Book contents
- Frontmatter
- Contents
- Notation
- Preface
- Part I Overview of Optimization:Applications and Problem Formulations
- Part II From General Mathematical Background to General Nonlinear Programming Problems (NLP)
- Part III Formulation and Solution of Linear Programming (LP) Problems
- 10 Introduction to LP Models
- 11 Numerical Solution of LP Problems Using the Simplex Method
- 12 A Sampler of LP Problem Formulations
- 13 Regression Revisited: Using LP to Fit Linear Models
- 14 Network Flow Problems
- 15 LP and Sensitivity Analysis, in Brief
- 16 Multiobjective Optimization
- 17 Optimization under Uncertainty
- 18 Mixed-Integer Programming Problems
- 19 Global Optimization
- 20 Optimal Control Problems (Dynamic Optimization)
- 21 System Identification and Model Predictive Control
- Index
20 - Optimal Control Problems (Dynamic Optimization)
from Part III - Formulation and Solution of Linear Programming (LP) Problems
Published online by Cambridge University Press: 17 December 2020
- Frontmatter
- Contents
- Notation
- Preface
- Part I Overview of Optimization:Applications and Problem Formulations
- Part II From General Mathematical Background to General Nonlinear Programming Problems (NLP)
- Part III Formulation and Solution of Linear Programming (LP) Problems
- 10 Introduction to LP Models
- 11 Numerical Solution of LP Problems Using the Simplex Method
- 12 A Sampler of LP Problem Formulations
- 13 Regression Revisited: Using LP to Fit Linear Models
- 14 Network Flow Problems
- 15 LP and Sensitivity Analysis, in Brief
- 16 Multiobjective Optimization
- 17 Optimization under Uncertainty
- 18 Mixed-Integer Programming Problems
- 19 Global Optimization
- 20 Optimal Control Problems (Dynamic Optimization)
- 21 System Identification and Model Predictive Control
- Index
Summary
Multiobjective optimization is becoming a useful tool in many practical and industrial applications, where different conflicting objectives have to be optimized simultaneously.This is a classical problem that has solutions at the famous Pareto Front, which quantifies the trade-off between different objective function values.Basic concepts are introduced in this chapter and various solution approaches of practical interest are presented.
- Type
- Chapter
- Information
- Optimization for Chemical and Biochemical EngineeringTheory, Algorithms, Modeling and Applications, pp. 287 - 311Publisher: Cambridge University PressPrint publication year: 2021