Module Details

Module Code: COMP9070
Title: Planning & Scheduling
Long Title: Planning & Scheduling
NFQ Level: Expert
Valid From: Semester 1 - 2018/19 ( September 2018 )
Duration: 1 Semester
Credits: 5
Field of Study: 4811 - Computer Science
Module Delivered in: 1 programme(s)
Module Description: Planning and scheduling are emerging areas derived from Artificial intelligence with important applications areas ranging from process planning and space mission control to scheduling work rosters for aircraft crews. In this module, the learner will be introduced to the theory of planning and scheduling and the challenges behind co-operative approaches applied to the field. Furthermore, this module will explore current state-of-the-art techniques to solve complex planning and scheduling problems.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Assess the benefits and limitations of complete and incomplete heuristics to solve planning and scheduling problems.
LO2 Categorize planning and scheduling problems with respect to their computational complexity.
LO3 Design and implement a solution to a real-world planning problem.
LO4 Analyse the performance of planning and scheduling heuristics to solve real world problems.
LO5 Design and implement a planning and scheduling solution that can deal with incomplete or uncertain information and operate in dynamic environments.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Introduction
Introduction and overview of complex real-world problems that are solvable using planning and scheduling techniques. Problems such as school timetabling, staff allocation, airline scheduling etc.
Classical planning
Formalisation of planning problems and logic-based representations for discrete planning, i.e., set representation and first-order logic
Planning algorithms
Mainstream classical planning algorithms such as: Forward State-Space Search, Breadth-First Search, Uniform-Cost Search, A*. Encoding the planning problem into a set of SAT (Boolean Satisfiability) and CSP (Constraint Satisfaction Problems) problems
Scheduling problems
Classical scheduling problems such as: single and multiple machine scheduling (identical Vs. different), the job-shop problem, flow Shops, and open Shops, staff allocation, school timetabling, airline scheduling, etc
Scheduling algorithms
Exact or complete algorithms to find optimal solutions, e.g., mixed integer programming, constraint programming, and dynamic programming for scheduling; Incomplete heuristic algorithms to find near-optimal solutions, e.g., First-come first-served (FIFS), first-in first-out (FIFO), Shortest Job First (SJF), Round Robin (RR) scheduling, etc
Planning and scheduling with uncertainty
Predictive-reactive scheduling to repair solutions after an unexpected event; proactive-reactive robust planning & scheduling that incorporate a degree of disruption or unexpected events
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Project % of Total Mark 40
Timing Week 6 Learning Outcomes 1,2,3
Assessment Description
In this project the students will be given an planning problem and the students will have to implement heuristic solution to tackle the problem.
Assessment Type Project % of Total Mark 60
Timing Sem End Learning Outcomes 1,2,3,4,5
Assessment Description
In this project the students will be given an scheduling problem with and without uncertainty and the students will have to implement heuristic solution to tackle the problem and critically evaluate the performance of their solution
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Presentation of theory Every Week 2.00 2
Lab Contact Lab supporting lectures Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Student undertakes independent study. The student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Presentation of theory Every Week 2.00 2
Lab Contact Lab supporting lectures Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Student undertakes independent study. The student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • Michael L. Pinedo. (2016), Scheduling: Theory, Algorithms, and Systems, 5. Springer, [ISBN: 3319265784].
  • Stuart Russell and Peter Norvig. (2016), Artificial Intelligence: A Modern Approach, 3. 10, 11, Prentice Hall, [ISBN: 1292153962].
Supplementary Book Resources
  • Malik Ghallab, Dana Nau and Paolo Traverso. (2004), Automated Planning and Acting, Cambridge University, [ISBN: 9781107037274].
  • Jussi Rintanen/Armin Biere, Marijn Heule, Hans van Maaren, Toby Walsh. (20), Handbook of Satisfiability, 15, IOS Press, 2009, [ISBN: 978-1-58603-9].
  • Philippe Baptiste, Philippe Laborie, Claude Le Pape, Wim Nuijten/Francesca Rossi Peter van Beek Toby Walsh. (2006), Handbook of Constraint Programming, 1. 22, Elsevier Science, [ISBN: 9780444527264].
Supplementary Article/Paper Resources
  • Laura Climent, Richard J. Wallace, Miguel A. Salido, Federico Barber. (2014), Robustness and Stability in Constraint Programming under Dynamism and Uncertainty, Journal of Artificial Intelligence Research, 49, p.49--7,
    https://doi.org/10.1613/jair.4126
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_KARIN_9 Master of Science in Artificial Intelligence 2 Elective