MIS41100 Hot Topics in Analytics

Academic Year 2019/2020

A compressed module based on readings at the cutting edge of analytics research. A detailed literature review on a topic of the student’s choice may form the basis of the student’s Capstone project.

2019-2020: this module will focus on simheuristics and learnheuristics which embed, respectively, simulation and machine learning in heuristics and metaheuristics.

Show/hide contentOpenClose All

Curricular information is subject to change

Learning Outcomes:

On completion of the module students should be able to:
● Describe in detail a recent line of research in analytics
● Summarise the research aims and achievements
● Describe context including multiple previous research papers
● Describe open questions
● Describe the potential benefit to business of the research
● Stay up to date throughout their career by reading new research, deciding when it is suitable for a given business problem, and applying it.

Indicative Module Content:

Summary of Contents
1. Intro to Optimization, Heuristics, and Metaheuristics
2. Biased-Randomized Algorithms (BRAs)
3. Applications of BRAs in transport, logistics, production, finance, and marketing
4. Simheuristics: combining metaheuristics with simulation for stochastic systems
5. Applications of Simheuristics in transport, logistics, production, and finance
6. Learnheuristics: combining metaheuristics with machine learning for dynamic systems
7. Applications of Learnheuristics in transport and logistics
8. Agile Optimization: concept and applications in smart cities

Student Effort Type Hours
Specified Learning Activities


Autonomous Student Learning


Online 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
Group Project: 2 reports to complete in small teams of 1 to 3 students. Typical activity: solving lab problems with Python and/or other software (Simio, Open Solver, etc.), analyzing selected articles, etc. Varies over the Trimester n/a Graded No


Continuous Assessment: Individual class presentations and active participation in class, including forums, quality of questions, etc. Throughout the Trimester n/a Graded No


Assignment: Individual writing of an original short paper on a scientific topic. Varies over 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

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Feedback will aim to help students understand how they can perform better on the assessment.

Name Role
Angel Juan Lecturer / Co-Lecturer
Lecture Offering 51 Week(s) - 39, 40, 41, 42, 43, 44, 45, 46 Wed 11:00 - 14:50

Discover our Rankings and Accreditations