- Reading time: 3 minutes
- Price: Free download
- Published: 17th September 2026
- Word count: 618 words
- File format: Text
Personal statement example
For the past four years I have built delivery schedules for a linen supply company serving hotels, care homes and two small hospitals. Every week I decide which of nine vans covers which sites, in what order, and how much clean stock each customer needs before the next visit. I do this with a spreadsheet, a wall map and a good memory for which roads are impassable at seven in the morning. It works, but I know it is not optimal, and I know that the problem I am solving badly by hand is one that operations research solves properly. That gap is why I want to return to formal study.
I graduated in Mathematics with a 2:1 and enjoyed the applied end of the degree most. My final-year project modelled punctuality on a city bus route as a queueing system, fitting arrival distributions to timetable data published by the operator and comparing predicted waiting times with the observed spread. The model was crude — I assumed far more independence between stops than really exists — but writing it taught me how much judgement sits inside a supposedly mechanical piece of modelling. Choosing what to leave out was harder than the algebra.
Work has sharpened that instinct. When we lost a major customer and gained three smaller ones, my old round structure stopped making sense, and I spent a fortnight rebuilding it. What I learned was that constraints are rarely as fixed as people say. Drivers insisted certain sites had to be first; on investigation, two of them simply preferred it. A genuinely infeasible requirement was the care home whose loading bay is shared with a bin collection. Separating real constraints from habit is, I think, half the work in any applied optimisation project, and I would rather learn to do it with proper method than by argument.
To prepare, I have spent the last year studying in the evenings. I have worked through a linear programming text, rederiving the simplex method by hand before trusting a solver, and I now use Python with PuLP for small problems. My own project has been a simplified version of my working week: eleven customers, three vans, time windows, and a capacity limit per vehicle. Even at that size the model gave me routes I would not have chosen, mainly because it was willing to send a van back to the depot mid-shift, which my spreadsheet logic never considered. I have also been reading about stochastic and robust formulations, since our real difficulty is not average demand but the weeks when a hotel suddenly hosts a conference. I am aware I have only scratched the surface of how uncertainty is handled, and that is one of the areas I most want to study formally.
Alongside work I organise fixtures for a local netball league of fourteen teams, which is a small scheduling problem with awkward humans attached: shared courts, two teams who cannot play on Thursdays, and a preference that nobody plays the same opponent twice in a month. Doing it by hand each season is a useful reminder of how quickly combinations grow.
I am returning to study with clear eyes about the adjustment. I have refreshed my calculus and matrix work deliberately, and I am used to long evenings of concentration after a full day. What I want from a master's is the depth I cannot reach alone: integer programming, simulation, and enough statistics to justify my assumptions rather than defend them. Afterwards I would like to work as an analyst in logistics or healthcare planning, where the questions are messy and the data is imperfect, which is the kind of problem I have spent six years living inside.
Why this example works — strengths and ways to improve
This is a strong, coherent statement for a taught master's in operations research. Your motivation comes from a real scheduling problem you handle every week, and your evidence is concrete and well reflected on. The main gaps are small: you could say more about your self-study model's results and limits, check the six-year timeline, and give the netball paragraph a reflective point.
Subject motivation
the problem I am solving badly by hand is one that operations research solves properly
Your motivation is persuasive because it grows out of a problem you know in detail. Nine vans, mixed customers and impassable roads make the case for formal study concrete. You also admit your current method is not optimal, which shows the subject would answer a need you already have, not just a general interest.
Academic preparation
rederiving the simplex method by hand before trusting a solver
Your Mathematics degree, queueing project, linear programming study and work with PuLP together give credible preparation. Rederiving simplex shows you want to understand the method, not just run the software. Naming the text you used, or the scale of the problems you have tackled, would help a reader judge your level more precisely.
Evidence and reflection
Choosing what to leave out was harder than the algebra.
This is real reflection. You name the specific flaw in your bus model, the independence assumption, and draw a lesson about judgement in modelling. The constraint example works the same way: you found that two drivers simply preferred going first, which turns an everyday observation into an insight about method. You avoid generic skill claims throughout.
Relevant experience
it was willing to send a van back to the depot mid-shift
Your self-built routing model is strong evidence because it produced a counterintuitive result you can explain. You could strengthen it by saying whether the routes held up against your real constraints and what the model got wrong. The netball fixtures are relevant but currently only describe the problem, without saying what you learned from it.
Credibility and voice
I am aware I have only scratched the surface of how uncertainty is handled
Your voice is modest, specific and believable. You do not overclaim expertise in stochastic or robust methods, and you name them as gaps. One detail weakens precision: "six years living inside" messy problems does not obviously match four years of scheduling plus your degree. Make the timeline explicit so a reader does not stumble.
Structure and format
What I want from a master's is the depth I cannot reach alone
The structure moves logically from the workplace problem to your degree, your work insight, self-study, an extra activity and your aims. Paragraphs are economical, and the ending names realistic goals. The netball paragraph sits slightly apart from the rest. Either link it briefly to combinatorial growth in your models or trim it.
What you’ve done well
- You anchor your motivation in a specific operational problem: nine vans, time windows and a shared loading bay. This makes your case for operations research credible rather than generic.
- Your reflection on modelling judgement, from the bus queueing assumptions to separating real constraints from drivers' habits, shows the critical thinking expected at postgraduate level.
- Your independent preparation is concrete and well sequenced: simplex by hand, then PuLP, then a small routing model with an honest view of its limits.
How this draft could improve
- Add one or two sentences on how your eleven-customer model performed against reality, and what you would change. This would turn it into a full project with evaluation.
- Add a reflective point to the netball paragraph, or cut it, so it supports your argument rather than standing as a separate example.
- Check the "six years" claim against your stated four years of work, and tailor your study aims to the actual programme's content when you apply.