Dynamic Capacity Ranges for Workforce Routing Optimization
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Solution Overview
Problem
Conventional appointment scheduling systems lack the information needed to efficiently group service appointments by physical location, leading to inefficient routing and increased late arrivals or missed appointments due to over-traversal of the service area.
Innovation Solution
The system provides booking agents with advanced knowledge of ideal service appointment windows based on physical job locations and worker availability, using dynamic capacity ranges and asymmetric time poles to optimize routing patterns, such as fan-out, fan-in and bisecting coverage, which helps in better scheduling and subsequent workforce routing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional appointment scheduling systems are used, then scheduling simplicity is maintained, but routing efficiency deteriorates and unassigned jobs increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating capacity ranges and ideal service windows based on historical data and worker patterns before actual scheduling occurs. This allows the routing system to operate more efficiently without requiring complex real-time calculations during the scheduling process itself.
Solution Approach 2:
The patent introduces capacity range estimates and ideal service window calculations as intermediary elements between raw worker availability data and final routing decisions. These intermediaries simplify the routing process by providing pre-processed information that guides route optimization without requiring the routing algorithm to handle all complexity directly.
2Productivity
If workers service all jobs within their shifts, then job completion increases, but worker travel time and fatigue increase
Solution Approach 1:
The system applies local quality by determining capacity ranges and ideal service windows specific to each worker's location, shift timing, and historical patterns. Rather than treating all workers uniformly, the system customizes service window estimates for each worker based on their specific characteristics and geographic context, optimizing routes for individual workers rather than applying a one-size-fits-all approach.
Solution Approach 2:
The patent implements dynamics by making service window estimates adaptive and flexible rather than fixed. The system continuously refines capacity range estimates based on actual worker performance, geographic location, and temporal patterns, allowing the scheduling system to dynamically adjust service windows to optimize both job completion and worker travel time efficiency.
3Ease of operation
If dynamic capacity ranges are implemented, then worker distribution improves, but computational requirements increase
Solution Approach 1:
The system performs preliminary computations to establish capacity ranges and ideal service windows before the actual scheduling and routing processes. By pre-calculating these parameters based on historical data and worker patterns, the system reduces the computational burden during real-time routing operations, making the overall system more efficient despite the initial computational requirements.
Solution Approach 2:
The patent utilizes parameter changes by transforming raw worker availability and performance data into derived parameters such as capacity ranges and ideal service windows. These transformed parameters are then used in routing optimization, reducing the complexity of computational operations by working with pre-processed, meaningfully derived parameters rather than raw data.
Data Source
AI summary
A system and method for workforce scheduling using dynamic capacity ranges are provided. A capacity model associates distances of locations within a service area from a depot with a series of appointment time windows such that appointment time windows having a relatively greater amount of available worker capacity are associated with a greater range of distances than appointment time windows having a relatively smaller amount of available worker capacity. After a distance from the depot to a location of an appointment to be scheduled is determined, appointment time window suggestions are automatically produced based on which appointment time windows as defined in the capacity model are associated with the location of the appointment to be scheduled. As capacity changes due to bookings, the capacity model is dynamically updated. A system and method for workforce routing, based at least in part on using dynamic capacity ranges, are also provided.