Download MPE(S)- Planning and Scheduling Methodologies

MPE(S)- Planning and Scheduling
Methodologies
Metodologias de Planeamento e
Escalonamento
http://paginas.fe.up.pt/~eol/PRODEI/mpe1314.htm
Eugénio Oliveira / Henrique L. Cardoso
{eco, hlc}@fe.up.pt
DEI- Faculty of Engineering of the University of Porto
LIACC – Artificial Intelligence and Computer Science Lab.
2014-PRODEI-MIEIC – Eugénio Oliveira
.
NIAD&R – Distributed Artificial Intelligence and Robotics Group
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Planning and Scheduling
• Planning and scheduling are the means by which an
organization/entity chooses a course of actions, before
performing it, to meet some objective.
• In computational and AI terms:
– planning focuses on selecting and ordering (sequencing?) the actions to
reach a goal
– scheduling centers on assigning time and resources to action sequences
/ events
• The AI approach focus on exploiting knowledge specific to the
organization/entity and its field of operations ('domain
knowledge')
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
2
Planning and Scheduling
• Planning and scheduling are often interconnected processes and
techniques for one of the processes are also often applicable (or
are at least complementary) to the other process.
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
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Planning
• Planning is the process of generating representations
(possibly partial) of future behavior prior to the use of such
plans to constrain or control that behavior
• Outcome:
– Usually a list or set of actions, with temporal and other constraints on
them, for execution by some agent or agents
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
4
Scheduling
• Scheduling concerns the allocation of limited resources
to tasks or events over time in order to reach an objective
Resources and tasks:
Resources may be machines in a shop-floor, CPU memory,
runways in a airport…
(Characteristic: resources are limited)
Tasks may be operations in a production process, execution of PC
programs, takeoffs and landings in a airport…
(Characteristic: Task execution uses Resources)
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
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Scheduling
• Objectives and performance measures may also be of
different kinds:
> profit Maximization
> production Maximization
> time Minimization
> Trade-off among different commitments
> etc
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
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Topics on Pl&Sc
• Applications:
– Empirical studies of existing planning/scheduling systems;
– domain-specific techniques; heuristic techniques;
– User interfaces for planning and scheduling.
• Formal Models:
– Reasoning about knowledge, action, and time;
– Representation Formalisms and ontologies for Pl⪼
– Solutions search methods
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
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Topics on Pl&Sc
• Intelligent Agency (agent-based methods):
– Resource-bounded reasoning;
– distributed problem solving;
– integrating reaction and deliberation
– “Swarm Intelligence”
• Automatic Learning (ML):
– Learning in the context of planning and execution;
– learning new plans; conditions for selecting plans; learning
new operators;
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
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Methods for Pl&Sc
• Memory Based Approaches:
– Case-based planning/scheduling; plan and operator reuse;
• Reactive Systems:
– Environment-driven behaviors;
– reactive control; behaviors in the context of minimal
representations;
• Robotics:
– Motion and path planning; actions planning and control;
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
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Methods for Pl&Sc
• Iterative Improvement search Techniques:
– Genetic Algorithms, Simulated Annealing, Tabu Search,
Neural Nets, etc. applied to Scheduling and/or Planning.
• To deal with “combinatorial explosion”
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
10
Syllabus for Planning and Scheduling
• Definitions of Planning and Scheduling.
– Planning vs Scheduling.
• Introduction to Planning and Scheduling
– traditional methodologies, problems and applications: PERT;
CPM.
• Automatic Plan Generation:
– Means-Ends Analysis, Linear, non-linear, hierarchical and partial order
planning. Planning and Learning: Plan generalization. Planning
problems and applications
• Deterministic and stochastic scheduling models:
– Single machine, parallel machine,
– Flow Shop (continuous flow of processing tasks with a minimum of idle time), Job Shop,
Open Shop.
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
11
Syllabus for Planning and Scheduling
• Scheduling problems complexity classes: The NP-Complete
problem class.
• Scheduling algorithms:
– Linear programming, dynamic programming, “Branch and Bound”,
Local search heuristics, Tabu Search, Simulated Annealing, Genetic
Algorithms, Neural Networks, Constraint Satisfaction. Cooperative
planning and scheduling.
