Search for Optimal Design of Multiproduct Batch Plants under Uncertain Demand using Gaussian Process Modeling Solved by Heuristics Methods

Abstract

This work deals with the problem of the search for optimal design of multiproduct batch chemical plants found in a chemical engineering process with uncertain demand. The aim of this work is to minimize the investment cost and find out the number and size of parallel equipment units in each stage. For this purpose, it is proposed to solve the problem in two different ways: the first way is by using Monte Carlo Method (MC) and the second way is by Genetics Algorithms (GAs). This GAs consider an effective mixed continuous discrete coding method with a four- point crossover operator, which take into account simultaneously, the uncertainty on the demand using Gaussian process modeling with two criteria maximization the Net Present Value (NPV) and Flexibility Index (FI). The results (number and size of equipment, investment cost, production time (Hi), NPV, FI, CPU time and Idle times in plant) obtained by GAs are better than the MC. This methodology can help the decision makers and constitutes a very promising framework for finding a set of "good solutions."