Abstract
AbstractThe optimal design of a multiproduct batch chemical plant is formulated as a multiobjective optimization problem, and the resulting constrained mixed‐integer nonlinear program (MINLP) is solved by the nondominated sorting genetic algorithm approach (NSGA‐II). By putting bounds on the objective function values, the constrained MINLP problem can be solved efficiently by NSGA‐II to generate a set of feasible nondominated solutions in the range desired by the decision‐maker in a single run of the algorithm. The evolution of the entire set of nondominated solutions helps the decision‐maker to make a better choice of the appropriate design from among several alternatives. The large set of solutions also provides a rich source of excellent initial guesses for solution of the same problem by alternative approaches to achieve any specific target for the objective functions. Copyright © 2006 Curtin University of Technology and John Wiley & Sons, Ltd.
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