Optimal agricultural spreading scheduling through surrogate-based optimization and MINLP models

Learning surrogate models for simulation-based optimization

We address a central problem in modeling, namely that of learning an algebraic model from data obtained from simulations or experiments. We propose a methodology that uses a small number of simulations or experiments to learn models that are as accurate and as simple as possible. The approach begins by building a low-complexity surrogate model. The model is built using a best subset technique t...

17 Surrogate - Based Optimization

Surrogate-Based Optimization

Surrogate-based optimization (Queipo et al. 2005, Simpson et al. 2008) represents a class of optimization methodologies that make use of surrogate modeling techniques to quickly find the local or global optima. It provides us a novel optimization framework in which the conventional optimization algorithms, e.g. gradient-based or evolutionary algorithms are used for sub-optimization(s). Surrogat...

Real-Time Optimization Based on Adaptation of Surrogate Models

Recently, different real-time optimization (RTO) schemes that guarantee feasibility of all RTO iterates and monotonic convergence to the optimal plant operating point have been proposed. However, simulations reveal that these schemes converge very slowly to the plant optimum, which may be prohibitive in applications. This note proposes an RTO scheme based on second-order surrogate models of the...

MINLP models for optimal design of reliable chemical plants

This paper proposes a multi-objective optimization model (P1) that determines the optimal selection of parallel units considering the trade-off between the availability of a serial producing system and total investment costs. Model (P1) is solved through the constrained MINLP model (P1’) that maximizes system availability subject to a series of upper bounds of the total cost, which yields a set...