COURSE UNIT TITLE

: OPTIMIZATION SOFTWARE

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IND 5051 OPTIMIZATION SOFTWARE ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR ADIL BAYKASOĞLU

Offered to

INDUSTRIAL ENGINEERING - NON THESIS
Industrial Engineering - Thesis (Evening Program)
INDUSTRIAL ENGINEERING
INDUSTRIAL ENGINEERING
INDUSTRIAL ENGINEERING - NON THESIS (EVENING PROGRAM)

Course Objective

Optimization is the search for the best and most effective solution, by collection of mathematical principles and methods used for solving quantitative problems in many disciplines, including natural sciences, management, engineering, economics, and business. There are many software tools available to solve optimization problems with different characteristics. The course gives insight into state-of-the-art optimization software packages that are commonly used to solve a wide range of mathematical programming models in which one seeks to minimize or maximize an objective function subject to a set of constraints.

Learning Outcomes of the Course Unit

1   The students will have understanding of the importance of optimization software to effectively solve optimization problems in a timely manner.
2   The students will have a profound understanding of a wide variety of optimization models and will be able to classify them into appropriate categories to select the appropriate software for a given optimization model.
3   The students will be familiar with state-of-the-art optimization software packages and their properties, and will be able to use these in an efficient manner.
4   The students will be able to code and solve a wide variety of mathematical models for optimization problems and analyze and interpret the results.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Optimization with LINGO Software Package
2 Optimization with LINGO Software Package
3 Optimization with LINGO Software Package
4 Optimization with LINGO Software Package
5 Optimization with GAMS Software Package
6 Optimization with GAMS Software Package
7 Optimization with GAMS Software Package
8 Optimization with GAMS Software Package
9 Optimization with ILOG CPLEX Software Package
10 Optimization with ILOG CPLEX Software Package
11 Mid-term Exam
12 Optimization with ILOG CPLEX Software Package

Recomended or Required Reading

Jorge J. More, Stephen J. Wright, 1993, Optimization Software Guide, SIAM.
LINGO The Modelling Language and Optimizer, User Manual, 2018, LINDO SYSTEMS INC.
Linus Schrage, 1997, Optimization Modeling With LINDO, Duxbury Press.
GAMS Documentation, 2018, GAMS Development Corporation
Bruce A. McCarl, 2003, GAMS User Guide, GAMS Development Corporation.
IBM ILOG CPLEX Optimization Studio CPLEX User s Manual, 2016, IBM Corp.

Planned Learning Activities and Teaching Methods

Course notes given by visual presentations and on board, applications in computer laboratory, in-class activities, discussions of journal articles, project presentations

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 FIN FINAL EXAM
3 FCG FINAL COURSE GRADE ASG * 0.50 + FIN * 0.50
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.50 + RST * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Midterm exam (30%) + Projects (20%) + Final exam (50%)

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

: Prof. Dr. Adil BAYKASOĞLU
adil.baykasoglu@deu.edu.tr

Office Hours

To be declared

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 14 7 98
Preparation for midterm exam 1 17 17
Preparation for final exam 1 25 25
Preparing presentations 1 20 20
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 203

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1445
LO.255
LO.35554
LO.4555