Overview:
- Credits:
- 7.5
- Level:
- 4
- Semester:
- Summer
- Subject:
- Management Information Systems
- School:
- Business
- Coordinator:
- Assoc Professor Peter Keenan
MIS41260 Decision Modeling and Metaheuristics
Academic Year 2023/2024
This course is devoted to decision science, which considers the application of mathematical modelling and analysis to managerial problems. The first part of the course will focus on optimization from a modelling perspective, and emphasize a structured approach to problem-solving in management situations. The second part of the course is essentially a tutorial on metaheuristics, providing descriptions, implementations and practical applications in the area of business analytics.Show/hide contentOpenClose All
Curricular information is subject to change
Learning Outcomes:
- Develop mathematical models and execute model-based analysis.
- Design and implement algorithms to solve practical optimization problems.
- Apply common analytic methods to make better decisions in complex problems
- Create both Excel solver models and Visual Basic applications for optimization
Network models
Goal Programming
MultiObjective Optimization
Neural Networks
Metaheuristics I: GRASP
Metaheuristics II: Tabu Search
Metaheuristic Applications
| Student Effort Type | Hours |
|---|---|
| Specified Learning Activities | 48 |
| Autonomous Student Learning | 84 |
| Lectures | 24 |
| Total | 156 |
Requirements, Exclusions and Recommendations
Not applicable to this module.
Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy
| Description | Timing | Component Scale | % of Final Grade | ||
|---|---|---|---|---|---|
| Examination: The exam consists of the modelling and resolution with Excel of optimization problems that arise in practical business situations similar to those explained in the class. | Coursework (End of Trimester) | Yes | Graded | No | 50 |
| Project: There is a project assignment and will count towards the final grade. It basically consists on implementing a heuristic algorithm to solve a combinatorial optimization problem. | Throughout the Trimester | n/a | Graded | No | 50 |
| Remediation Type | Remediation Timing |
|---|---|
| In-Module Resit | Prior to relevant Programme Exam Board |
Feedback Strategy/Strategies
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
How will my Feedback be Delivered?
Not yet recorded.
| Name | Role |
|---|---|
| Mr Rafael Marti Cunquero | Lecturer / Co-Lecturer |
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