4colors Optimizer
Better algorithms. Better optimization decisions
The 4colors Optimizer finds better plans for hard decisions within the time you have. Complementary algorithms run in parallel and adapt to each problem.
One engine for scheduling, routing, rostering, assignment, loading, packing, resource allocation, network design, QUBO and beyond. Scheduling records broken after almost 20 years.
Explore Optimizer for SchedulingDiscuss an optimization challenge
One engine, many planning problems
It brings together mathematical optimization, constraint-based reasoning and specialized search methods, for scheduling, routing, packing, assignment, loading and resource allocation problems.
In software:
4colors Optimizer for Scheduling
High-performance scheduling inside your applications and workflows.
In planning:
Takt, Roster and Stride
Optimization for production, workforce and project planning, ready to use.
In custom projects:
Custom Optimization
Integrated optimization across problem types, and models built for problems unique to your organization.
One problem. Different search strategies
Different problems reward different search strategies. A production schedule dominated by precedence constraints is not the same search problem as geometric loading or large-scale assignment.
4colors Optimizer runs a portfolio of complementary algorithms in parallel. It dynamically directs computation toward promising approaches based on the problem instance and progress of the search.
Share of compute · Illustration
Constraint reasoning leads while precedence rules dominate; heuristics refine sequences once a feasible schedule exists.
Problem-specific heuristics take most of the budget; mathematical programming checks bounds on promising arrangements.
Mathematical programming carries the search; learned guidance helps decide which neighborhoods to explore next.
A search architecture that adapts
Four parts work together, so the search fits the problem rather than the other way round.
Parallel portfolio
Multiple methods explore different approaches to a problem, rather than relying on one fixed strategy.
Instance-aware decisions
Problem structure and search progress inform which methods run and how much effort they receive.
Mathematical methods
Mathematical programming, constraint reasoning and problem-specific heuristics work together where each is most useful.
Learning and discovery
Models trained on earlier runs suggest promising methods for each new instance, and AI-assisted methods help design and test new search strategies.
Your time budget. Your best available solution
Allocate a time budget to the decision. Follow improving solutions as the search runs, stop when the current answer is useful, or continue to the chosen limit.
For models with warm-start support, a previous solution can provide a starting point when conditions change.
Best objective found (lower is better)
Optimization across problem families
Explore problem familiesScheduling and sequencing:
Production, workforce, operations and time-constrained resources.
Routing and transportation:
Vehicles, deliveries, routes, time windows and capacity.
Packing and loading:
Geometric arrangements, compatibility, capacity and handling requirements.
Assignment and allocation:
Match people, machines, tasks or computing resources under competing constraints.
Integrated optimization:
Coordinate decisions across production and workforce, packing and routing, or manufacturing and logistics using one model for the whole operation.
Where to use the technology
In software:
4colors Optimizer for Scheduling
Bring high-performance scheduling into your applications and workflows.
In planning:
Takt, Roster and Stride
Apply optimization in production, workforce and project planning.
In custom projects:
Custom Optimization
Combine modeling, algorithms and integration for integrated optimization and problems unique to your organization.
Tested on real problems
We measure solution quality and speed against established methods on demanding test problems. The results guide every improvement to the optimizer.
Inside the algorithms
Explore all articles and guides- Insight
The future of optimization is a team of algorithms
Different problems reward different kinds of intelligence. Modern solvers increasingly put those differences to work.
Read the insight - Insight
How an Adaptive Optimizer Decides Where to Search
Better optimization is sometimes less about one brilliant method than about deciding which method deserves the next second of computation.
Read the insight - Insight
The optimizer that chooses how to optimize
Modern solvers make decisions about their own search. Increasingly, those decisions can be learned.
Read the insight - Insight
Can AI Help Design Better Optimization Algorithms?
One optimization problem is finding a good solution. Another is finding a better way to search.
Read the insight - Insight
The measure of a better algorithm
Benchmarks reveal progress in optimization. Real-world problems reveal what that progress is worth.
Read the insight - Insight
What is another second of search worth?
An optimization algorithm can keep finding better answers. The interesting question is when another second changes the decision.
Read the insight - Insight
Exact Methods and Heuristics: Two Ways to Search for Better Answers
An optimization solver may find a useful answer quickly and then spend much longer explaining how close it is to the best possible answer.
Read the insight
Bring a decision that matters
Send a representative problem, and see what the Optimizer finds within the time you can give it.
Discuss your optimization challengeExplore Optimizer for Scheduling