Engineering & CS
10 topics
Engineering & CS
Operations research
Operations research optimises complex systems and decision processes using mathematical programming. Linear programming, combinatorial optimisation, and scheduling connect it to economics, ML, and robotics.
Topics in this field
Discrete-Event Simulation
Modeling stochastic systems by advancing a simulation clock through events and analyzing steady-state and transient behavior.
Dynamic Programming
Solving complex optimisation problems by breaking them into overlapping subproblems. Bellman's principle of optimality is the key insight.
Integer Programming
Optimization over integer or binary decision variables, essential for combinatorial problems in scheduling, routing, and resource allocation.
Job Scheduling
Minimizing makespan and completion times in single-machine, multi-machine, and project scheduling settings.
Linear Programming
Optimising a linear objective over a polytope. The simplex algorithm and interior-point methods are the two main solution approaches.
Markov Decision Processes
The mathematical framework for sequential decision-making under uncertainty. Foundation of reinforcement learning.
Network Flow
Optimising flows through networks — transportation, logistics, matching, and scheduling all reduce to network flow problems.
Queuing Theory
Mathematical models of waiting lines and service systems. The M/M/1 queue and Little's law are the foundations.
Stochastic Optimization
Optimization under uncertainty using probability distributions over parameters, covering two-stage programs and stochastic gradient methods.
Traveling Salesman Problem
Find the shortest tour visiting all cities exactly once — a canonical NP-hard problem driving advances in combinatorial optimization and approximation algorithms.