Route Optimization Algorithms Explained: From VRP to Solver Choice

Key Takeaways

• A route optimization algorithm picks which vehicle takes which stops and in what order. It does not find the perfect answer. It finds a good one within the time you give it.
• Route optimization runs in five steps: collect the inputs, apply the rules, pick the objective, run the solver, then publish and re-plan as the day changes. 
• The problem underneath is the vehicle routing problem. Add one more stop, and the number of possible routes grows quickly. 
• There are five types of route optimization algorithms: exact, heuristic, metaheuristic, dynamic, and hybrid. Almost every commercial engine runs heuristics and metaheuristics. 
• Google OR-Tools, the open-source solver most builds start with, handles fleets under 200 stops with little tuning. Above 900 stops with live changes, you need a custom solver.
• Machine learning does not solve routing. It predicts how long a stop really takes and how long a lane really takes to drive.
• A good heuristic solver puts 1,000 stops in order in around 100 milliseconds. An exact solver on the same problem can run for hours.

Ask three routing vendors how their software works and you get three answers. One says AI, One says machine learning, and One says a proprietary engine.

Underneath, they are all solving the same maths problem, and most solve it the same way.

You do not need to write a solver to buy one or scope one. But you do need to know what the engine is doing. That is what tells you whether a $200-a-month product will handle your fleet or whether you need something built.

This guide walks through the five steps a routing engine runs, what happens inside the solver, and how to match an approach to your stop count and your rules. No maths notation, no code. 

Who this guide is for: CTOs and engineering leads scoping a routing build, plus operations directors who need to brief one or judge a vendor’s answer.

What Is a Route Optimization Algorithm?Copy link to "What Is a Route Optimization Algorithm?"

A route optimization algorithm decides which vehicle takes which stops, and in what order, while following your rules on capacity, driver hours, and delivery windows. It does not check every possible route. There are too many. Instead, it builds one that works, then keeps improving it until your time is up.

That surprises people. Routing software does not return the perfect answer. It returns the best answer it found in the seconds you gave it.

Take a courier with 150 stops and eight drivers. The engine has to pick which driver gets which stops, put each driver’s stops in order, keep every customer inside their delivery window, and spread the work so nobody finishes at 3 pm while someone else runs until 8. Turn-by-turn directions do none of that.

Knowing how the engine reaches that answer is the difference between briefing a routing build well and briefing it badly. Teams that skip this scope custom route optimization software development around features instead of around the maths that drives the cost.

Routing One Van vs Routing a Fleet: TSP and VRP ExplainedCopy link to "Routing One Van vs Routing a Fleet: TSP and VRP Explained"

TSP and VRP are two core routing problems, but they solve different optimization needs. TSP focuses on finding the best sequence of stops for one vehicle, while VRP determines how a fleet should serve multiple stops while respecting real-world constraints. 

The Traveling Salesman Problem (TSP)

Here is the simplest routing model: one vehicle, one set of stops, and one route to optimize. 

• What it is. One vehicle leaves a depot, visits every stop once, and returns to the start.
• What it solves for. The shortest possible loop. One number to minimize, usually distance or time.
• What it ignores. Everything else. No weight limits, no delivery windows, no driver hours. Every stop is treated the same.
• Where you see it. A single technician doing local calls, or a courier with one van and no capacity worries. Consumer route planner apps are usually solving this.

In short: TSP works when you have one vehicle and a simple goal: find the shortest loop through all stops. 

The Vehicle Routing Problem (VRP)

Here is the real-world version of routing: multiple vehicles, multiple routes, and constraints that each route must satisfy. 

• What it is. A fleet of vehicles serves a set of customers, starting and ending at one or more depots.
• What it solves for. The cheapest set of routes across the whole fleet, not the shortest single route. A plan can have one long route and still be the best answer overall.
• What it has to respect. Vehicle capacity, delivery windows, driver shift limits, depot rules. These are not optional extras. They decide whether a route is even legal.
• Where you see it. Parcel networks, grocery distribution, waste collection, field service fleets. This is what vehicle route optimization means in a delivery business.

In short: VRP fits real delivery operations because it plans multiple routes while respecting the constraints that make those routes workable.

