Algorithmic COT Allocation With Dynamic Auction Pricing
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Solution Overview
Problem
Current systems for managing distribution of certificates of transportation (COTs) via online auctions lack intelligent functionality to handle situations where the number of available and requested COTs do not align, and fail to dynamically determine pricing based on allocation, leading to inefficiencies in asset distribution.
Innovation Solution
An algorithmic distribution application that intelligently manages COT distribution by aggregating bid requests, adjusting allocations, and dynamically determining prices based on availability and bid placement, ensuring fair distribution and optimal pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current simple auction systems are used to manage COT distribution, then the system is easy to operate, but it cannot intelligently determine allocation when available and requested COTs do not align, and cannot dynamically determine pricing
Solution Approach 1:
The patent implements dynamic allocation and pricing mechanisms where the system automatically adjusts COT assignments and prices based on real-time auction data, availability constraints, and bid patterns. The algorithm dynamically determines winning bids, allocation quantities, and prices rather than using fixed rules, enabling intelligent adaptation to varying auction scenarios while maintaining automated operation.
Solution Approach 2:
The system incorporates feedback loops where auction results, allocation outcomes, and pricing decisions are continuously refined based on bid responses and availability constraints. The algorithm learns from auction patterns and adjusts future allocations and pricing strategies, enabling intelligent decision-making that improves over time while maintaining system automation.
2Productivity
If COTs are allocated based on maximum bid only, then the allocation process is simple, but it does not account for availability constraints or dynamic pricing opportunities
Solution Approach 1:
The patent changes multiple parameters simultaneously including bid amounts, allocation quantities, and prices based on availability constraints and auction dynamics. Rather than fixing allocation rules, the system adjusts these parameters dynamically to optimize both efficiency and fairness, achieving high productivity while maintaining automated operation through sophisticated algorithms.
Solution Approach 2:
The allocation process transitions from static maximum-bid-only rules to dynamic multi-parameter optimization. The system continuously adjusts allocation decisions based on real-time factors including COT availability, bid patterns, and pricing strategies, enabling efficient resource distribution without requiring manual intervention.
3Measurement precision
If each COT is treated individually in auctions, then pricing can be precise, but determining how many COTs to allocate becomes complex when availability and requests do not align
Solution Approach 1:
The patent merges individual COT auction processes into a unified algorithmic framework that handles multiple COTs simultaneously. The system combines pricing precision with batch allocation logic, determining both individual COT prices and overall allocation quantities through integrated algorithms that resolve availability-constraint mismatches automatically.
Solution Approach 2:
The allocation system serves multiple functions simultaneously: it determines individual COT prices with precision, allocates COTs across multiple bidders, handles availability constraints, and resolves mismatches between supply and demand. This multi-functional approach achieves both pricing precision and efficient allocation without requiring separate manual processes.
4Adaptability or versatility
If the system allows flexible bid requests, then bidder participation increases, but determining winner allocation and pricing becomes more challenging
Solution Approach 1:
The system handles flexible bid requests through dynamic algorithms that adapt to varying bid patterns, quantities, and price points. The winner determination process dynamically evaluates all bid combinations against availability constraints and optimizes allocation outcomes, enabling high bidder participation while maintaining automated, efficient processing through sophisticated computational logic.
Data Source
AI summary
An apparatus, a method, and a non-transitory computer readable medium provide for intelligently managing distribution of certificates of transportations (COTs). In embodiments, an application server is configured to execute an algorithmic distribution application configured to manage distribution of available COTs based on a plurality of bid requests received from bidders. The algorithmic distribution application may not merely determine a winner of an online auction by determining a bidder that placed the largest bid, but may include functionality to intelligently determine whether all available COTs have been requested or not, and to adjust the allocation of the available COTs to the requesting bidders according to the availability of the COTs, as well as to dynamically determine a price of the allocated COTs, which may not always be a maximum bid of the winning but may be adjusted based on the allocation of the available COTs to the requested bids.