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.

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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

Indicative Module Content:

Network models
Goal Programming
MultiObjective Optimization
Neural Networks
Metaheuristics I: GRASP
Metaheuristics II: Tabu Search
Metaheuristic Applications

Student Effort Type Hours
Specified Learning Activities


Autonomous Student Learning






Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % 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


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


Carry forward of passed components
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
Lecture Offering 51 Week(s) - 43, 44 Fri 10:00 - 12:50
Lecture Offering 51 Week(s) - 43 Mon 10:00 - 12:50
Lecture Offering 51 Week(s) - 44 Mon 10:00 - 12:50
Lecture Offering 51 Week(s) - 43, 44 Thurs 10:00 - 12:50
Lecture Offering 51 Week(s) - 43, 44 Tues 10:00 - 12:50
Lecture Offering 51 Week(s) - 43, 44 Wed 10:00 - 12:50

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