Resource Allocation Manager Using Genetic Algorithms

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Solution Overview

Problem

Businesses face challenges in managing cash reserves effectively, as levels that are too high are not cost-efficient while levels that are too low can lead to operational risks, necessitating a balance for maximizing profits and maintaining resource allocation.

Innovation Solution

A computer system with a resource allocation manager that generates a finance-investment plan using genetic algorithms, balancing cash reserves by evaluating asset and liability patterns, maintaining an asset-to-liability ratio, and forecasting potential cash flows to optimize cash flow management.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a business maintains a high working capital reserve level, then operational reliability is improved, but cost efficiency deteriorates

Engineering Contradiction:

Improveoperational reliabilityVSAvoidcost efficiency

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system dynamically adjusts the working capital reserve level parameter based on genetic algorithm optimization, transforming it from a static high level to an optimized variable level that balances reliability and cost efficiency

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The genetic algorithm automatically optimizes the working capital reserve level without manual intervention, allowing the system to self-adjust to the optimal balance between operational reliability and cost efficiency

Inventive Principle:

Principle #25Self-service

2Loss of energy

If a business maintains a low working capital reserve level, then cost efficiency is improved, but operational reliability deteriorates

Engineering Contradiction:

Improvecost efficiencyVSAvoidoperational reliability

Core Design Contradiction:

Loss of energyVSReliability

Solution Approach 1:

The system transforms the working capital reserve level from a static low level to a dynamically optimized parameter that ensures sufficient operational reliability while maintaining cost efficiency

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The genetic algorithm self-adjusts the working capital reserve level to the optimal point, eliminating the need for manual balancing between cost efficiency and operational reliability

Inventive Principle:

Principle #25Self-service

3Productivity

If traditional cash flow management methods are used, then device complexity is reduced, but productivity deteriorates

Engineering Contradiction:

Improvecash flow management efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces traditional manual cash flow management methods with an automated genetic algorithm-based system, substituting mechanical/manual processes with computational optimization to dramatically improve cash flow management efficiency

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS8744888B2Resource allocation management

Publication Date: 2014.06.03 SAP SE

AI summary

In accordance with aspects of the disclosure, a system and methods are provided for managing resource allocation by generating a finance-investment plan relative to one or more time intervals while maintaining a cash reserve at a predetermined threshold based on information related to financial activities including asset related activities and liability related activities. The systems and methods may include evaluating accounts receivable patterns for each asset to determine cash surplus ranges within the one or more time intervals, evaluating accounts payable patterns for each liability to determine cash flow gaps within the one or more time intervals, maintaining an asset-to-liability ratio during the one or more time intervals, and generating the finance-investment plan while maintaining the cash reserve at the predetermined threshold within the one or more time intervals based on the asset-to-liability ratio and potential cash flow forecasting schemes for each asset and liability.