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DRPS : Course Catalogue : Business School : Common Courses (Management School)

Postgraduate Course: Soft Computing (CMSE11448)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryReal life decision problems are often too complicated to be modelled by e.g., mathematical tools. Even if they are modelled, these type of problems are often intractable and extremely challenging to solve. In recent years, the emergence of soft computing as an alternative way of solving problems in areas such as optimisation has attracted attentions from both academics and practitioners. This course offers alternative approaches to solve complex problems which could otherwise be difficult to solve by traditional techniques. It aims at training students in the field of Soft Computing with emphasis on uncertainty modeling (e.g., Bayesian Networks) and approximation (e.g., heuristics, metaheuristics ,hyperheuristics and evolutionary computations) to address decision making problems in business. Variety of applications will be examined including transportation, logistics and fleet management. The course further aim is to enhance students understanding of the critical nature of designing and/or selecting appropriate methods for solving complex decision problems. It provides opportunities for students to learn from each other, from practitioners in the field, and from the latest theoretical and applied research in the field.
Course description This course has been designed around two main topics of approximation techniques and Bayesian modelling. At the beginning of the course, a project will be introduced in the area of location analysis or logistics. Students are expected to work on the project individually and as a member of a group during the term time.

Content outline:
The first part of the course begins with an introduction to soft computing followed by a brief discussion on basic approximation techniques such as CLS. Built upon this introduction, the advanced techniques e.g., meta-heuristics, evolutionary computation and hyper-heuristics will be discussed in the following weeks. In the second part pf the course, Bayesian Networks (BNs) and its applications to Business Analytics are discussed. The focus will be on how to design a BN and how to perform inference analysis on a hypothesis of interest.

Student Learning Experience:
Students are expected to learn basic concepts and theories from lectures. Working on their individual and group assignment during the term time, students will learn how to apply the concepts and theories learned in the lectures to solve a complex business problem such as the one described in their assignment brief.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2022/23, Not available to visiting students (SS1) Quota:  None
Course Start Block 3 (Sem 2)
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 10, Seminar/Tutorial Hours 5, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 83 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 80% coursework (individual) - assesses all course Learning Outcomes
20% coursework (group) - assesses all course Learning Outcomes
Feedback Formative feedback: TBC
Summative: Feedback will be provided on the course assessment.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Critically discuss and express the concept and methods of soft computing using proper terminologies.
  2. Analyse decision problems in business settings using soft computing techniques.
  3. Implement a soft computing technique, interpret results and formulate managerial guidelines and make recommendations.
  4. Communicate findings effectively and efficiently verbally and in writing.
Learning Resources
Recommended Journals:
European Journal of Operational Research
INFORMS journal on Computing
Transportation Science
Computers and Operations Research
Transportation Research-Part B

Additional Information
Graduate Attributes and Skills Research & Enquiry:

On completion of the course, students should be able to:

- Understand how optimisation and other complex problems could be modelled using approximation and Bayesian techniques

- Understand the basic framework of heuristic, metaheuristic and hyper-heuristic approaches as well as Bayesian Networks and know how to apply them to practical situations

- Identify underlying assumptions of the approximation and Bayesian modelling techniques and critically evaluate their validity on applications

Personal & Intellectual Autonomy:

On completion of the course, students should be able to:

- think independently and exercise personal judgement while solving complex optimisation problems

- analyse situations and applying creative and inventive thinking to develop an appropriate solution technique to an optimisation problem

- implement the solution technique(s) and review decisions based on appropriate techniques

Communication skills

On completion of the course, students should be able to:

- explain implications of network models/analysis to general audiences

- develop appropriate documentation to communicate the result of a small project
KeywordsNot entered
Course organiserDr Nader Azizi
Tel: (0131 6)51 1491
Course secretaryMs Friederike Traiser
Tel: (0131 6)50 8072
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