The difference in one table

	One van (TSP)	A fleet (VRP)
Vehicles	One	Many
Weight and volume limits	Ignored	Enforced
Depots	One	One or many
Delivery windows	Ignored	Usually enforced
Who gets which stops	Not a question	The hardest question
Goal	Shortest single loop	Lowest total cost across the fleet

The row that matters most is the fifth one. TSP never has to decide which vehicle takes a stop, because there is only one. VRP does, and that single extra decision is what makes it a different class of problem.

Both get slow as they grow. Mathematicians call this NP-hard. In plain terms, nobody has found a way to get the perfect answer quickly once the numbers get big, and the wait grows faster than the fleet does.

Note: Why this matters when you buy. A tool built for one van orders a single driver’s day well and falls apart when you ask it to split work across eight. If a demo only ever shows one route on a map, that is what you are looking at.

What Real-World Rules Does a Routing Algorithm Handle?Copy link to "What Real-World Rules Does a Routing Algorithm Handle?"

A routing engine is not just doing maths on a map. It checks every route against the rules your operation actually runs on.

Rule type	What the engine checks
Delivery time windows	The driver arrives inside the slot the customer was promised
Vehicle capacity	The van is not loaded past its weight or volume limit
Driver rules	Shift hours, rest breaks, license class, and site certifications
Road conditions	Traffic patterns, toll roads, one-way streets, low bridges, weather
Cargo rules	Temperature limits and dangerous goods that cannot travel together
Depot rules	Loading bay slots, opening hours, and which site serves which customer

Miss one of these and the plan looks Research is moving. Reinforcement learning on screen and fails on the road. Most routing projects that go wrong go wrong here, not in the algorithm.

You might also like: Routing is one layer inside a wider stack. For how the layers connect and which one to build first, read our guide on how to build a logistics management system.

Vehicle Routing Problem Variants Your Operation Actually NeedsCopy link to "Vehicle Routing Problem Variants Your Operation Actually Needs"

Nobody solves the plain vehicle routing problem in real life. Every operation adds rules, and each set of rules has a name.

1. Capacitated Vehicle Routing Problem (CVRP)

The most common one. Every vehicle has a weight or volume limit that it cannot exceed. You have a capacitated vehicle routing problem the moment your vans differ in size.

2. Vehicle Routing Problem with Time Windows (VRPTW)

Each customer takes deliveries only between set hours. A vehicle routing problem with time windows is harder than CVRP because arriving early wastes time and arriving late fails the drop.

3. Multi-Depot Vehicle Routing Problem (MDVRP)

Vehicles start and end at different sites. A multi-depot vehicle routing problem also has to pick which depot serves each customer before it picks which vehicle goes where.

4. Pickup and Delivery VRP (PDPTW)

Some stops collect, some deliver, and a collection has to happen before its matching drop. Common in courier, waste, and returns work.

5. Mixed Fleet VRP (HFVRP)

Your fleet is not all the same. A small van, a rigid truck, and a refrigerated unit each cost different amounts per mile and reach different places.

Most real builds combine three or four of these at once. A grocery operation is capacitated, time-windowed, multi-depot, and refrigerated. That mix is why a tool built for one variant rarely fits.

You might also like: For how these rules get captured before design starts, read our guide on how to build route optimization software.

Which Vehicle Routing Problem Variant Fits Your Operation?Copy link to "Which Vehicle Routing Problem Variant Fits Your Operation?"

Routing gets hard fast, and the reason is worth knowing before you set expectations with a board. Adding a stop does not add work for the engine. It multiplies it.

Stops on one route	Possible orderings
5	120
8	40,320
10	3.6 million
12	479 million
15	1.3 trillion

With one vehicle, the software must find the best order for all these stops. With multiple vehicles, it must also decide which vehicle should serve each stop.

For example, 20 vans serving 600 stops creates an enormous number of possible combinations. Checking every option would take too long. So routing software starts with a good route and keeps improving it until it reaches its time limit.

What this means for you: Ask a vendor how long their routing engine spends solving a route. That setting can affect how good the final routes are.

Types of Route Optimization Algorithms and When Each One FitsCopy link to "Types of Route Optimization Algorithms and When Each One Fits"

Every vehicle routing problem algorithm falls into one of five families. The first three decide how the route gets built. The last two decide how it adapts.

