Lecturer: S.M. Reza Pishvaie

Status (in the study program):

Optional course in graduate study; Compulsory for Process Eng. Group students.

Aims/Scope/Objectives: The students are acquainted with engineering judgment and formulation of optimization problems in chemical & petroleum processes (both upstream and downstream). The basic aim is to familiarize student with three key components of an optimization problem, namely, the objective function, the process model, and constraints. The students learn the approach how to attack the optimization problems through the convenient formulation (pre-optimization), suitable method of solution and necessary post-optimization studies. The graduates of this study are equipped with theoretical and practical knowledge of both static and dynamic optimization problems under both paradigms of classical and modern evolutionary schemes. Thisi is espcally the case when they are encountered with Chem. Eng.-oriented problems.

Syllabus:

         Introduction to optimization formulation.

         Mathematical backgrounds.

         Unconstrained static optimization methods.

         Constrained static optimization methods.

         Dynamic optimization, Variational approach.

         Evolutionary/Modern Techniques.

         Multi-Objective (Vector) Optimization.

         Application and case studies.

         Advanced topics.

References:

[1]. Rao, S.S., "Optimization, Theory & Applications", 3d  Ed. Wiley Eastern Ltd., (Reprint: 2004).

[2]. Edgar, T.F. and D.M. Himmelblau, " Optimization of Chemical Processes", McGraw-Hill Int., (1984).

[3]. Denn, M.M., "Optimization by Variational Methods", McGraw-Hill, NY, (1969).

[4]. Pontryagin, L.S., et al, "The Mathematical Theory of Optimal Processes", Wiley & Sons, NY (1962).

[5]. Pike, R.W., "Optimization for Engineering Systems", Van Nostrand Reinhold Co. Inc., (1986).

[6]. Nocedal, J. and Wright, S.J., "Numerical Optimization", Secaucus, N.J., USA: Springer-Verlag NY, Inc., 1999.

Teaching Method: Lectures, Seminar.

Prerequisites: Mathematics, (preferably) MATLAB.

Personal work required: Home-Works & Term-Project 

Examination method: Project-based.

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download the fonts of .pdf files.

Basic Probabilities and Statistics, (last updated : 1389/01/05)

Course Materials

Part I (Continuous Classic Optimization):

Cover (Ed. 7), updated on 1392/11/13

Chapter 01 - Introduction, updated on 1392/11/14

Chapter 02 - Supporting Math, updated on 1392/11/16

Chapter 03 - Geometric Programming, Incomplete (will be updated very soon)

Chapter 04 - Linear Programming, updated on 1392/11/23

Chapter 05 - Quadratic Programming, updated on 1392/11/17

Chapter 06 - One-Dimensional Search Methods, updated on 1392/11/23

Chapter 07 - Unconstrained Multi-Variable Direct (line-) Search Methods, updated on 1392/11/23

Chapter 08 - Unconstrained Multi-Variable Indirect (line-) Search Methods

Chapter 09 - Unconstrained Multi-Variable Trust-Region Methods

Chapter 10 - Constrained Multi-Variable Methods (NLP)

Part II (Discrete Optimization):

Cover (Ed.1)

Chapter 11 - Supporting Math - Under Preparation

Chapter 12 - Dynamic Programming

Chapter 13 - Integer Programming

Chapter 14 - Mixed-Integer Programming

Part III (Dynamic Optimization):

Cover (Ed. 2)

Chapter 15 - Introduction

Chapter 16 - Continuous Dynamic Programming - Under Preparation

Chapter 17 - Continuous Dynamic Optimization for Lumped System - Variational Approach

Chapter 18 - Continuous Dynamic Optimization for Distributed System - Variational Approach - Under Preparation

Chapter 19 - Optimal Control - Variational approach

Part IV (Special Optimization Techniques):

Cover (Ed. 1)

Chapter20_StochasticProg - Under Preparation

Chapter 21 - Global (Absolute) Optimization - Under Preparation

Chapter 22 - Multi-Objective (Vector) Optimization - Under Preparation

Chapter 23 - Combinatorial optimization - Under Preparation

Chapter 24 - Convex Programming - Under Preparation

Chapter 25 - Concave Programming - Under Preparation

Chapter 26 - Parametric Programming - Under Preparation

Chapter 27 - Separable Programming  - Under Preparation

Chapter 28 - Fuzzy Optimization - Under Preparation

Chapter 29 - Disjunctive Programming - Under Preparation

Chapter 30 - Multi-level Optimization - Under Preparation

Chapter 31 - Semi-definite Programming - Under Preparation

Chapter 32 - Semi-infinite Programming - Under Preparation

Chapter 33 - Young Programming - Under Preparation

Chapter 34 - Meta-Heuristic Programming - Under Preparation

Part V (Modern Evolutionary Techniques):

Cover (Ed. 2)

Chapter 35 - Simulated annealing - Under Preparation

Chapter 36 - Genetic Algorithms (GAs)

Chapter 37 - Ant Colony

Chapter 38 - Differential Evolution (DE)

Chapter 39 - GRASP - Under Preparation

Chapter 40 - Harmony Search

Chapter 41 - Particle Swarm Optimization (PSO) - Under Preparation

Chapter 42 - Scatter Search - Under Preparation

Chapter 43 - Tabu (Taboo) Search - Under Preparation

Chapter 44 - Noising Method - Under Preparation

Chapter 45 - Free Search - Under Preparation

Chapter 46 - Shuffled Frog Leaping - Under Preparation

Chapter 47 - Memetics

Chapter 48 - Artificial Immune System (AIS) - Under Preparation

Chapter 49 - Cross-Entropy Method - Under Preparation

Chapter 50 - Distributed Search - Under Preparation

Chapter 51 - Bee Colony - Under Preparation

Chapter 52 - Alienor Method - Under Preparation

Chapter 53 - Musical Instrument Tuning Algorithm (MITA) - Under Preparation

Part VI (Applications):

Cover (Ed. 1)

Util: xyExtract_Install.zip, 40 Farsi fonts for XP

 

General rules for examination of this course

Homeworks :

HW- 1 Due Time: (1392/12/26)

HW-2, Due Time: (1393/02/06), More Info

HW-3, Due Time: (1393/02/20)

HW-4, Due Time: (1393/03/24)

HW-5, Due Time: (1393/05/11), More Info