Service Request Allocation via Predicted Provider Shortage Severity
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Solution Overview
Problem
Existing systems face challenges in efficiently allocating service requests to service providers during peak hours and in undersupplied areas, leading to user dissatisfaction, reduced productivity, and increased operational costs for service regulators.
Innovation Solution
A method and system that adaptively allocate service requests by detecting predicted severity levels of service provider shortages at destination and current locations, adjusting priority levels accordingly, and allocating the highest-priority request to the service provider.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dynamic incentives are provided to service providers to move towards undersupplied areas, then service provider positioning is improved, but operational cost increases
Solution Approach 1:
The system enables service providers to autonomously decide whether to accept service requests based on their current location and predicted supply severity, eliminating the need for external incentives. Providers self-adjust their positioning by selectively accepting requests in undersupplied areas, achieving supply repositioning without monetary costs to the regulator.
Solution Approach 2:
The system implements a feedback mechanism where service providers receive real-time information about predicted supply severity levels at their current and destination locations. This feedback enables them to make informed decisions about accepting requests that will move them toward undersupplied areas, optimizing positioning through information-driven behavior rather than incentive-driven behavior.
2Productivity
If service requests are allocated to move service providers towards undersupplied areas, then market repositioning is improved, but service request fulfillment reliability deteriorates
Solution Approach 1:
The system dynamically adjusts the priority level parameter of service requests based on predicted supply severity levels. Requests in areas with higher supply severity receive higher priority levels, enabling the allocation system to prioritize moving providers to undersupplied areas while still ensuring critical requests are fulfilled. This parameter-based prioritization balances repositioning goals with fulfillment reliability.
3Productivity
If priority levels are adjusted based on predicted supply severity, then allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary detection of predicted supply severity levels at destination locations before allocating service requests. By pre-calculating supply severity and using it to adjust priority levels, the system enables efficient allocation decisions without requiring complex real-time optimization during the allocation moment. The complexity is shifted to the prediction phase, simplifying the actual allocation process.
Data Source
AI summary
The present disclosure provides a system and a method for allocating one of a plurality of service requests to a service provider, each service request of the plurality of service requests requiring the service provider to move from one location to another to complete the each service request, the method comprising: detecting a first predicted severity level of a lack of available service providers for completing available service requests condition at a destination location indicated in a service request of the plurality of service request during an estimated arrival time window within which the service provider is estimated to move to the destination location from a pickup location to complete the service request; increasing a priority level of the service request based on the first predicted severity level; and allocating one service request associated with a highest priority level from the plurality of service requests to the service provider.