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One scheduling discipline. Applied to your operation.

Underneath every scheduling problem sits the same discipline: modeling real constraints, forecasting demand, and re-optimizing the moment reality changes. We lead with logistics — and we partner with clients to apply that discipline wherever a single delay cascades. You bring the domain knowledge; we bring the optimization expertise; the first assessment fuses the two into a solution that fits your operation.

Primary focus

Logistics & supply chain

In logistics, dynamic scheduling eliminates delivery windows based on guesswork and replaces them with live optimization — matching real demand to real capacity, minute by minute.

  • Real-time route rerouting — algorithms monitor live traffic, weather and port bottlenecks to instantly change vehicle paths.
  • Dynamic ETA calculations — machine learning updates delivery windows continuously, sending precise customer notifications.
  • Predictive maintenance — schedules trucks for service before breakdown, avoiding peak delivery windows.
  • Load balancing — AI matches unpredicted incoming orders with available capacity and nearby drivers automatically.
Research-backed

Peer-reviewed work from TU Eindhoven (with logistics-automation partner Vanderlande) showed learned scheduling policies cutting late orders from 11.7% to 3.7% versus conventional heuristics on real warehouse data — Cals et al., Computers & Industrial Engineering, 2021.

Primary goal

Minimize fuel & transit time

Key variables: traffic, fuel, driver hours. Disruption scale: minutes to hours.

  • Core AI: reinforcement learning
  • Core AI: graph neural networks
  • Live re-optimization on every event
Primary goal

Maximize patient care & utilization

Key variables: staff credentials, bed capacity. Disruption scale: seconds to minutes.

  • Core AI: queueing theory
  • Core AI: predictive analytics
  • Skill-mix & staffing-law compliance
Same discipline, applied

Healthcare operations

Healthcare scheduling directly impacts patient outcomes, where static shift planning often leads to extreme staff burnout or long emergency wait times.

  • Predictive patient inflow — forecasts Emergency Department surges from historical data, weather and local events.
  • Automated shift swapping — staff swap via app while AI verifies required skill mix and staffing laws are maintained.
  • OR optimization — re-sequences surgeries in real time when an operation runs long or an emergency arrives.
  • Discharge & bed management — predicts discharge times to prepare beds for incoming admissions.
Same discipline, applied

Large capital projects

Large infrastructure, energy and construction projects feature thousands of interconnected dependencies, where a single delay triggers millions in overruns.

  • AI-driven 4D BIM scheduling — connects 3D models with live timelines, simulating how material delays affect the physical build.
  • Weather & risk mitigation — dynamically reschedules high-risk outdoor tasks like crane operations or concrete pouring.
  • Resource & crew leveling — tracks material arrivals and labor availability, moving idle crews to alternative critical tasks.
  • What-if simulation — generates thousands of timelines in seconds to show the exact cost and time impact of a disruption.
Primary goal

Prevent timeline & budget overruns

Key variables: labor, heavy equipment, weather. Disruption scale: days to weeks.

  • Core AI: constraint programming
  • Core AI: Monte Carlo simulation
  • Scenario planning at portfolio scale
Primary goal

Fair, compliant rosters that cut sick leave

Key variables: CAO rules, working-time law, qualifications, staff preferences. Disruption scale: hours — the 06:00 sick call.

  • Core AI: constraint programming
  • Core AI: fairness-aware optimization
  • Explainable — every roster decision traceable
Same discipline, applied

Public sector & workforce rostering

Municipalities, public transport, enforcement teams and semi-public organizations still build rosters by hand — one overloaded planner in a spreadsheet, balancing CAO rules, part-time contracts and fairness. When someone calls in sick at 06:00, the whole plan collapses.

  • Compliance built in — CAO agreements, the Dutch Working Hours Act (Arbeidstijdenwet) and qualification requirements enforced automatically in every roster.
  • Dynamic re-rostering — a sick call or no-show triggers instant, rule-compliant repair proposals instead of a morning of phone calls.
  • Fairness by design — nights, weekends and holidays distributed transparently across the team, with preferences and part-time contracts respected.
  • Explainable for the works council — every automated decision can be traced and justified, keeping the OR and your people on board.
At a glance

Core technology comparison

The same dynamic-scheduling philosophy, tuned to each domain's variables and disruption speed.

Feature Logistics Healthcare Capital projects Public sector
Primary goal Minimize fuel & transit time Maximize patient care & utilization Prevent timeline & budget overruns Fair, compliant rosters that cut sick leave
Key variables Traffic, fuel, driver hours Staff credentials, bed capacity Labor, heavy equipment, weather CAO rules, qualifications, preferences
Disruption scale Minutes to hours Seconds to minutes Days to weeks Hours — the 06:00 sick call
Core AI tech Reinforcement learning, graph neural networks Queueing theory, predictive analytics Constraint programming, Monte Carlo simulation Constraint programming, fairness-aware optimization

Which operation should we optimize first?

Tell us where a single delay hurts most. We'll show you what dynamic, multi-constraint scheduling could recover.

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