Study information

Modelling, Simulation and Machine Learning for Operations Management - 2023 entry

MODULE TITLEModelling, Simulation and Machine Learning for Operations Management CREDIT VALUE15
MODULE CODEENGM039 MODULE CONVENERDr Martino Luis (Coordinator), Prof Voicu Ion Sucala (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11 0 0
Number of Students Taking Module (anticipated) 300
DESCRIPTION - summary of the module content

The knowledge you gain within this module will enhance your ability of the use of mathematical modelling techniques, computer simulation, and machine learning to tackle modern operations and supply chain management problems, as well as, to make informed strategic decisions.

This module focuses on developing your skills in operations research/ management science, simulation modelling, and machine learning to solve practical engineering and management problems drawn from various functional areas (operations, supply chain, logistics, quality, finance, etc.) in different organisations (manufacturing, service, public sector, etc.). The module provides you with advanced analytical tools and methods to help you make optimal decisions. This module equips you with practical hands-on experience to the theories and techniques of modelling and simulation in a variety of contexts, and you will gain expertise in simulation software.

In this module, you will undertake some laboratory activities to learn industry 4.0 methodology in the Exeter Digital Enterprise System (ExDES) laboratory. You will also learn a sound foundation of machine learning applied to manufacturing and supply chain processes. You don’t need any prior knowledge of data science to understand and be able to apply these machine learning methods and tools.

The module is suitable for non-specialist students.

The module is recommended for interdisciplinary pathways.

AIMS - intentions of the module
This module aims to deliver an in-depth understanding of operations research, computer simulation, and machine learning to analyse and solve important managerial problems in engineering by using operational research methods, simulation modelling, and machine learning techniques. The knowledge you gain on this module will help you to appreciate a range of associated courses, especially in manufacturing and service system modelling and simulation, operations and supply chain analysis, Industry 4.0 technologies such as IoT and digital twins of manufacturing and supply chain processes.
 
The module will train you to use specific software tools, i.e., Excel Solver, POM QM, Tecnomatix and/ or AnyLogic. This module will also aim to provide you with hands-on experience within the Exeter Digital Enterprise System (ExDES) laboratory using the Industry 4.0 demonstrator, IoT gateway and its associated software.
INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

Discipline and Module Intended Learning Outcomes:

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge: 

1. Demonstrate knowledge and understanding of operational research techniques for production planning and control, discrete event simulation, system modelling and analyses, and decision analysis;
2. Utilise optimisation techniques and mathematical models to concrete industrial situations;
3. Develop digital twin models by creating and running computer simulation models for manufacturing or service industry
4. Apply machine learning theories to conceptualise managerial problems, accurately present data, interpret and analyse results to make informed strategic decisions;

Discipline Specific Skills and Knowledge: 

5. Demonstrate advanced problem-solving skills and adapt such skills to solve increasing complex industrial problems;
6. Appreciate industrial situations that can be improved through simulation modelling, construct simulation models and utilise machine learning to analyse and solve challenging real-world problems.;

Personal and Key Transferable / Employment Skills and Knowledge: 

7. Prove experience in project management skills, through set problem sheets and assignments, in terms of setting targets, scheduling and progress control;
8. Enhance critical thinking, problem solving skills, and independent learning skills; 
9. Exhibit team work skills, initiative and responsibility through group work and problem solving;
10. Enhance technical writing report skills, presentation skills, and communication skills to communicate work to technical and non-technical audiences.
SYLLABUS PLAN - summary of the structure and academic content of the module

Whilst the module’s precise content may vary from year to year, it is envisaged that the syllabus will cover some or all of the following topics:

  • Introduction to Operation Research;
  • Linear Programming: graphical modelling and solution; what if analysis of Linear Programming models;
  • Dynamic Programming, Transportation problems; integer problems;
  • Queuing models; steady state queues with one server and several servers;
  • Introduction to modelling and simulation: discrete event simulation; construction of simulation models;
  • Discrete even simulation using Technomatix; data collection in ExDES lab, group project on developing a digital twin of a lab process;
  • Foundation of Machine Learning: Machine Learning Workflow and Applications in Engineering;
  • Machine Learning methods (Supervised and Unsupervised Learning), tasks (Regression and Classification) and algorithms (Generalised Linear Models, KNN ad K-Means);
  • Introduction to basic concepts of training and testing;
  • Introduction to Deep Learning, concepts of Decision Trees, Ensemble Learning and Neural Networks;
  • Demonstration of these methods and techniques on engineering problems
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 41 Guided Independent Study 109 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching activities  22 Lectures
Scheduled Learning and Teaching activities  11 Laboratory sessions
Scheduled Learning and Teaching activities  8 Computer sessions
Guided independent study 109 Lecture and assessment preparation; wider reading

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
None      

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 0 Written Exams 0 Practical Exams 20
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Linear programming e-quiz 20 1 hour 1,2 Automatic feedback
Modelling and simulation group project 60 10 pages 3,4,6-9 Written feedback
Engineering Competence Structured Assessment - interview 20 10 minutes 5,10 Oral feedback

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
All above Written Exam (100%) All Referral/Deferral Period

 

RE-ASSESSMENT NOTES
Deferral – if you have been deferred for any assessment you will be expected to submit the relevant assessment. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.
 
Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be expected to submit the relevant assessment. The mark given for a re-assessment taken as a result of referral will be capped at 50%.
 
Where there are practical reasons why the original form of assessment on a module cannot be replicated for referral or deferral purposes, an alternative form of assessment must be used. Examples of when this approach is justified include where the original assessment relied on fieldwork, group work, access to specialist equipment, or input from visiting staff; or where the process of assessment throughout the module was intricate, involving many assessments. The method of reassessment should address as many of the module’s intended learning outcomes as is possible.
RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Reading list for this module:

Taha, H. A. 2017. Operations Research: An Introduction. 10th Edition, Pearson, 978-0131360143.

Hillier, F. S. & Hillier, M.S. 2019.  Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets.  6th Edition.  McGraw Hill, 9781260091854.

Ragsdale, C. 2022 Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Business Analytics. 9th Edition. Cengage Learning, 9780357132098.

Fishman, G. S. 1979. Principles of Discrete Event Simulation. John Wiley & Sons, 000-0-471-04395-8.

Law, A. 2015. Simulation Modeling and Analysis. 5th Edition. McGraw Hill, 9781259010712.

Gopal, M. 2019. Applied Machine Learning. 1st Edition. McGraw Hill, 9781260456844.

Anderson, D., Dennis, S., Williams, T., Wisniewski, M., & Pierron, X. 2017.  An Introduction to Management Science: Quantitative Approaches to Decision Making. 3rd Edition. Cengage Learning 9781473729322.

Reading list for this module:

There are currently no reading list entries found for this module.

CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 4th July 2023 LAST REVISION DATE Thursday 5th October 2023
KEY WORDS SEARCH Management science; decisions; systems; operational research; simulation, machine learning, digital twin

Please note that all modules are subject to change, please get in touch if you have any questions about this module.