Family	What it does	Best for	Examples
Exact	Tests every path combination to find the true best route	Small, stable route plans and high-value deliveries	Branch and bound, integer programming
Heuristic	Uses simple rules to build a good-enough route fast	Quick daily dispatch and small teams	Nearest neighbor, Clarke-Wright savings, sweep
Metaheuristic	Searches wider to escape routes that only look good	Large networks with many real-world rules	Simulated annealing, tabu search, ant colony optimization, genetic algorithms
Dynamic and real-time	Changes the plan mid-shift using live traffic and new orders	Unpredictable traffic, cancellations and urgent jobs	Rolling horizon, event-driven replanning
Hybrid and learning-based	Combines the above with machine learning that adapts over time	Large fleets where conditions change daily	Reinforcement learning-assisted routing, ML-guided heuristics

Choose the family based on how much route quality, speed, and certainty your operation needs.

How exact algorithms work

Exact solvers work three ways.

• Branch and bound. Builds a tree of possible routes and cuts off any branch that cannot beat the best answer found so far.
• Dynamic programming. Breaks the problem into smaller pieces and saves each answer, so it never solves the same piece twice.
• Integer programming. Writes the whole problem as equations and hands it to a solver such as Gurobi or CPLEX.

All three work well up to roughly 100 stops. Past that, they get too slow to use, which is why almost no delivery fleet runs one.

Where Dijkstra’s algorithm actually fits

Dijkstra’s algorithm is often called a route optimization algorithm, but that is not quite accurate. It solves a smaller problem: finding the shortest path between two points on a road network.

• What it does: Finds the shortest or fastest path between two locations.
• What it does not do: Decide which vehicle serves which stops or determine the best order for an entire day’s deliveries.
• Where it fits: A routing engine uses Dijkstra’s algorithm, or a similar shortest-path method, to calculate the distance and travel time between stops.
• How optimization works: The solver uses those travel times and distances to build and compare complete routes while respecting fleet constraints.

Why this matters when you buy: A vendor calling its product “Dijkstra-based” is mainly describing the road-network or mapping layer, not the optimization solver. Ask what algorithm or solver handles the fleet-level routing above it.

How Route Optimization Works: Step-by-Step ProcessCopy link to "How Route Optimization Works: Step-by-Step Process"

Two things happen when you press optimize. The software runs a workflow, and inside that workflow, a route optimization algorithm does the maths. Most vendors show you the first part and hide the second.

1. Collect the inputs

The engine pulls in every stop, vehicle, depot, and driver you plan to use tomorrow. Addresses get turned into map coordinates, and service times get attached to each customer. Miss a depot or a vehicle here, and the plan is wrong before any maths happens.

2. Apply the rules

Next, it loads the rules that decide whether a route is even allowed. Capacity, driver hours, delivery windows, and access restrictions all narrow the options before any sequencing starts. This is the step that turns a map exercise into a real vehicle routing problem.

3. Pick the objective

Now you tell it what a good route means. Fewest miles, most drops, lowest cost per drop, and best window compliance are four different goals, and they pull against each other. Rank them, because an engine optimizing for everything optimizes for nothing.

4. Run the solver

This is where the route optimization algorithm actually works. It builds a first route fast, improves it with small changes, then searches wider to escape answers that only look good. It stops when your time limit runs out.

5. Publish and re-plan

The finished routes go out to drivers on their app, and dispatch gets a view of the day. When something changes- a breakdown, a rush order, a failed drop- the engine adjusts the rest of the run rather than starting the whole day again.

 Step 4 is the part vendors call their engine. Here is what actually happens inside it.

What Happens Inside the Solver?Copy link to "What Happens Inside the Solver?"

Three steps, in order. Every commercial engine does some version of this.

Step-1. Build a first route fast

The engine needs something to improve, so it makes a rough plan quickly.

• Nearest neighbor. Start at the depot, go to the closest unvisited stop, repeat. Simple and fast, and usually 15% to 25% worse than the best possible route.
• Clarke-Wright savings. Start with every stop as its own trip, then merge the pairs that save the most distance. Better than nearest neighbor, still fast.
• Sweep. Draw a line out from the depot and rotate it like a clock hand, grouping stops into vehicle loads as it turns. Useful when your territory is roughly circular.

This step takes milliseconds. The answer works but is rarely good.

Step-2. Improve it with local search

Now the engine makes small changes and keeps the ones that help.

