Timetabling with Genetic Algorithms. Timetabling Problem Specifically university class timetabling Specifically university class timetabling Highly complex.

Slides:



Advertisements
Similar presentations
Crew Pairing Optimization with Genetic Algorithms
Advertisements

Algorithm Design Methods (I) Fall 2003 CSE, POSTECH.
Algorithm Design Methods Spring 2007 CSE, POSTECH.
G5BAIM Artificial Intelligence Methods
Constraint Optimization We are interested in the general non-linear programming problem like the following Find x which optimizes f(x) subject to gi(x)
Local Search Algorithms Chapter 4. Outline Hill-climbing search Simulated annealing search Local beam search Genetic algorithms Ant Colony Optimization.
1 An Adaptive GA for Multi Objective Flexible Manufacturing Systems A. Younes, H. Ghenniwa, S. Areibi uoguelph.ca.
Multi-Objective Optimization NP-Hard Conflicting objectives – Flow shop with both minimum makespan and tardiness objective – TSP problem with minimum distance,
Integrated Decision Support and Optimization ICT Roadmap For Minerals & Energy Resources: Industry Roundtable - 16 Oct 2013 James Balzary.
Tuesday, May 14 Genetic Algorithms Handouts: Lecture Notes Question: when should there be an additional review session?
CSC344: AI for Games Lecture 5 Advanced heuristic search Patrick Olivier
An Analysis of the Scheduling of Tutorials and Practicals Karen McGirr Supervised by Dr. Abhaya Nayak.
Date:2011/06/08 吳昕澧 BOA: The Bayesian Optimization Algorithm.
Task Assignment and Transaction Clustering Heuristics.
Iterative Improvement Algorithms
Trading optimality for speed…
Artificial Intelligence Genetic Algorithms and Applications of Genetic Algorithms in Compilers Prasad A. Kulkarni.
An indirect genetic algorithm for a nurse scheduling problem Ya-Tzu, Chiang.
Better Ants, Better Life? Hybridization of Constraint Programming and Ant Colony Optimization Supervisors: Dr. Bernd Meyer, Dr. Andreas Ernst Martin Held.
MAE 552 – Heuristic Optimization Lecture 10 February 13, 2002.
© P. Pongcharoen ISA/1 Applying Designed Experiments to Optimise the Performance of Genetic Algorithms for Scheduling Capital Products P. Pongcharoen,
Evolutionary Computational Intelligence Lecture 8: Memetic Algorithms Ferrante Neri University of Jyväskylä.
Metaheuristics The idea: search the solution space directly. No math models, only a set of algorithmic steps, iterative method. Find a feasible solution.
Island Based GA for Optimization University of Guelph School of Engineering Hooman Homayounfar March 2003.
Applications of Evolutionary Algorithms Michael J. Watts
1 Paper Review for ENGG6140 Memetic Algorithms By: Jin Zeng Shaun Wang School of Engineering University of Guelph Mar. 18, 2002.
Cristian Urs and Ben Riveira. Introduction The article we chose focuses on improving the performance of Genetic Algorithms by: Use of predictive models.
A Comparison of Nature Inspired Intelligent Optimization Methods in Aerial Spray Deposition Management Lei Wu Master’s Thesis Artificial Intelligence Center.
Genetic Algorithms Michael J. Watts
A Survey of Distributed Task Schedulers Kei Takahashi (M1)
HOW TO MAKE A TIMETABLE USING GENETIC ALGORITHMS Introduction with an example.
