gams:a_gams_implementation_of_the_example_model_mentioned_in_an_overview_of_genetic_algorithms_for_the_solution_of_optimisation_problems
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| + | ====== A MINLP GAMS implementation of the example model mentioned in "An overview of genetic algorithms for the solution of optimisation problems" ====== | ||
| + | <code> | ||
| + | $ontext | ||
| + | An overview of genetic algorithms for the solution of optimisation problems | ||
| + | Simon Mardle and Sean Pascoe | ||
| + | University of Portsmouth | ||
| + | |||
| + | http://www.economicsnetwork.ac.uk/cheer/ch13_1/ch13_1p16.htm | ||
| + | $offtext | ||
| + | |||
| + | Set i boat class /i1*i3/ | ||
| + | j fish species /j1*j3/ | ||
| + | |||
| + | Parameter | ||
| + | P(j) Price of fish species j / j1 2.5, j2 2, j3 1.5 / | ||
| + | F(i) Fixed Costs of boat class i / i1 1, i2 0.8, i3 0.8 / | ||
| + | V(i) Variable Costs of boat class i / i1 0.01, i2 0.008, i3 0.008 / | ||
| + | K(j) Carrying capacity of fish species j / j1 2000, j2 2500, j3 4000 / | ||
| + | R(j) Growth rate of fish species j / j1 0.1, j2 0.5, j3 0.3 / | ||
| + | ue(i) upper bound on effort / i1 275, i2 160, i3 200 /; | ||
| + | |||
| + | Table Q(i,j) Catchability coefficient of fish species j by boat class i | ||
| + | j1 j2 j3 | ||
| + | i1 0.0002 0.0001 | ||
| + | i2 0.0002 0.00005 | ||
| + | i3 0.0002 | ||
| + | ; | ||
| + | |||
| + | Variable | ||
| + | z profit | ||
| + | xb(i) boats | ||
| + | xe(i) effort | ||
| + | xf(j) fishing mortality | ||
| + | xc(j) catch | ||
| + | xl(j) landings; | ||
| + | Positive Variables xe,xf,xc,xl; | ||
| + | Integer Variables xb; | ||
| + | |||
| + | Equation | ||
| + | obj revenue from landings minus the fixed and variable costs of the fishing | ||
| + | e1(j) fishing mortality | ||
| + | e2(j) evaluates catch from this fishing mortality rate | ||
| + | e3(j) constrains landings to be no greater than catch; | ||
| + | |||
| + | obj.. z =e= sum(j, P(j)*xl(j)) - sum(i, F(i)*xb(i) + V(i)*xe(i)*xb(i)); | ||
| + | e1(j).. xf(j) =e= sum(i, Q(i,j)*xe(i)*xb(i)); | ||
| + | e2(j).. xc(j) =e= K(j)*xf(j) - K(j)/R(j)*sqr(xf(j)); | ||
| + | e3(j).. xl(j) =l= xc(j); | ||
| + | |||
| + | model fish /all/; | ||
| + | option optcr=0; | ||
| + | xe.up(i) = ue(i); | ||
| + | xl.up(j) = 500; | ||
| + | |||
| + | xb.l(i)=uniform(1,10); | ||
| + | xe.l(i)=uniform(100,200); | ||
| + | |||
| + | solve fish max z using rminlp; | ||
| + | fish.optcr=0; | ||
| + | solve fish max z using minlp; | ||
| + | </code> | ||
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