• 2-opt. Take two stops, flip the section between them, and check if the route got shorter. Repeat thousands of times.
• Or-opt. Move a run of one to three stops somewhere else in the route.
• Relocate and swap. Move one stop to a different vehicle, or trade two stops between vehicles.

A solver running 2-opt puts 1,000 stops in order in around 100 milliseconds on ordinary hardware. That is why these methods dominate.

Step-3. Escape a route that only looks good

Local search has one weakness. It stops as soon as no small change makes things better, even when a much better route exists if you moved several stops at once.

Think of walking downhill in fog and stopping at the bottom of a small dip, when the real valley is over the next rise. That dip is called a local optimum, and metaheuristics exist to get you out of it.

• Simulated annealing: Sometimes accept a worse route on purpose, more often early and less often later, so the search can climb out of a dead end.
• Tabu search: Keep a short list of recent moves and refuse to undo them, so the search stops going in circles.
• Ant colony optimization: Virtual ants explore routes and leave a digital trail on the ones that work. Over many passes, the trail builds up on good paths and fades on bad ones.
• Guided local search: Mark the parts of the route that keep appearing as expensive, so the engine is forced to try something different.
• Large neighborhood search: Rip out a chunk of the plan, build that part again from scratch, and keep the new version if it is better. This is what most modern engines use at scale.
• Genetic algorithms: Keep a group of routes, combine the good ones, and change them slightly. Slower, but strong on messy rule sets.

Simple version: Step one gets you an answer. Step two polishes it. Step three stops it from settling for the first decent answer it finds.

What Makes a Delivery Route Optimization Algorithm DifferentCopy link to "What Makes a Delivery Route Optimization Algorithm Different"

Textbook vehicle routing problems usually work with clean, predictable data. Real delivery operations are rarely that simple. A delivery route optimization algorithm has to account for real-world conditions such as:

1. Service time varies by stop

A delivery at a loading dock may take four minutes, while a shop with limited parking could take 20 minutes. Using one average service time makes the whole schedule inaccurate. 

2. Travel time changes throughout the day

Five miles might take 12 minutes early in the morning but 40 minutes during rush hour. The algorithm needs real travel-time data, not distance divided by average speed. 

3. Deliveries fail

A customer may not be home, an address may be wrong, or a delivery may be refused. The system needs to record these outcomes and adjust the remaining route.

4. Drivers need to make changes

Drivers often know practical details that are missing from the data, such as a locked gate after 3 pm or a road that regularly floods. The system should let them override or adjust the planned route.

Important: The core methods are the same as those used in textbook VRP. What separates a delivery route optimization algorithm that runs a real fleet from one that runs a demo is whether it can handle these conditions. 

What Data Does a Route Optimization Algorithm Need?Copy link to "What Data Does a Route Optimization Algorithm Need?"

Heuristics, metaheuristics, and exact solvers all assume one thing: that the engine already knows how your operation works. Most of that information is not in your systems yet, and the gaps are rarely where teams expect. 

1. Location data

Before the engine can optimize a route, it needs accurate location data for every stop and facility. These inputs determine where vehicles can go and whether the planned route works in practice. 

Input	Where it comes from	What breaks without it
Stop addresses turned into map coordinates	Your order system, cleaned	The engine sends a driver to the wrong side of a road, or the wrong town
Depot and branch locations	Fleet or facilities records	Multi-depot plans start and end in the wrong place
Site access notes	CRM, order system, and a planner’s head	Vans arrive at a gate they cannot use

2. Order and task data

Before building the routes, the engine needs to know what each order requires and how long each stop actually takes. This data helps it create schedules that match real delivery conditions. 

Input	Where it comes from	What breaks without it
Delivery windows per customer	Order system or contract records	Drivers arrive outside the slot and drops roll to tomorrow
Real service time per customer	90 days of completed routes with arrival and departure times	Every plan runs late by mid-afternoon
Order priority and handling needs	Order system, often a free-text field	Urgent, fragile or chilled loads get sequenced like everything else

3. Vehicle data

The engine also needs accurate vehicle data to assign the right loads to the right vehicles. Capacity, equipment, and loading rules can all affect whether a planned route is actually workable. 

