Scheduling & sequencing
A better schedule changes more than the calendar
4colors puts the right work on the right resources at the right time. Its algorithms handle deadlines, dependencies and tight capacity at scale.
Good schedules in seconds where CP-SAT needs minutes or runs out of time. New benchmark records, beating results that stood almost 20 years.
Explore Optimizer for SchedulingDiscuss a scheduling problem
The hard part is what the calendar doesn't show
A job looks ready to run, but its material hasn't arrived. A qualified operator is available, but the machine requires a long changeover.
Moving one task to meet a deadline may delay three others. A feasible schedule must respect all those relationships, and a good schedule must also make sensible tradeoffs among them.
Ten jobs, three machines, three product families. Changeovers (red) cost time.
All jobs done after13 h
Changeovers3
Illustrative example, not customer data.
Sequencing, scarce resources and competing deadlines
Sequence work
Choose task order while considering precedence, setup times, due dates and operational priorities. A shorter queue on one machine isn't necessarily a better plan for the operation.
Allocate constrained resources
Match jobs with eligible machines, qualified people and limited equipment. Account for capacity and availability at the moments each activity needs them.
Compare alternatives
Explore how different priorities, deadlines or resource levels change what is achievable. Faster solving makes it more practical to investigate several scenarios, not only the first workable schedule.
Relevant software
From the solver engine to ready-made planning apps, pick the level that suits your team.
4colors Optimizer
The solver engine behind every 4colors product. A portfolio of search methods for hard planning decisions in routing, loading, scheduling and allocation, from Python or the API.
Optimizer for Scheduling
Advanced scheduling algorithms for the software you build to manage jobs, machines, people and resources. Due dates, setups and shared resources sit in one model.
Planning Applications
Takt, Roster and Stride bring optimization to production, shift and project planning. When a rush order lands or someone calls in sick, each re-plans within your rules.
Custom Optimization
Original models and algorithms built around your own resources, rules and objectives. We work with your team from the first prototype to a planner that runs in your systems.
Scheduling in different settings
Job-shop scheduling exposes machine sequences and changeovers. Employee rostering introduces skills, fairness and coverage. Satellite observation scheduling adds changing windows and instrument constraints. Their models differ; the shared challenge is finding strong plans among many interconnected choices.
Relevant work
Best known
RG300 project scheduling benchmark
4colors Optimizer established best-known schedules on 113 of 480 projects in a public project-scheduling benchmark.
The constraints inside a schedule
Explore more use cases- Insight
A Good Production Schedule Is More Than a Gantt Chart
The bars show when work happens. The difficult questions are why it happens in that order, and what changing the order would cost.
Read the insight - Guide
Hard and Soft Constraints: Where a Schedule Can Bend
The distinction between a rule and a preference tells an optimizer what kind of compromise is possible.
Read the guide - Insight
The Schedule Broke. Now What?
The fastest way to recover may begin with the schedule you already have.
Read the insight - Guide
Modeling Changeovers in Production Scheduling
The order of jobs can determine how long a factory spends changing products rather than making them.
Read the guide - Insight
Why the Best Schedule May Not Be the Most Useful
A schedule can improve on paper and still disrupt the operation it was meant to help.
Read the insight