IE 509 – Heuristics
Spring 2015
Lecturer
Zeynep Sargut
Room: A312 phone: 488-8137 email: zeynep.sargut@ieu.edu.tr
url: homes.ieu.edu.tr/zsargut
Course Objectives
The purpose of this course is to fundamental concepts of heuristics in solving various
optimization problems with emphasis on metaheuristics
Course Learning
Outcomes
The students who succeeded in this course;
Course Content
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Understand the basic types of heuristic search methods
Understand the details of basic metaheuristics
Be able to implement these heuristic methods to appropriate problems
This course introduces the concept of heuristics to the students who have already known about
mathematical optimization. The topics include basic heuristic constructs (greedy, improvement,
construction); meta heuristics such as simulated annealing, tabu search, genetic algorithms, ant
algorithms and their hybrids. The basic material on the heuristic will be covered in regular
lectures The students will be required to present a variety of application papers on different
subjects related to the course. In addition, as a project assignment the students will design a
heuristic, write a code of an appropriate algorithm for the problem and evaluate its
performance.
Rules


Check the web site regularly for reading materials and papers.
There will be 4 projects that may include programming. You may use Matlab or any programming
language to code the heuristics.
The final will be an individual presentation (20 minutes) of your selection of a paper in the below list.
The presentation should be emailed to the instructor for grading.

EVALUATION SYSTEM
Semester Requirements
Number
Percentage of Grade
Projects
4
80
Final/Oral Exam
1
20
100
Total
TENTATIVE SCHEDULE
Week
Subjects
Requirements
1
Review of Optimization (Objective function, feasible region),
Search, Binary Search
2
Nonlinear optimization
3
Nonlinear optimization search algorithms
4
General algorithmic structure, complexity, efficiency, experiments
and benchmarking NP-completeness, NP-hardness
Project 1 assigned
1
5
Combinatorial Optimization, Heuristics (greedy, construction,
improvement), Examples
Project 1 submission
6
Vehicle and driver assignment problem in public transportation
Project 2 assigned
7
Intro to Metaheuristics --Very Large Scale Neighborhood Search
8
Tabu Search
9
Simulated annealing- Particle Swarm optimization
10
GRASP (Greedy randomized adaptive search procedure)
11
Evolutionary Algorithms – Genetic Algorithm
12
Multi-objective Tabu Search
13
More topics on crew scheduling
14
Presentations & Discussions
15
Presentations & Discussions
Project 2 submission,
Project 3 assigned
Project 3 submission
Project 4 assigned
Project 4 submission
Required Readings and Supplementary Materials

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F. Glover, M. Laguna. Tabu Search. Kluwer, 1997.
E.G. Talbi. Metaheuristics: From Design to Implementation. Wiley 2009.
F. Glover, G. Kochenberger. Handbook of Metaheuristics. Springer 2003.
T. González. Handbook of Approximation Algorithms and Metaheuristics. Chapman & Hall 2007.
M. Dorigo and T. Stützle. Ant Colony Optimization. MIT Press, Cambridge, MA, 2004.
Presentation papers
1. Multiobjective metaheuristics for the bus–driver scheduling problem, H. Lourenco, J. Paixao, R.
Portugal, Transportation Science, 35 (3) (2001), pp. 331–341
2. Tabu search for multiobjective optimization: MOTS, MP Hansen - Proceedings of the 13th
International Conference on Multiple Criteria Decision Making, 1997
3. Very large-scale neighborhood search techniques in timetabling problems, C Meyers, JB Orlin, Practice
and Theory of Automated Timetabling VI, 2007
4. A bus driver scheduling problem: a new mathematical model and a GRASP approximate solution, R De
Leone, P Festa, E Marchitto - Journal of Heuristics, 2011
5. Urban Transit Scheduling: Framework, Review and Examples, A. Ceder, Journal of Urban Planning and
Development, 2002.
6. Network models for vehicle and crew scheduling, P Carraresi, G Gallo, European Journal of
Operational Research, 1984
7. Iterated local search for the multiple depot vehicle scheduling problem, B Laurent, JK Hao, Computers
& Industrial Engineering, 2009
8. A decomposition approach for the integrated vehicle-crew-roster problem with days-off pattern, M
Mesquita, M Moz, A Paias, M Pato - European Journal of Operational Research, 2013
9. A heuristic procedure for the crew rostering problem, L Bianco, M Bielli, A Mingozzi, S Ricciardelli,
European Journal of Operational Research, 1992
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