Input	Where it comes from	What breaks without it
Capacity by weight and volume	Fleet records, usually a spreadsheet	Plans the vehicle physically cannot complete
Vehicle type and equipment	Fleet records	A van without a tail lift gets a pallet drop
Loading rules	Warehouse team, rarely documented	The last item loaded is the last one needed

4. Driver data

Before assigning routes, the engine also needs to know which drivers are available and what they are allowed to do. Shift limits, driving time, and required certifications help keep route plans safe and compliant. 

Input	Where it comes from	What breaks without it
Shift hours and remaining driving time	Telematics feed	Routes that break driver hours rules
Licences, certifications and site inductions	HR records	An uncertified driver gets sent to a restricted site

5. Live conditions

Routes also depend on what is happening on the road, not just what the map says. Live travel times, traffic, and weather data help the engine estimate arrival times under actual operating conditions. 

Input	Where it comes from	What breaks without it
Real travel time per lane, per hour	GPS traces from your own vehicles	Customer arrival times are wrong from the first stop
Traffic and weather	Map provider feed	The plan holds on a clear Tuesday and fails on a wet Friday

The hardest inputs are the ones nobody owns. Site access notes, loading rules, and real service times usually exist only in the memory of the person who has planned routes for eight years. Getting them out is an interview, not a data export.

That is why a routing build starts with 90 days of route history rather than with code. Our logistics software development work treats this as its own stage, not as a step inside development, and writing a spec before development starts is what stops the rules from arriving in week nine. 

Where Does Machine Learning Fit in Route Optimization?Copy link to "Where Does Machine Learning Fit in Route Optimization?"

This is the most common misunderstanding in routing, and vendors do not correct it.

Machine learning does not solve the vehicle routing problem. The solver does that. Machine learning makes the solver’s inputs accurate.

What ML predicts	What it replaces	What it fixes
Service time per customer	One fixed average for every stop	Plans that run 90 minutes late by mid-afternoon
Travel time per lane, per hour	Map provider estimates	Arrival times that are wrong from stop one
Chance a delivery fails	Nothing. Most engines ignore it	Repeat visits to addresses that are never in
Demand per route next week	A planner’s guess	Vehicles booked that are not needed

Research is moving. Reinforcement learning can teach a system routing decisions by trial and error, and neural networks can hand a solver a better starting route than a simple rule would. Both are promising. Neither is standard in production fleets yet, though several logistics technology trends are pushing in that direction faster than most teams expect. 

An engine that solves perfectly against the wrong inputs gives you a perfect plan for a day that doesn’t exist. That is why our machine learning development services train these models on a client’s own completed runs rather than on industry averages.

The order matters. Get the solver working first, then add prediction. Starting with ML before the routing model is stable means fixing the wrong layer.

Which Vehicle Routing Problem Software Should You Use?Copy link to "Which Vehicle Routing Problem Software Should You Use?"

Most teams do not write a solver. They pick vehicle routing problem software off the shelf and build around it.

Solver	License	Best at	Where it runs out
Google OR-Tools	Open source, Apache 2.0	The default choice. Handles CVRP, VRPTW and MDVRP out of the box	Very large problems need heavy tuning
Google Route Optimization API	Paid, per request	Hosted service with nothing to run yourself. Covers the common rules	You do not own the logic, and cost grows with volume
VROOM	Open source, BSD	Fast, clean API, pairs well with OSRM for map data	Fewer rule types than OR-Tools
jsprit	Open source, Apache 2.0	Java teams, flexible rule modeling	Smaller community, slower development
LKH	Free for academic use	Very strong on single-vehicle sequencing	License terms restrict commercial use
Gurobi	Commercial license	The true best answer on smaller problems	Cost and solve times at fleet scale
Custom solver	Yours	Rules nothing off the shelf can model	Build and maintenance cost

Most builds start with OR-Tools. It is open source, it covers the common variants, and your routing logic stays yours rather than sitting behind a license that reprices as your fleet grows.

Which Route Optimization Algorithm Fits Your Fleet Size?Copy link to "Which Route Optimization Algorithm Fits Your Fleet Size?"

This is the table nobody else publishes. Find your row.

