DICOPT solvers

Once an initial solution is incorporated into the MINLP model, the model is solved via the DICOPT solver employing the Outer approximation algorithm for equality relaxation and augmented penalty (OA/ER/AP). 

The model was solved using GAMS DICOPT, with CLPEX as the MIP solver and CONOPT as the NLP solver. The computational results are shown in Table 3.7. The resulting plant requires only one reactor as shown in Fig. 3.14. The optimal capacities of the remaining units are 75 units for the mixer (Cl), 75 units for the reactor... 

The results for scenario 2 were obtained using GAMS 2.5/DICOPT. The NLP and MILP combination of solvers selected for DICOPT were MINOS5 and CPLEX, respectively. The overall formulation involves 421 constraints, 175 continuous variables and 36 discrete variables. Only 2 nodes were explored in the branch and... 

As in the previous case the solution procedure described in Sect. 4.4 was used to solve the example. The resulting models were formulated in GAMS 22.0, as with the previous case. CPLEX 9.1.2 was used to solve the MILP and the DICOPT2 solution algorithm was used to solve the exact MINLP. In the DICOPT solution algorithm, CLPEX 9.1.2 was the MIP solver and CONOPT3 the NLP solver. The same processor as the previous example was used to find a solution. 

The example was formulated in GAMS 22.0 and solved using the DICOPT2 solution algorithm, with CPLEX 9.1.2 as the MIP solver and CONOPT3 as the NLP solver. The model was solved using a Pentium 4 3.2 GHz processor and required 16.8 CPU seconds to find a solution. DICOPT did 4 major iterations to find the final solution. The optimal number of time points was 8, which resulted in 192 binary variables for the model. 

A number of efficient codes are available for the solution of MINLPs. These include the AlphaECP, BARON, DICOPT, MINLP, and SBB solvers descriptions of these can be found on the http //www.gamsworld.org/minlp/solvers.htm web site. 

The MINLP-model instances comprised 200 binary variables, 588 continuous variables and 1038 constraints. The linearization not only eliminates the nonlinearity but also leads to a reduced number of398 continuous variables and 830 constraints (the number of 200 binary variables is unchanged). The MINLP-problems were solved by the solver architecture DICOPT/CONOPT/CPLEX, and the MILP problems were solved by CP LEX, both on a Windows machine with an Intel Xeon 3 GHz CPU and 4 GB RAM. 

MODEL grouplnorm2cutl TYPE MINLP SOLVER DICOPT... 

We write the solver statement. In this case, the problem is an MINLP and thus it is required that an MINLP solver be used. DICOPT is used by default as well as a local NLP solver such as CONOPT. 

The above problem corresponds to an MINLP model due to nonlinearity in the Hazen-Williams correlation. This model is solved using the DICOPT method in the GAMS system (Brooke et al., 1998). DiCOPT invokes milp and NLP solvers iteratively. In this work, we have used the cplex 6.5 milp solver and four different NLP solvers. 

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