Order Batch Scoring for Online Concierge Systems
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Solution Overview
Problem
Traditional online concierge systems face computational inefficiencies when assigning order batches to pickers due to the uncertainty of picker acceptance, leading to prohibitively expensive computational processes.
Innovation Solution
The system generates candidate order batches and scores them based on predicted rewards for each picker, allowing for per-picker evaluation and dynamic adjustment of offered batches, enabling effective assignment while reducing computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional solutions optimize across many possible order batches to account for picker acceptance uncertainty, then assignment effectiveness is improved, but computational cost becomes prohibitively expensive
Solution Approach 1:
The patent segments the optimization process into two distinct phases: (1) batch generation phase where multiple candidate order batches are created using heuristic methods, and (2) selection phase where pickers evaluate and accept/reject batches. This segmentation avoids the need to optimize across all possible batches simultaneously, reducing computational cost while maintaining assignment effectiveness through iterative refinement.
Solution Approach 2:
The system performs preliminary actions by generating multiple candidate order batches in advance using efficient heuristic algorithms before the picker acceptance stage. This preliminary batch generation allows the system to prepare diverse options without the computational burden of full optimization, enabling quick selection based on picker responses.
2Ease of operation
If the system offers order batches to pickers with the option to accept or reject, then picker autonomy and acceptance rates are improved, but the complexity of the assignment process increases
Solution Approach 1:
The patent implements a dynamic assignment process where order batches are offered to pickers who can accept or reject based on their current capacity and preferences. The system dynamically adjusts by offering alternative batches to pickers who reject initial offers, creating a flexible and adaptive assignment process that balances picker autonomy with system efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms where picker acceptance or rejection decisions are used to guide subsequent batch offerings. When pickers reject batches, the system uses this feedback to generate and offer alternative batches, creating a closed-loop process that improves assignment effectiveness while maintaining picker autonomy through iterative refinement.
Data Source
AI summary
An online concierge system generates order batches for pickers and offers those order batches. The online concierge system generates a set of candidate order batches, which are subsets of a received set of orders. The online concierge system generates a set of order batch scores for each of a set of candidate pickers, and offers the candidate order batches to the candidate pickers for the candidate pickers to service based on the set of order batch scores. If a candidate picker accepts the offered order batch, the online concierge system identifies candidate order batches with overlapping orders with the order batch accepted by the candidate picker, and rescinds the offer for each of the candidate pickers to service the order batches.