Your operation	Rules that apply	Approach that fits
Under 50 stops, one depot	Capacity only	OR-Tools with default settings. No tuning needed
50 to 200 stops, one depot	Capacity plus time windows	OR-Tools with guided local search and a set time limit
200 to 900 stops, multi-depot	Capacity, windows, driver skills	Tuned metaheuristic, usually large neighborhood search or tabu search
900+ stops with mid-shift changes	Full rule list plus live replanning	Custom solver. Split stops into groups first, then order each group. Start each run from yesterday’s plan rather than from nothing
Any size, best answer must be provable	Regulatory or contractual	Exact solver such as Gurobi, accepting long solve times

The pattern. Stop count decides the family. Rule count decides how much tuning you need on top.

Also read: For what each of these costs to build, and what year one really totals, see our breakdown of logistics software development cost.

Why Solve Time Is a Routing Design DecisionCopy link to "Why Solve Time Is a Routing Design Decision"

Most teams treat solve time as something the software reports. It is something you choose.

More time helps, but only up to a point. A jump from 5 seconds to 60 gives you a much better plan. Extending that to 10 minutes gives you a slightly better one, while going from 10 minutes to an hour barely helps at all.

Your planning model	Realistic time limit
Overnight batch at 2 am	10 to 30 minutes. Take the quality
Morning plan before dispatch	60 to 300 seconds
Mid-shift reassignment	Under 5 seconds, or dispatch works around it
Live customer quote at checkout	Under half a second

Decide this before you pick a solver. An engine built for a 20-minute overnight run cannot be made to answer in half a second by adding hardware. It is a different design.

Two ways engines replan during the shift

• Rolling horizon. The engine re-plans a moving window, usually the next 30 to 60 minutes, rather than the whole day. Routes stay current without constantly changing for drivers.
• Event-driven replanning. Nothing recalculates until something happens: a breakdown, a rush order, a major delay. Routes stay stable between events, which is what dispatchers and drivers prefer.

In practice, choose the solve time based on how often your routes change and how quickly your operation needs a new answer. 

Advantages of Route Optimization AlgorithmsCopy link to "Advantages of Route Optimization Algorithms"

A good route optimization algorithm changes six things about how a fleet runs.

1. Lower running costs

Vehicles drive fewer unnecessary miles, which cuts fuel, tire wear, and maintenance. Fewer miles also means fewer hours, so overtime drops with it.

2. More stops per driver

Less dead time between drops means each driver completes more work in the same shift. You grow capacity without adding vans or headcount.

3. Accurate customer ETAs

An engine using real travel and service times gives arrival windows customers can plan around. That turns an all-day wait into a one-hour slot, and cuts the calls asking where the driver is.

4. Planner hours returned

A planner spending five hours a day building tomorrow’s runs gets most of that back. That time usually moves to customer service and exception handling.

5. Lower emissions

Shorter routes and less idling cut fuel burn, which cuts carbon output. For operations reporting on emissions targets, mileage saved is the easiest number to move.

6. Less pressure on drivers

A clear, workable sequence beats a schedule that was never achievable. Drivers stop guessing, stop rushing, and stop rebuilding the plan in the cab.

How big is the gain?

It depends entirely on what you are replacing.

What improves	Coming from manual planning	Coming from a basic tool
Miles driven	10% to 20% less	3% to 8% less
Stops per driver per day	10% to 15% more	3% to 6% more
Planner hours	Most of the day returned	Little change
Window compliance	Large gain	Moderate gain

The honest version. Most of the gain comes from moving off spreadsheets, not from picking a cleverer metaheuristic. If you already run a decent product, a custom engine earns its place on rules it can represent and yours cannot, not on a few extra percent of mileage.

Limitations of Route Optimization Algorithms: Four Things That Go WrongCopy link to "Limitations of Route Optimization Algorithms: Four Things That Go Wrong"

The algorithm is rarely the problem. These four are.

1. The rules are wrong, not the solver

Teams spend weeks changing settings when the real issue is a rule nobody wrote down. Fix the rule list before touching anything else.

2. Address Accuracy

Incorrect or incomplete addresses can send drivers to the wrong location and disrupt the entire route. Validate and standardize addresses before running the optimization.

3. Too many hard rules

Mark everything as compulsory and the engine returns nothing on a busy day. Split rules into must-follow and nice-to-have, and let the second group bend when the day is tight.

4. No slack in the plan

A schedule with zero spare time collapses on the first delay. Build in 10% to 15%, and the day absorbs small problems without a full replan.

