Find out how.
The Timefold Solver is a lightweight, embeddable constraint-satisfaction engine, the commercial successor to OptaPlanner with 20+ years of constraint-solving heritage and a proven track record on NP-hard problems. On the Timefold Platform, it's productized into REST APIs you can call without any solver expertise. Here's what that means in practice.
Timefold ships with 50+ pre-configured routing constraints and stays extensible for the rules unique to your operation, without breaking as you scale from dozens to millions of planning decisions. Capacity, time windows, skills, dependencies, SLAs, priorities: combine any of them and the solver optimizes around all of them at once.
Assign jobs first and plan routes second, and you lock in a schedule that ignores travel. Timefold solves both together, matching the right jobs to the right vehicles on the most efficient routes in a single optimization pass.
Plan the week, month, or year ahead, then continuously roll the horizon forward and optimize only what's new. When reality disrupts the plan, whether a cancellation, a delay, or a new urgent order, Timefold replans in real time, resolving disruptions in under a second.
Every plan shows the trade-offs it made and why, with real-world metrics. Planners and dispatchers see the reason behind each routing decision and can adjust constraint weights with confidence, instead of fighting a black box.
JSON in, JSON out. Stateless. Technology agnostic. Timefold slots into your existing systems over a stable REST API, and it never locks you to one map provider. Point it at any source of distance and travel-time data and the Platform handles pre-calculation, updates, throttling, and concurrency.
For every 1.4 field workers nearing retirement, only one enters. In the meantime, grid investment will double by 2030. You can't hire your way out, you can schedule your way out. Timefold's assigns the right jobs to your crews on the best routes, without changing your existing workflows and tech stack.
for Timefold customers
vs. manual planning
around a real-time disruption
in a single Timefold solve
A sample of the routing constraints Timefold optimizes out of the box. All are configurable as hard, medium, or soft, with adjustable weights.
minimize travel time, mileage, and waiting.
respect load, weight, and volume on every route.
serve every stop when it's allowed.
match certifications, equipment, and customer preferences.
same vehicle, right order, every time.
prerequisites, mutual exclusions, same-vehicle rules.
honour contractual response times; make optional stops truly optional.
legal hours, start/end locations, rest and lunch.
several vehicles or people on the same visit.
JSON in, JSON out. Stateless. Technology agnostic.
Timefold consumes the data you already have (stops, vehicles, constraints) as structured JSON over a REST API.
/ Fit Timefold into the way you work
/ Start with sample data or your own
Your routing challenge is sent through the API to the stateless constraint solver, which optimizes every constraint at once.
/ Stateless constraint solver
/ No solver expertise required
A fully optimized set of routes comes back as structured JSON, ready to render as-is or adjust as things change.
/ Replan in real time
/ Explainable, metric-backed results
"We needed a solution that would integrate seamlessly into our technology stack. Timefold’s engineering-first mindset immediately stood out. Their capabilities are advanced and comprehensive, their APIs are exceptionally clean, and their economic model is both uncomplicated and well aligned with organizations that rely on a platform provider."
"Timefold worked in partnership with NEC to provide the optimisation required and extended their platform to accommodate specific requirements for the Social Housing sector. The time to market was shortened through agile partnerships working."
"Tasked with a complex scheduling problem for a prospect, we were operational in real-time, with real data in 4 days. Thanks in large part to the solid foundation of Timefold, the client selected us on the strength of our demonstration."
The vehicle routing problem (VRP) is the challenge of finding the cheapest set of routes for a fleet of vehicles to serve a group of customers, while respecting real-world limits like vehicle capacity, delivery time windows, and driver shifts.
Yes. The VRP is NP-hard, meaning the number of possible solutions grows factorially with the number of stops, so no algorithm can guarantee the optimal answer quickly for realistic problem sizes. This is why production systems use heuristics and metaheuristics rather than exact methods.
The traveling salesman problem finds the shortest route for one vehicle visiting every location once. The VRP generalizes this to a whole fleet and adds constraints like capacity and time windows. A VRP is effectively many interacting TSPs solved together.
The most common variants are the Capacitated VRP (CVRP), VRP with Time Windows (VRPTW), Pickup & Delivery Problem (PDP/VRPPD), Multi-Depot VRP (MDVRP), Heterogeneous Fleet VRP (HVRP), Open VRP (OVRP), Split Delivery VRP (SDVRP), Dynamic VRP (DVRP), and Electric VRP (EVRP).
There are four families of methods: exact algorithms (optimal but don't scale), construction heuristics (fast, approximate), metaheuristics (near-optimal at scale, and the production standard), and emerging AI/learning methods. Most real systems combine construction heuristics with metaheuristics.
Routing tools typically report 5–30% cost savings versus manual planning. Timefold customers see an average 25% reduction in travel time after implementation.
Options range from open-source libraries (Google OR-Tools, VROOM) and commercial solvers (Hexaly, Timefold Solver) to full routing APIs. Timefold offers production-ready routing APIs, including Field Service Routing and Pickup & Delivery Routing (in preview), powered by the Timefold Solver.
Yes, this is the Dynamic VRP. Timefold supports continuous and real-time planning, replanning around disruptions like cancellations, delays, and new orders in under a second.
George Dantzig and John Ramser introduced it in 1959 in their paper "The Truck Dispatching Problem," applied to petrol deliveries.
Timefold solves routing and scheduling together, handles any combination of constraints at any scale, explains every decision, replans in real time, and integrates over a stateless REST API (JSON in, JSON out) without requiring any solver expertise.
Less travel. Lower cost. Routes your team can trust.