KL6001 - Optimisation and decision analytics

What will I learn on this module?

‘Optimisation and Decision Analytics’ is designed to introduce students to the problem solving methods that apply to specific business problems. You will learn about the two main areas of operational research - linear programming and simulation. These topics were created to analyse and solve everyday business problems and we will explore both the theoretical and practical approaches to solving such problems using software such as Excel and R.

Outline syllabus
Simulation Models: techniques for generating both uniform and non-uniform pseudo random numbers, bootstrapping, tests of randomness and stochastic integration.
Linear Programming: Formulation of a problem for two or more variables; graphical solution; sensitivity analysis; simplex algorithm and the Big M method. Duality and its interpretation. Integer programming using the Cutting Plane and Branch and Bound Algorithms. Kuhn-Tucker conditions and quadratic programming. Use of Excel to solve these linear programming problems.

How will I learn on this module?

You will learn through a sequence of lectorials which combine formal lectures and hands on experience using computer software. Classes will be scheduled in our modern computer laboratories enabling you to apply the techniques presented in the lecture part of the session and, in this way, deepen your understanding of the material and develop your practical skills. This, in turn, develops your confidence to explore the subject area further as an independent learner outside the classroom.

Formative feedback is available weekly in the classes as you get to grips with new techniques and solve problems. In addition, we operate an open door policy where you can meet with your module tutor to seek further advice or help if required. Your ability to select appropriate techniques and use the appropriate computational approach to solve simulation problems is assessed in a lab-based exam after the first semester whereas your ability to appraise various business problems and the wider area of linear programming and develop appropriate mathematical solutions is also tested in a laboratory exam at the end of the module.

General feedback on assessments will be given in class followed by individual feedback. An opportunity to discuss work further will be available on an individual basis when work is returned and also through our open door policy.

How will I be supported academically on this module?

Direct contact with the teaching team during the lectorials will involve participation in both general class discussions as well as one to one discussions during the hands-on part of the class. This gives you a chance to get immediate feedback pertinent to your particular needs in this session. Further feedback and discussion with the teaching team area are also available at any time through our open door policy. In addition, all teaching materials and supplementary material (such as interesting articles) are available through the e-learning portal.

What will I be expected to read on this module?

All modules at Northumbria include a range of reading materials that students are expected to engage with. The reading list for this module can be found at: http://readinglists.northumbria.ac.uk
(Reading List service online guide for academic staff this containing contact details for the Reading List team – http://library.northumbria.ac.uk/readinglists)

What will I be expected to achieve?

Knowledge & Understanding:
1. Determine appropriate statistical methods to generate pseudorandom numbers for a variety of statistical distributions and evaluate their suitability.
2. Formulate, analyse and solve optimisation problems in a business context using linear programming techniques.

Intellectual / Professional skills & abilities:
3. Develop the appropriate linear programming technique for the given problem, use the appropriate software to generate relevant metrics and evaluate the results to create a report
4. Test the randomness of generated pseudorandom numbers and critically assess their usefulness.

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
5. Interpret and critically analyse the mathematical aspects of selected business problems, obtain solutions and interpret those solutions.

How will I be assessed?

SUMMATIVE
1. Lab based examination (50%) – 1,3,5
2. Lab based examination (50%) –2,3,5


Formal examinations at the end of each semester

FORMATIVE
Formative assessment will be available on a weekly basis in the lectorials through normal lecturer-student interactions, allowing them to extend, consolidate and evaluate their knowledge.

Formative feedback will be provided on student work and errors in understanding will be addressed reactively using individual discussion. Solutions for laboratory tasks will be provided after the students have attempted the questions, allowing students to receive feedback on the correctness of their solutions and to seek help if matters are still not clear.

Pre-requisite(s)

None

Co-requisite(s)

None

Module abstract

‘Optimisation and Decision Analytics’ is designed to introduce students to the operational research methods that apply to specific business problems. You will learn about a range of linear programming techniques as well as Monte Carlo simulation – topics created to analyse and solve everyday business problems. This subject area was created during the Second World War to solve practical problems and, as such, is a very practical subject.

You will learn through a series of lectorials which combine formal lectures and hands on experience using computer software. Classes will be scheduled in our modern computer laboratories enabling you to apply the techniques presented in the lecture part of the session and, in this way, deepen your understanding of the material and develop your practical skills.

On this module, you will learn to solve problems in a variety of areas that apply to business. The module is assessed using two examinations - each examination based on the material given in one semester. The examinations are based on computer laboratories so that you can use the practical skills that you have learnt throughout the module to solve problems.

Course info

UCAS Code G100

Credits 20

Level of Study Undergraduate

Mode of Study 3 years full-time or 4 years with a placement (sandwich)/study abroad

Department Mathematics, Physics and Electrical Engineering

Location City Campus, Northumbria University

City Newcastle

Start September 2022

Fee Information

Module Information

All information on this course page is accurate at the time of viewing.

Courses starting in 2021 are offered as a mix of face to face and online learning. We continue to monitor government and local authority guidance in relation to Covid-19 and we are ready and able to flex accordingly to ensure the health and safety of our students and staff.

Contact time is subject to increase or decrease in line with additional restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors, potentially to a full online offer, should further restrictions be deemed necessary in future.

Our online activity will be delivered through Blackboard Ultra, enabling collaboration, connection and engagement with materials and people.

 

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