How SolGuruz Chooses the Right Routing ModelCopy link to "How SolGuruz Chooses the Right Routing Model"

We have shipped 102+ products across 14 industries since 2019, including LogiConnect, where the routing engine cut fuel and time costs by 15% to 20% against manual planning.

1. We size the problem before choosing a method

Stop count, depot count, and rule count decide the family. We will not pick a solver from a fleet size alone.

2. We test against your own history

The model runs against at least 20 real operating days from your last quarter, and we compare it with what your planners produced by hand. If it loses there, it loses live.

3. We keep the solver yours

We build vehicle route optimization on open libraries such as OR-Tools and VROOM rather than reselling a licensed engine. 

4. We set the time limit with you

Overnight planning and mid-shift replanning are different designs. Choosing late means rebuilding.

5. We treat driver deviations as missing rules

The audit measures where and why they happen, then we put them in the model rather than arguing with the depot.

Teams that want this capacity inside their own sprints can hire dedicated developers for logistics platforms from the same bench.

Read the full build in our B2B and B2C supply chain management app case study.

The Bottom LineCopy link to "The Bottom Line"

A delivery route optimization algorithm is not magic, and it is not one thing. It is three things. A method that suits your stop count. A rule list built from how your operation really works. And a time limit you set before anyone writes code.

Get those three right and an open-source engine beats an expensive product that cannot handle your rules. Get them wrong, and no amount of computing power helps.

Start by counting. How many stops on your biggest day, how many depots, and how many rules a route has to follow. Those three numbers tell you which row of the fleet size table you are in. If you want a second opinion on the numbers, contact us, and we will look at them with you. 

FAQsCopy link to "FAQs"

1. What is a route optimization algorithm?

It decides which vehicle takes which stops and in what order, while following capacity, driver hours, and delivery window rules. It returns the best answer found within your time limit, not a perfect one.

2. What are the types of route optimization algorithms?

Five families. Exact, heuristic, metaheuristic, dynamic, and hybrid. The first three decide how a route gets built. The last two decide how it adapts during the day.

3. What is the vehicle routing problem?

It is the maths problem underneath all routing software. Given a fleet, a depot, and a set of stops, find the cheapest set of routes that serves every customer without breaking a rule.

4. What is the difference between the traveling salesman problem and VRP?

TSP puts stops in order for one vehicle. VRP adds more vehicles, capacity limits, several depots, and time windows. Both get slow as they grow, but VRP is the one real fleets face.

5. What is a capacitated vehicle routing problem?

A VRP where every vehicle has a weight or volume limit that the engine cannot exceed. It is the most common variant, and you have one the moment your vehicles differ in size.

6. What is the vehicle routing problem with time windows?

A VRP where each customer takes deliveries only between set hours. It is harder than a capacitated VRP because arriving early wastes time and arriving late fails the drop.

7. How does route optimization work step by step?

Five steps. Collect the inputs, apply the rules, pick the objective, run the solver, then publish and re-plan as the day changes. The solver step is where the algorithm does its work.

8. Is Dijkstra's algorithm a route optimization algorithm?

No. Dijkstra finds the shortest path between two points, which is what your map provider does between stops. A routing engine calls it many times, then optimizes the whole day on top.

9. What is a metaheuristic in route optimization?

A method that stops the search, settling for the first decent answer. Simulated annealing, tabu search, ant colony optimization, and large neighborhood search all escape routes that only look good locally.

10. How is a delivery route optimization algorithm different from a general one?

It has to handle real inputs. Service time that changes by customer, travel time that changes by hour, failed drops, and plans that change halfway through the day.

11. Is Google OR-Tools good enough for a commercial routing engine?

For most fleets under 200 stops, yes, with little tuning. Above 900 stops with live replanning, it needs heavy tuning or a custom solver built around your specific rules.

12. Does route optimization need machine learning?

No. A solver handles routing without it. Machine learning predicts real service time per customer and real travel time per lane, which makes the solver's answer match what happens on the road.

13. How many stops can a routing algorithm handle?

A well-built engine handles several thousand across multiple depots. The limit is rarely the stop count. It is the time you allow and how many must-follow rules apply at once.

14. Can a routing algorithm replan mid-shift?

Yes, using rolling horizon or event-driven replanning. Both need answers in under five seconds, live position data, and a way to push changes to drivers. Neither can be added to an overnight engine later.