Introduction to Optimization Modeling

LEVEL : INTERMEDIATE          HRDF : CLAIMABLE

TRAINER : IR DR KHOR CHENG SEONG

WHEN  


17 - 18 FEBRUARY 2021

WHERE 


MS TEAMS

TIME


9.00AM - 5.00PM

RM 850 FOR PROFESSIONAL

10% Discount for Early Bird (until 29 Jan 2021)/ Group / Students

COURSE SUMMARY

INTRODUCTION

The course provides an introduction to optimization methods focusing on modeling techniques. It covers basic optimization algorithms for linear programming (LP, e.g., simplex method) and nonlinear programming (NLP, e.g., gradient-based methods) including mixed- integer linear and nonlinear programming (MILP, MINLP). Emphasis is placed on model formulations to suit applicability in terms of balancing accuracy, speed, and practicality. A highlight of the course is the hands-on practical sessions to enable participants to grasp, apply, and reinforce the theoretical aspects.

COURSE CONTENT
PART 1: Introduction to Optimization—Background and Applications.

  • Motivating Example/Case Study #1: Refinery planning (LP model).
  • Working Session 1: Practice on Motivating Example.
  • Working Session 1 (continued): Solution on Motivating Example.

PART 2: Introduction to Continuous Optimization (LP, NLP).

  • Modeling principles and techniques.
  • Background theory on algorithms for LP and NLP.
  • Case Study #2: Downstream compressor operation with chemicals addition(NLP model).
  • Working Session 2: Practice on Case Study #2 (with partial solution; to be completed as take-home assignment).

PART 3: Introduction to Discrete Optimization (MILP, MINLP)

  • Modeling principles and techniques.
  • Background theory on algorithms for discrete/integer programming (MILP/MINLP).
  • Case Study #3: Downstream asset inventory management (MILP model).
  • Working Session 3: Practice on Case Study #3.
  • Working Session 3 (continued): Solution on Case Study #3.
  • Case Study #4: Upstream gas field planning (MILP/MINLP model).
  • Working Session 4: Practice on Case Study #4.
  • Working Session 4 (continued): Solution on Case Study #4.


OBJECTIVES

Upon completion of this course, participants will be able to:

  • Identify the problem or purpose suitable for optimization modeling.
  • Determine a suitable optimization model for particular purpose and need.
  • Apply the skill or knowledge needed for optimization modeling.


WHO SHOULD ATTEND?

  • PETRONAS Data Science Department/Group Digital.





OUR TRAINERS



1. Ir Dr Khor Cheng Seong (UTP)

Ir Dr Khor Cheng Seong (UTP) obtained his PhD from Imperial College London, UK and masters from University of Waterloo, Canada, both in chemical engineering with research topics on optimization applications to the process industries. He has been involved in several consultancy projects and studies applying modeling and optimization to industrial problems. Findings and results from his work can be found in research journals, book volumes, and conference proceedings. He is a senior lecturer at Universiti Teknologi PETRONAS. He has industrial experience as a modeling and optimization engineer for petroleum refineries and petrochemical plants. He is a chartered engineer and member of the Institution of Chemical Engineers (IChemE, UK).

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REGISTRATION FEES

      Professional       

myr850

*fee quoted does not include SST, GST/VAT or withholding tax (if applicable)

Early bird/ group/ student

myr765

*fee quoted does not include SST, GST/VAT or withholding tax (if applicable)

OUR LOCATION

Centre for Advanced & Professional Education (CAPE)

 Level 16, Menara 2, Menara Kembar Bank Rakyat, 50470, Jalan Travers, Kuala Lumpur.

CALL US

+605 - 368 7558

DROP US AN EMAIL

cape@utp.edu.my