DepartmentSchool of Mathematics and Physics
February and October
Applications accepted all year round
The work on this project will:
- Investigate the optimality of a feasible inventory policy for a remanufacturing system that models returns from past sales.
- Propose and investigate a heuristic inventory policy that can be practically implemented for the system.
- Provide a theoretical guarantee, supported numerically, for the closeness of the heuristic inventory policy to optimality.
Environmental sustainability has attracted increasing attention in recent years due to human impact on the global environment that has led to unfavourable climate change taking place in the world now. An example of such human activities is the abandonment of used products which are left to disintegrate on their own without proper handling. This depletion of resources has resulted in global warming potential.
Remanufacturing, an advanced form of recycling, is often considered an environmentally friendly choice of end-of-life option. It is a preferable option for environmental gains with the advantage of alleviating the depletion of resources, hence reducing global warming potential. Examples of remanufactured products include engines, photocopiers and toner cartridges.
In this project, using dynamic programming methodology, we consider a periodic review inventory model for a remanufacturing system that models its returned products from past sales. We investigate inventory policies for remanufacturing these cores to serviceable products that are as good as new in a finite horizon setting so that system cost is kept as low as possible.
This research will be conducted at the School of Mathematics and Physics, University of Portsmouth. The School provides a conducive environment to carry out the proposed research, with suitable resources, such as, a personal computer with relevant software, access to a high performance computer cluster for intensive computing and a suitably equipped library with online resources.
The student will be mainly supervised by Dr. Sim, the first supervisor of this project. Dr. Sim has more than 10 years of experience working on inventory optimisation, with a number of papers in the area published in widely-read, international operational research journals. He has experience supervising graduate students, and is keen to work with students. With the skills developed doing the project, the student will be well placed to either pursue a career in industry or academia upon graduation.
Fees and funding
Visit the research subject area page for fees and funding information for this project.
Funding availability: Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).
Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
A good first degree or MSc degree in Mathematics, Operational Research or Industrial/Manufacturing Engineering with a strong quantitative background. The candidate should have the ability to read and write mathematical proofs, and have programming experience.
How to apply
When you are ready to apply, please follow the 'Apply now' link on the Mathematics PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.