class number | Topic | Slides/handouts for class | Video file | References and Notes |
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25 February | 11B |
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Video |
Solving a basic ILP
free variable income "total income";
positive variables x1, x2;
binary variable delta "use ingredient x3 or not at all";
EQUATIONS
obj "maximize income",
blend "blending constraint";
obj.. income =E= 18*x1 - 3*x2 - 9*(20*delta);
blend.. 2*x1 + x2 + 7*(20*delta) =L= 150;
x1.up = 25;
x2.up = 30;
model recipe /all/;
SOLVE recipe using MIP maximizing income;
Solving the knapsack problem
free variable value "total value";
sets j "item j" /1*5/;
binary variables x(j) "whether to include item in the knapsack";
parameter v(j) "value of object j"
/1 4,
2 2,
3 10,
4 1,
5 2/;
parameter w(j) "weight of object j"
/1 12,
2 1,
3 4,
4 1,
5 2/;
EQUATIONS
obj "maximize value",
weight "weight constraint";
obj.. value =e= sum(j, v(j)*x(j));
weight.. sum(j, w(j)*x(j)) =L= 15;
model knapsack /all/;
solve knapsack using mip maximizing value;
Solving the knapsack problem for selecting amount projects, with constraints
free variable value "total value";
sets j "item j" /1*5/;
binary variables x(j) "whether to include item in the project";
parameter v(j) "value of object j"
/1 50,
2 72,
3 25,
4 41,
5 17/;
parameter w(j) "cost of object j"
/1 8,
2 21,
3 15,
4 10,
5 7/;
EQUATIONS
obj "maximize value",
weight "weight constraint",
me1v2 "1 and 2 are mutually exclusive",
me1v3v5 "1, 3 and 5 are mutually exclusive",
d4v3 "4 depends on 3";
obj.. value =e= sum(j, v(j)*x(j));
weight.. sum(j, w(j)*x(j)) =L= 35;
me1v2.. x('1') + x('2') =L= 1;
me1v3v5.. x('1') + x('3') + x('5') =L= 1;
d4v3.. x('4') =L= x('3');
model projects /all/;
solve projects using mip maximizing value;