Enhancement Description
In IFS PSO version 6.16.0.44, when a technician is manually allocated two long-duration activities in parallel at the same location, the visit-splitting behavior is inconsistent. Although both activities exceed the technician’s shift duration, only one activity is automatically divided into multiple visits aligned with the technician’s working shifts. The other activity remains as a single continuous visit and extends through non-working hours, including overnight periods.
This behavior occurs specifically in scenarios involving manual parallel allocations. While the standard optimization engine correctly applies visit-splitting logic for long activities during automated scheduling, this logic is not consistently re-applied when activities are manually allocated in parallel. As a result, activities with identical characteristics—same duration, same location, and same assigned technician—can be treated differently from a visit segmentation perspective.
Business Impact
This limitation negatively impacts planning accuracy and schedule clarity. A single long visit spanning non-working hours gives the impression that a technician is continuously working without breaks, which does not reflect real operational constraints. This affects dispatcher confidence, planning visibility, capacity analysis, and downstream integrations that rely on accurate visit and shift representation. From an operational standpoint, planners expect consistent behavior for all long-running activities, regardless of whether they are allocated manually or by the optimization engine.
Expected Enhancement
It is requested that PSO consistently apply visit-splitting logic to all activities that exceed a technician’s shift duration, including those that are manually allocated in parallel. When multiple long activities are assigned to the same technician and location, each activity should be independently evaluated and split into visits according to the technician’s shift calendar. This would ensure consistent scheduling behavior, improve visibility of workload across shifts, and better align manual planning actions with automated optimization outcomes.