Use a MultiObjective Approach¶
In this article we will compare a multiobjective approach to separately solving single objectives.
Let’s take for an example a problem trying to find a healthy diet for a reasonable price. First let’s define our objectives:
Minimize calories: I’m trying to lose some weight, so my healthy diet should be low in calories.
Minimize price: A reasonable price means that it doesn’t have to be the absolute minimum, but should be within a close range.
Of course, I want to include minimum/maximum requirements for nutrients such as proteins.
MultiObjective Approach¶
Let’s use the multiobjective feature available since AIMMS 4.65 and CPLEX 12.9.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25  Procedure SolveMultiObj {
Body: {
ep_GMP := gmp::Instance::Generate( DietProblem );
p_retcode := GMP::Column::SetAsMultiObjective(
GMP : ep_GMP,
column : TotalCost,
priority : 2,
weight : 1,
abstol : 0,
reltol : 0.1);
if not p_retcode then raise error "Unable to set TotalCost as an objective" ; endif ;
p_retcode := GMP::Column::SetAsMultiObjective(
GMP : ep_GMP,
column : TotalCalories,
priority : 1,
weight : 1,
abstol : 0,
reltol : 0.0);
if not p_retcode then raise error "Unable to set TotalCalories as an objective" ; endif ;
GMP::Instance::Solve( ep_GMP );
}
}

There are several remarks on the above:
Because the
TotalCost
objective has a higher priority value, it will be solved first.Because the
reltol
argument on line 11 has value 0.1, subsequent solves will not increase the total cost by more than 10%.
Single Objective Approach¶
To be able to compare the multiobjective procedure to traditional single objective solves, there are also two solution procedures in this application:
MainExecution
, a traditional single objective solve minimizing total cost.SolveMinCalo
, a traditional single objective solve minimizing total calories.
Comparing Results¶
The application has both WinUI and WebUI interfaces, but WebUI is featured in the following screenshots.
The objectives are summarized in the table below:
Cost 
Calories 

Minimize total cost 
21.85 
2698.40 
Minimize total calories 
29.80 
2546.40 
Multi objective 
23.85 
2576.40 
As you can see, the multiobjective values are not as good as either of the individual objective values, but the multiobjective approach provides a good balance.
You may also find the CPLEX log interesting, as found in the file log/cplex 12.9.sta
.
(See also Retrieve Solver Log Files in AIMMS Developer.)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37  Solve problem 'DietProblem' with 9 rows, 15 columns (0 binaries, 9 generals), and 83 nonzeros.
MIP  Integer optimal solution: Objective = 2.1849999998e+01
Solution time = 0.09 sec. Iterations = 20 Nodes = 0
Solve problem 'MinCaloDietProblem' with 9 rows, 15 columns (0 binaries, 9 generals), and 83 nonzeros.
MIP  Integer optimal solution: Objective = 2.5464000000e+03
Solution time = 0.02 sec. Iterations = 13 Nodes = 0
Solve problem 'DietProblem' with 8 rows, 14 columns (0 binaries, 9 generals), and 73 nonzeros.
Multiobjective solve log . . .
Starting optimization #1 with priority 2.
Finished optimization #1 with priority 2.
Objective = 2.1849999998e+01, Nodes = 0, Time = 0.05 sec. (0.45 ticks)
Cumulative statistics: Nodes = 0, Time 0.05 sec. (0.45 ticks)
Starting optimization #2 with priority 1.
Finished optimization #2 with priority 1.
Objective = 2.5764000000e+03, Nodes = 0, Time = 0.02 sec. (0.43 ticks)
Cumulative statistics: Nodes = 0, Time 0.06 sec. (0.89 ticks)
MIP  Multiobjective optimal
Solution time = 0.08 sec. Iterations = 43 Nodes = 0

A breakdown of above log:
lines 1  4 for the first solve (minimize total cost)
lines 7  10 for the second solve (minimize total calories)
lines 13  37 for the multi objective solve.