Schedule Optimization is an advanced scheduling engine that uses Operational Research and Machine Learning models to determine the best assignment for every job — balancing technician availability, travel time, skills, priority, and SLA requirements across your entire workforce simultaneously.
Most scheduling tools optimize locally. Schedule Optimization works across the full picture — fitting more jobs into working hours, routing technicians efficiently, reducing overtime, and giving priority work the placement it needs to meet commit times. When conditions change mid-day, it adapts rather than leaving dispatchers to manually rebuild the schedule.
The result is a schedule that does more with the same workforce: fewer miles driven, higher first-time fix rates, less overtime, and less time dispatchers spend solving problems the system should handle automatically.
Schedule optimization capability offers two main modes:
• Batch Optimization and Intraday Optimization (scheduled, prioritized, on-demand)
• Integration with FSM features: Territories, Capacity & Reservations, Planned Crews, Workforce Optimization, Advanced Task Dependencies, Agent Efficiency
• 40+ configurable constraints & objectives (travel, work, SLA, skill, overtime penalties)
Fixed
• Conflict task status overwrite: Unchanged tasks display correct status in Run Detail.
• Intraday task optimization: Corrected horizon date consideration for next-day task assignment.
• Dynamic Qualifier matching: Resolved cache population, deduplication, and fallback logic.
- Required plugins and products
- Require Work Management and Geo Location from Field Service Management Core, ML Predictive Intelligence
- Dependencies
- Family Apps - com.snc.work_management, com.snc.geolocation and com.glide.platform_ml
- Store apps - FSM Map Provider Integration
- Properties that need to be created or set to activate the content pack - NA
- Affected business rules - NA
- Affected script includes - NA
- .jar files that need to get uploaded, if applicable - NA
Schedule optimization feature works based on Platform ML Capability [fsm-optimizer]. The customer instance should be registered with the Platform ML Scheduler to establish connectivity with the respective capabilities.
Beans.ai, a third-party map provider, can be configured to determine travel estimation while scheduling. Necessary connection and credentials should be set up to avail this feature.