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PhD Project – End-of-life Planning for mobile phones.

Supervisors: Dr. Anna-Lena Sachs and Dr. Idris Eckley (Lancaster University), and Louise LLoyd (Tesco Mobile)

Description: The management and control of stock in a business with integrated online and offline storefronts facing uncertainty in demand poses a great deal of operational challenges. Historically there has been much research into inventory control under uncertainty. However, inventory control at the end of sales life remains an under-researched area within the literature. In particular, the challenge of optimising stock levels to ensure remaining stock is sold before the end of sales life, as well as preventing stockouts and unsatisfied customers, is of vital importance. It is this challenge that lies at the heart of this PhD and will lead to development of novel operational research methodology at the intersection of three research areas: omnichannel retailing, end-of-life product management and stochastic inventory control. Novel methods will be proposed to create effective inventory control policies. Dynamic programming will play a key role in this project, both in method development, as well as the need for heuristics to account for the well known “curse of dimensionality”. The project is in partnership with Tesco Mobile.


Undergraduate Dissertation: Operational Research and the Simplex Algorithm
Supervisor: Dr. Richard Danyi

From Supply chain management and finding better ways to manage stock, to financial institutions wanting to optimise profits; Linear Programming provides a pragmatic, straightforward and efficient way to turn real-world problems into a set of linear inequalities and equations. From a more general Operational Research standpoint, Linear Programming is an integral tool to help find optimal solutions to a range of problems within the area. By focusing on the most well known tool for optimising Linear Programming problems, namely the Simplex Method, we will discover how this is apparent from a practical and theoretical standpoint. Attaining a solid footing in what makes the method so adaptable to any n −dimensional linear problem. We will also dive into areas like non-linear programming and Integer programming with Simplex, alongside a comparison to interior points methods, analysing how these other methods work to achieve similar results. Culminating in a final scrutiny of how the method holds up in the current world of fast computations, new innovations and a changing mathematical landscape.


Masters Dissertation: Stochastic Modelling of Malaria
Supervisor: Dr. Jie Yang

By considering a compartmental model for the spread of malaria, this thesis aims to evaluate the effectiveness of stochastic models in the spread of infectious diseases. This is achieved through introducing different variations and avenues which can be taken with stochastic modelling of the spread of disease by delving into the evolution of discrete and continuous times models that employ randomness to account for the uncertainty that is prevalent in real world cases. Models range from simple discrete-time Markovian processes, to implementing theory from stochastic calculus to create stochastic differential equations from underlying ordinary differential equations. A sensitivity analysis on the various models is provided. Key characteristics for disease models such as the basic reproduction number and the disease free equilibrium are explored. A model validation is included using a a dataset concerning cases in an endemic region of Malaysia. Finally, the thesis culminates in a discussion and conclusion on the practicalities, utility and viability of stochastic compartmental disease modelling in comparison to other techniques employed in both historic and contemporary times.



Lancaster University (2021-Present)

  • Sprint Report on Statistical Learning for Decision: Link
  • Research Topic report on the Particle Filter: Link
  • Research Topic report on the Data driven newsvendor: Link
  • Presentation on the Data driven newsvendor: Link

University of Hull (2017-2020)

  • Overview of Astronomical object Fornax A: Link
  • Computational evaluation of the Monty Hall problem and its generalisations: Link
  • Flu Case time-series prediction with ARIMA and Neural Networks: Link.