Module code: ENGM072

Module Overview

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.

Module provider

Chemistry and Chemical Engineering

Module Leader

CECELJA Franjo (Chst Chm Eng)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 95

Lecture Hours: 22

Tutorial Hours: 11

Guided Learning: 11

Captured Content: 11

Module Availability

Semester 1

Prerequisites / Co-requisites

ENG1085 (MATHEMATICS 2) or equivalent

Module content

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 pattern

Assessment type Unit of assessment Weighting
Coursework COURSEWORK 40

Alternative Assessment


Assessment Strategy

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.

Module aims

  • 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.

Learning outcomes

Attributes Developed
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

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

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

Upon accessing the reading list, please search for the module using the module code: ENGM072

Other information

In line with Surrey’s Curriculum Framework, we are committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience. This module contributes to the five pillars in the following ways:



Most of tutorial sessions and the coursework are designed and positioned here such to provide students with and expose them to a more authentic (real world) problem solving experience towards at the end of their programme. Through this students will gain experience of report writing for specific audiences/stakeholders, and ability to demonstrate capability in problem solving directly applicable to a wide range of sectors, which could be cited in interviews and applications to show students’ experience of applying scholarly knowledge to another sector.


Digital capabilities

Significant level of digital skill is a clear output of this module which students gain through a direct and indirect exposure:

  • Digital capability gained through a direct exposure includes the use of General Algebraic Modelling System (GAMS), a software tool for solving optimization and decision making problems. During every tutorial students use GAMS in computer laboratory gaining experience in running the software but also in using computers in more general terms;
  • Digital capability gained through an indirect exposure includes teaching materials and key content available in multimedia forms through the Virtual Learning Environment Surreylearn.


Global and Cultural Capabilities

Engineering in general and Decision Making in particular are global synonyms and the tools and skills used on this module can be used internationally and multiculturally. Students learn about generic engineering and professional code of conduct and the importance of respect in teamwork. Students learn to work together in groups with other students from different backgrounds to solve a problem. This module allows students to develop skills that will allow them to develop applications with global reach and collaborate with their peers around the world.



Students will complete this module with social, ethical and contextually aware knowledge. This module has gender inequality (in the broadest and most inclusive use of the term) at its core, aligned with the UN’s gender equality sustainability goal. It also seeks to ensure community sustainability through the knowledge, skills and awareness students will have upon completion of the module.


Resourcefulness and resilience

This module directly contributes to the educational elements of resourcefulness and resilience as students are honing their autonomous learning to a sophisticated and advanced level. Throughout the module, from concept of formulating the problem to implementation of tools and finding solutions, students will be highly independent, yet supported by their supervisor in the course of tutorials. Students will gain particular skills in informed decision making as this is the core nature of the module They will have to problem solve, navigate ethical considerations and consider their arguments and findings in context. Within a network of support, students will further develop the extent to which they are independent and resourceful learners with a great deal of confidence in conducting and leading independent work towards solution.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Petroleum Refining Systems Engineering MSc 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
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
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

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 2024/5 academic year.