OPTIMISATION AND DECISION MAKING - 2020/1
Module code: ENGM072
In light of the Covid-19 pandemic, and in a departure from previous academic years and previously published information, the University has had to change the delivery (and in some cases the content) of its programmes, together with certain University services and facilities for the academic year 2020/21.
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Nowadays, the design, planning and operations management relay on mathematical models the complexity of which depends on the detail of models and complexity of the problem they represent. In process industry these design and operation planning functions are particularly complex and a wide range of optimisation processes and methodologies are used to minimise risks and/or improve quality in making concomitant decisions. Consequently, the module intends to introduce to students the formulation of the decision making problems and application of optimisation techniques to support decisions with real-life worked examples.
Chemical and Process Engineering
CECELJA Franjo (Chm Proc Eng)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: G200
Module cap (Maximum number of students): N/A
Prerequisites / Co-requisites
ENG1085 or equivalent
Introduction to the decision making process: the basic concept of knowledge management, decision making process and knowledge support to the decisions
Fundamentals of optimisation: concept of feasibility and optimality, convexity, formulation of general optimisation algorithms
Linear programming: concept of feasible point method for optimisation, formulation of equality and inequality constrained optimisation problems, geometry of linear programming, standard form of linear optimisation problem, the concept of basic solutions and extreme points, the Simplex method for linear programming, solution of network optimisation problems
Unconstrained optimisation: optimality conditions, Newton’s method of optimisation, methods of unconstrained optimisation with derivatives, methods of optimisation that do not require derivatives
Nonlinear programming: optimisation with linear equality constraints, optimisation with linear inequality constraints, nonlinear constraints
Integer and Mixed Integer Programming: concept of integer programming, concept of mixed integer linear programming, Branch and bound method for mixed-integer linear programming
General Algebraic Modelling System (GAMS): general principles of programming in GAMS, practical issues in using GAMS, results and result interpretation.
|Assessment type||Unit of assessment||Weighting|
|Examination||EXAM 2 HOURS||60|
The assessment strategy is designed to provide students with the opportunity to demonstrate
· Understanding of scientific principles, methodologies and mathematical methods associated with decision making and optimisation, as well as the ability to formulate and solve particular optimisation problem in the final examination. The coursework tests and amplifies awareness and ability to formulate and solve a practical optimisation problem in engineering.
Thus, the summative assessment for this module consists of:
· Coursework – 40%, 15 hrs (LOs 2, 3, 4)
· Examination – 60%, 2 hrs (LOs 1, 2, 4, 5)
Formative assessment and feedback
- Formative verbal feedback is given during laboratory experiments
- Formative feedback on coursework is given verbally and available on SurreyLearn to provide feedback on understanding of optimisation and decision making process and respective problem formulation and solution.
- A systematic understanding and critical awareness of the importance of the process of optimisation and decision making in process engineering;
- A knowledge of formulating decision-making problems and applying technology to support decisions;
- A knowledge and skill to use the General Algebraic Modelling System (GAMS) in solving engineering problems.
|1||Identify and classify optimisation techniques.||KP|
|2||Select and use optimisation techniques appropriate for a particular problem.||K|
|3||Formulate optimization and decision-support models.||KC|
|4||Use commercial modelling platforms (GAMS) to solve small and large problems.||PT|
|5||Recognise the importance and relevance of using graphical representations, reviewing results and consequent critical thinking, as well as concomitant reporting.||PT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Overall student workload
Independent Study Hours: 117
Lecture Hours: 22
Tutorial Hours: 11
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
- Introduce principles of decision making process in general and optimisation in particular and their implementation and use through theory and worked examples. This is mainly delivered through lectures and laboratory experiments using GAMS on independently worked out examples.
The learning and teaching methods include:
- 2 hours lecture per week x 11 weeks
- 1 hour tutorial x 11 weeks
- 2 hours revision lectures
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Reading list for OPTIMISATION AND DECISION MAKING : http://aspire.surrey.ac.uk/modules/engm072
Programmes this module appears in
|Batteries, Fuel Cells and Energy Storage Systems MSc||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Petroleum Refining Systems Engineering MSc||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Chemical and Petroleum Engineering MEng||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Chemical Engineering MEng||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Information and Process Systems Engineering MSc||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Renewable Energy Systems Engineering MSc||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Process Systems Engineering MSc||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2020/1 academic year.