– Linear programming (linear programming is a technique for the
optimization of a linear objective function)
– Dynamic programming (In computer science, dynamic programming is
a method for solving complex problems by breaking them down into
simpler sub-problems –stages. Each one divided into states).
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
12
Syllabus for Planning and Scheduling
• Real planning and scheduling problem modeling:
– Staff allocation, school timetabling, exams timetabling, International
Timetabling Competition, airline scheduling, train scheduling.
Planning and Scheduling real problems Modeling
- Bus and Train Scheduling, Airline Scheduling, Sports Scheduling
Timetabing, Exames scheduling ...
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
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Outcomes
• Acquire knowledge of current state of the art and trends in
planning and scheduling
• Understanding the problems and selecting appropriate
techniques to model and solve them
• Have a broad critical understanding of how Artificial
Intelligence may be used for Pl&Sc
• Understand the challenges behind cooperative planning and
scheduling systems for dynamic multi-agent environments;
• Specify and Implement a System including
Planning/
Scheduling algorithms
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
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Teaching Methods (1)
• Emphasis will be put on problem solving, decision making,
creative thinking/design
• Classes
• Papers Reading
• Tools experimentation and analysis
• Project-oriented Learning
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
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Teaching Methods (2)
• lectures, paper readings, analysis, writing, oral presentations,
design, implementation, experimentation, among others
• Detailed feedback given to students about the quality of their
research work and learning process.
• This high-level teaching method will enable students to
increase their skills in research in all other areas related to
informatics and computer science.
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
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Grading
• This is a research-oriented course, intended first to teach the
students the state of the art in planning and scheduling and
then to help them to do a simple project and a paper for
submission to a conference about this subject.
• Evaluation of students will be based on:
– Fullfilment of a Practical Assignement with demonstration, oral
defense and production of a paper:
– No Exam!
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
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Grading
• Distributed Evaluation without Exam:
– Project/Practical Assignments: 100%
• Intermediate Oral Presentation
30%
• Planning/Scheduling project implementation :
– Scientific Paper (8/12 pages)
40%
– Project Presentation (Oral / Demo) 30%
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
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Mini-Projects
• Timetabling (look at the International Timetabling Competition – ITC)
http://www.cs.qub.ac.uk/itc2007/
• Scheduling problems for the Aviation.
– Using Swarm Intelligence (ant c., particle s., firefy alg.)
• International Planning Competition (deterministic, learning, uncertainty)
http://ipc.icaps-conference.org/
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
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Mini-Projects
• Task planning for a Manipulator Robot
Apply non-linear algorithm for an “Assembly Task”
• Mobile Robot path planning
• Getting acquainted with LISA (Library of Scheduling Algorithms)
(http://lisa.math.uni-magdeburg.de/download.php)
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
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Bibliography
Joseph Leung, Laurie Kelly and James H. Anderson, Handbook of
Scheduling: Algorithms, Models, and Performance Analysis, CRC Press, Inc.
Boca Raton, USA, 2004, ISBN:1584883979
Peter Brucker, Scheduling Algorithms, Fifth Edition, Springer, New York, Inc.,
2007, ISBN: 978354069515
Handbook of Metaheuristics, Editors: Michel Gendreau, Jean-Yves Potvin,
International Series in Operations Research & Management Science,
Volume 146 2010, Springer. ISBN: 978-1-4419-1663-1 (Print) 978-1-44191665-5 (Online)
Michael Pinedo. Scheduling: Theory, Algorithms and Systems, Prentice Hall,
2001.
Malik Ghallab, Dana Nau, and Paolo Traverso. Automated Planning – Theory
and Practice, Elsevier/Morgan Kaufmann, 2004.
Stuart Russel and Peter Norvig, Artificial Intelligence: A Modern Approach,
Prentice-Hall, 3rd Edition, 2010
Barry McCollum et al., 2nd International Timetabling Competition, [online],
available at: http://www.cs.qub.ac.uk/itc2007/ (consulted on 15/02/2011)
ICAPS, International Conference on Automated Planning and Scheduling,
[online], available at http://ipc.icaps-conference.org/ (consulted on
15/02/2011)
2014-PRODEI-MIEIC – Eugénio Oliveira
NIAD&R – Distributed Artificial Intelligence and Robotics Group
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