r/OperationsResearch

Hey everyone,

I’m planning to apply for a Master’s in Operations Research, but my background is a bit non-traditional. I have a business degree in MIS which unfortunately didn't give me a rigorous academic math foundation. I am essentially relearning the formal math prerequisites from scratch.

I have exactly 5 months to prep before applying, and I can realistically dedicate about 20-25 hours a week to studying. I spent my first three weeks deep in Stewart’s Early Transcendentals doing single-variable calc and even some real analysis axioms, but I feel like I’m getting way too bogged down in pure theory instead of computational application.

I really need advice on how to efficiently pace myself through Multivariable Calculus, Linear Algebra, and Probability/Statistics given my limit. What theoretical weeds can I safely skip so I can focus strictly on what’s needed for linear programming and stochastic modeling?

Also, since these math classes won't be on my undergraduate transcript, how do I actually prove my competency to an admissions committee? Are online certificates respected, should I take the GRE Math Subject Test, or do I need to enroll in accredited extension courses for a letter grade?

Would love to have a chat with someone who can guide me. Really appreciate any and all advice!

TL;DR: Non-math business grad needs to learn Calc, LinAlg, and Stats in 5 months (25 hrs/week) for an MSOR application. Need advice on what specific topics to prioritize/skip and how to formally prove to admissions that my self-study is legitimate.

I've been working on a conceptual framework for an autonomous on-demand public transit system. The core dispatch problem is formulated as a variant of the PDPTW with the following objective:

min F(π) = α·W(π) + β·D(π) + γ·(1−OCC(π))

where W is average passenger waiting time, D is deadhead km ratio, and OCC is average fleet occupancy. The weights α, β, γ sum to 1 and are configurable by the operator.

For the solver I've proposed an LNS approach (Ropke & Pisinger 2006) with worst removal + regret-based insertion, running in 30-second dispatch cycles.

A few questions for people with more OR experience:

1. Is LNS the right choice here, or would a rolling horizon approach with column generation be worth the added complexity for a real-time system?

2. For the demand prediction module, I've proposed LSTM-based spatiotemporal forecasting. Are there better architectures for this specific problem (short-horizon, high spatial granularity)?

3. The conceptual simulation suggests ~20-24% deadhead ratio. Does this seem reasonable for a system operating in low-density suburban areas?

Full write-up (preprint link)

https://papers.ssrn.com/sol3/papers.cfm?abstract\_id=6513843