Optimization Problems - Optimization: In the real world, there are many problems (e.g. Traveling Salesman Problem, Playing Chess ) that have numerous possible.
FINAL EXAM SCHEDULER (FES) Department of Computer Engineering Faculty of Engineering & Architecture Yeditepe University By Ersan ERSOY (Engineering Project)
REDECS ADAPTIVE SHIP MAINTENANCE RESCHEDULING October, 2001 RESIDENCE HOTEL UNITEN KAJANG PATHIAH ABDUL SAMAT (UPM) ALICIA TANG Y. C. (UNITEN)
1 Contents 1. Statement of Timetabling Problems 2. Approaches to Timetabling Problems 3. Some Innovations in Meta-Heuristic Methods for Timetabling University.
Computer Science CPSC 322 Lecture 16 CSP: wrap-up Planning: Intro (Ch 8.1) Slide 1.
Algorithms and their Applications CS2004 ( ) 13.1 Further Evolutionary Computation.
Exact and heuristics algorithms
C OMPARING T HREE H EURISTIC S EARCH M ETHODS FOR F UNCTIONAL P ARTITIONING IN H ARDWARE -S OFTWARE C ODESIGN Theerayod Wiangtong, Peter Y. K. Cheung and.
Local Search Pat Riddle 2012 Semester 2 Patricia J Riddle Adapted from slides by Stuart Russell,
1 An Approach to Accelerate Heuristic for Exam Timetabling Jiawei Li (Michael)
Kanpur Genetic Algorithms Laboratory IIT Kanpur 25, July 2006 (11:00 AM) Multi-Objective Dynamic Optimization using Evolutionary Algorithms by Udaya Bhaskara.
Artificial Intelligence for Games Informed Search (2) Patrick Olivier
Zehra KAMIŞLI ÖZTÜRK Anadolu University, TURKEY Müjgan SAĞIR Eskisehir Osmangazi University, TURKEY.
G5BAIM Artificial Intelligence Methods Dr. Graham Kendall Course Introduction.
Asst. Prof. Dr. Ahmet ÜNVEREN, Asst. Prof. Dr. Adnan ACAN.
CPSC 322, Lecture 16Slide 1 Stochastic Local Search Variants Computer Science cpsc322, Lecture 16 (Textbook Chpt 4.8) Oct, 11, 2013.
© P. Pongcharoen CCSI/1 Scheduling Complex Products using Genetic Algorithms with Alternative Fitness Functions P. Pongcharoen, C. Hicks, P.M. Braiden.
Artificial Intelligence By Mr. Ejaz CIIT Sahiwal Evolutionary Computation.
Distributed Control and Autonomous Systems Lab. Sang-Hyuk Yun and Hyo-Sung Ahn Distributed Control and Autonomous Systems Laboratory (DCASL ) Department.
Applications of Tabu Search OPIM 950 Gary Chen 9/29/03.
COM621 – Interactive Web Development 2015/2016 Module Co-Ordinator: Dr. Pratheepan Yogarajah Room:
Genetic Algorithm(GA)
 Presented By: Abdul Aziz Ghazi  Roll No:  Presented to: Sir Harris.
Local search algorithms In many optimization problems, the path to the goal is irrelevant; the goal state itself is the solution State space = set of "complete"
Warehouse Lending Optimization Paul Parker (2016).
Tabu Search for Solving Personnel Scheduling Problem
Balancing of Parallel Two-Sided Assembly Lines via a GA based Approach
Algorithm Design Methods
Bin Packing Optimization
G5BAIM Artificial Intelligence Methods
An Agent-Based Algorithm for Generalized Graph Colorings
Exam 2 LZW not on syllabus. 73% / 75%.
Metaheuristic methods and their applications. Optimization Problems Strategies for Solving NP-hard Optimization Problems What is a Metaheuristic Method?
Multi-Objective Optimization
Aiman H. El-Maleh Sadiq M. Sait Syed Z. Shazli
Algorithm Design Methods
Algorithm Design Methods
Scheduling is Difficult
Algorithm Design Methods
Presentation transcript:

Timetabling with Genetic Algorithms

Timetabling Problem Specifically university class timetabling Specifically university class timetabling Highly complex problem (NP-Hard) Highly complex problem (NP-Hard) Example: School of Computing Example: School of Computing Hundreds of interactions between students and lecturers per weekHundreds of interactions between students and lecturers per week Complex set of constraintsComplex set of constraints Fixed number of rooms and timeslots (~50 rooms on different sites, 40 hours in a week)Fixed number of rooms and timeslots (~50 rooms on different sites, 40 hours in a week)

Timetabling Problem Approaches include Tabu Search, Tiling Algorithms, Simulated Annealing and Multi-Agent Systems Approaches include Tabu Search, Tiling Algorithms, Simulated Annealing and Multi-Agent Systems GAs a good candidate GAs a good candidate Work well on other scheduling tasksWork well on other scheduling tasks Have previously been applied to timetabling in several different waysHave previously been applied to timetabling in several different ways

Constraints Hard constraints (weight 1.0) – if broken result in invalid timetable Hard constraints (weight 1.0) – if broken result in invalid timetable All classes must be scheduledAll classes must be scheduled No class/lecturers double bookedNo class/lecturers double booked Room capacities must not be exceeded and correct room types usedRoom capacities must not be exceeded and correct room types used Soft constraints (weight 0.01) – relate to quality of a feasible timetable Soft constraints (weight 0.01) – relate to quality of a feasible timetable Classes scheduled within preferred hoursClasses scheduled within preferred hours Hour for lunch is allowedHour for lunch is allowed Bunch classes into groupsBunch classes into groups Avoid long runs of consecutive lecturesAvoid long runs of consecutive lectures

Fitness Function To use GAs or MAs for timetable generation, we need a way of evaluating a timetable To use GAs or MAs for timetable generation, we need a way of evaluating a timetable Timetable fitness: Timetable fitness:

Timetabling GA 1 Two approaches studied Two approaches studied In one, each value (allele) in chromosome represents the timeslot and room given to a module In one, each value (allele) in chromosome represents the timeslot and room given to a module So allele value 0 means room 0, Monday, 9amSo allele value 0 means room 0, Monday, 9am Value 39 means room 0, Friday, 4pmValue 39 means room 0, Friday, 4pm Value 40 means room 1, Monday, 9amValue 40 means room 1, Monday, 9am … Large number of allele values, heavy onus on fitness function Large number of allele values, heavy onus on fitness function

Timetabling GA 2 In the second GA, each allele represents the timeslot assigned to a class In the second GA, each allele represents the timeslot assigned to a class A greedy algorithm assigns the rooms later, taking the classes in order of size and assigning rooms to each in turn A greedy algorithm assigns the rooms later, taking the classes in order of size and assigning rooms to each in turn

Memetic Algorithms (MAs) Builds on the idea of a GA Builds on the idea of a GA GAs have static genes being passed through generations, MAs have memes which can be changed GAs have static genes being passed through generations, MAs have memes which can be changed Adds local search (hillclimbing) to algorithm; aims to reduce search space MA must cover Adds local search (hillclimbing) to algorithm; aims to reduce search space MA must cover

Memetic Algorithms (MAs) cont. Could adapt either of the GAs Could adapt either of the GAs Initially tried both, had to drop one due to time constraints Initially tried both, had to drop one due to time constraints The timetabling MA adds a local search to the genetic + greedy algorithm hybrid The timetabling MA adds a local search to the genetic + greedy algorithm hybrid

Local Search Attempts to resolve problems with the timetable Attempts to resolve problems with the timetable Takes classes which clashed with others and attempts to find a new timeslot where there is no clash Takes classes which clashed with others and attempts to find a new timeslot where there is no clash

Optimisation Fractional factorial screening experiment determines significant factors Fractional factorial screening experiment determines significant factors Response surface experiment finds optimal values for those factors Response surface experiment finds optimal values for those factors Confirmation experiment run to check values Confirmation experiment run to check values

Comparison Comparing number of generations to find a feasible timetable, and fitness over time Comparing number of generations to find a feasible timetable, and fitness over time Found hybrid GA to be best approach Found hybrid GA to be best approach Faster and better quality solutions Faster and better quality solutions MA performed surprisingly poorly – possible local search implementation issue MA performed surprisingly poorly – possible local search implementation issue

Conclusions and Future Work Successfully confirmed that timetabling can be automated with GAs Successfully confirmed that timetabling can be automated with GAs More work required on MA More work required on MA Include extra features of the School timetable Include extra features of the School timetable ElectivesElectives Classes shared with other schoolsClasses shared with other schools Account for distance between buildingsAccount for distance between buildings

Questions?