Find the Best Decision Within Your Constraints
Model, solve, and explain optimization problems — scheduling, routing, allocation, planning, and decisions with competing goals.
Optimization engineering services solve decisions where resources are limited and the rules can't be broken. Common pain points include schedules and routes built by hand, costs inflated by habit-based allocation, planning tools that produce infeasible plans, and models too slow at real scale. We solve this with mathematical optimization, combinatorial optimization, operations research, metaheuristics, and decision algorithms — formulated around how your operation actually works.
What Is Optimization Engineering?
Optimization engineering turns a business or technical decision into a model that a solver can work on: what you are trying to maximize or minimize, what you are allowed to change, and which rules can never be broken. Done well, it replaces spreadsheets, rules of thumb, and manual planning with decisions that are measurably better and can be explained.
It sits between algorithm engineering and operations. The math is only half the work — the other half is getting the formulation right so the model reflects how your operation actually runs.
Optimization Services We Offer
Service | What It Solves |
Linear, integer, mixed-integer, nonlinear, convex, and constraint optimization models | |
Knapsack, assignment, scheduling, vehicle routing, and resource allocation problems | |
Supply-chain, workforce, inventory, production, and logistics planning | |
Genetic algorithms, particle swarm, simulated annealing, and ant colony optimization for problems too large to solve exactly | |
Multi-objective optimization, decision trees, utility models, and portfolio optimization |
Problems Optimization Engineering Solves
Schedules, rosters, or delivery routes are still built by hand and take hours every week
Costs are higher than they should be because capacity, stock, or budget is allocated by habit
A planning tool exists but produces infeasible plans that people override
A model works on small test data but is far too slow on the real problem size
Several goals conflict — cost, speed, service level — and nobody can see the trade-offs clearly
How We Approach an Optimization Problem
Understand the Decision
We start with the decision you make today, who makes it, and what a better answer would be worth.
Formulate the Model
We define the objective, decision variables, and constraints in plain language first, then mathematically.
Choose the Solving Method
Exact solvers where the problem size allows, decomposition or heuristics where it does not — chosen from the problem's structure, not by default.
Validate Against Reality
We test the solution against historical cases and operational judgment, not just mathematical correctness.
Deliver and Integrate
You get the model, the code, and a clear explanation — as a standalone tool or integrated into your existing system.
Technologies We Use
Python with PuLP, Pyomo, OR-Tools, and SciPy; commercial and open-source solvers such as Gurobi, CPLEX, HiGHS, and CBC; and custom heuristic implementations where off-the-shelf solvers don't scale.
Optimization Engineering With Codersarts
We work on optimization problems for operations teams, founders, and researchers. Every engagement ends with a model your team understands — what it optimizes, what it assumes, and when to re-run it. Related work often connects to data analysis, simulation for uncertain inputs, and performance engineering when solve time matters.
Optimization Engineering FAQs
What kind of problems can be optimized?
Any decision with a clear goal, choices you control, and rules to respect — scheduling, routing, allocation, pricing, inventory, and portfolio decisions are common examples.
Do we need a lot of data?
Not always. Many optimization models need parameters (costs, capacities, times) rather than large datasets. We tell you early what is required.
What if the problem is too large to solve exactly?
We use decomposition, approximation, or metaheuristics, and we are explicit about how close to optimal the result is.
Can you improve an existing model or solver setup?
Yes. We review the formulation and the solving approach, which is often where the biggest gains are.
Is our data kept confidential?
Yes. Your data, code, and results stay confidential, and an NDA is available on request.
Have a Decision That Should Be Optimized?
Tell us what you are deciding today and what limits you work within — we'll show you how it can be modeled and solved.