Production Allocation Apparatus Using Supply Function Models
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Solution Overview
Problem
Existing methods for determining optimal production allocations in demand-supply systems face challenges in efficiently calculating allocations considering predicted demand quantities and change rate constraints of producers, often requiring iterative processes that are time-consuming and resource-intensive.
Innovation Solution
A production allocation determining apparatus that calculates total supply functions, determines supply quantity limits, and optimizes marginal costs to allocate production quantities among multiple producers, using a combination of supply function models and change rate constraints to repeatedly adjust allocations based on predicted demand and capacity limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If iterative methods (equal λ method) are used to calculate optimal allocations considering change rate constraints and supply limits, then allocation optimality is improved, but computational time and resource consumption increase
Solution Approach 1:
The patent pre-calculates and stores supply function models for each producer before the actual allocation determination. These models represent the relationship between supply quantities and marginal costs in advance, allowing the system to quickly query pre-computed information during runtime rather than performing complex iterative calculations, thus reducing computational time while maintaining allocation optimality
Solution Approach 2:
The patent creates simplified representations (supply function models) that copy the essential characteristics of complex producer behaviors and cost structures. These models enable the system to work with condensed data representations instead of full detailed models, significantly reducing computational complexity while preserving the ability to determine optimal allocations
2Measurement precision
If iterative processes are used to adjust allocations based on marginal costs and supply limits, then allocation accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The patent creates simplified representations (supply function models) that copy the essential characteristics of complex producer behaviors and cost structures. These models enable the system to work with condensed data representations instead of full detailed models, significantly reducing computational complexity while preserving the ability to determine optimal allocations
Solution Approach 2:
The patent extracts only the essential relationships between supply quantities and marginal costs into separate supply function models, isolating the critical information needed for allocation decisions from the complete detailed data. This extraction allows the main allocation process to operate with minimal data, reducing computational resource consumption while maintaining allocation accuracy
Data Source
AI summary
A production allocation determining apparatus is configured to calculate a total supply function model representing a relationship between a total supply quantity and a marginal cost; calculate, based on current supply quantities, change rate constraints, and on capacity upper and lower limits, supply quantity upper and lower limits of the respective producers at a time s; calculate an optimum price based on the total supply function model, a predicted value of a demanded quantity at the time s, and on the supply quantity upper and lower limits; and calculate, based on the supply quantity upper and lower limits, the supply function models, and on the optimum price, optimum supply quantities. Calculation of the supply quantity upper and lower limits, calculation of the optimum price, and calculation of the optimum supply quantities are repeatedly executed from the time s=T to the time s=1.