Resource Allocation Optimization via Parameterized Distribution
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Solution Overview
Problem
Existing resource management approaches for fleets and equipment are inefficient and require significant computational power, making them unsuitable for large-scale operations and resource allocation optimization.
Innovation Solution
A heuristic method for resource management that categorizes resources into classes, determines a target distribution based on optimization parameterization using weightings, and adjusts the computational effort by limiting recursion depth, allowing efficient allocation of resources to meet use requests with minimal processing power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional resource management approaches are used for fleet optimization, then resource allocation can be achieved, but computational power requirements become excessive and processing time increases
Solution Approach 1:
The patent segments the resource allocation problem into discrete use requests and individual resources, representing them as a structured data model. Each use request is broken down into specific parameters (resource class, time period, location) that can be independently evaluated. This segmentation allows the system to process allocation decisions in manageable units rather than as a monolithic optimization problem, reducing computational burden while maintaining allocation effectiveness.
Solution Approach 2:
The patent transforms the resource allocation problem from a continuous optimization challenge into a discrete parameter-matching exercise. By defining specific parameters for use requests (resource class, start time, end time, location) and comparing them against available resources, the system changes the problem parameters from requiring complex mathematical optimization to requiring efficient parameter comparison and matching. This parameter transformation enables implementation on devices with limited processing power.
2Manufacturing precision
If complex optimization algorithms are applied to determine target distribution, then allocation optimality improves, but device complexity and processing requirements increase
Solution Approach 1:
The patent implements a self-service approach where the system automatically determines target resource distribution by evaluating use requests against available resources based on predefined criteria. The optimization logic is embedded in the system itself, which autonomously performs the allocation decisions without requiring external complex optimization algorithms. The system serves its own optimization needs through internal evaluation of use request parameters against resource availability, maintaining allocation quality while avoiding external computational complexity.
3Productivity
If traditional fleet management systems are used, then comprehensive resource control is achieved, but implementation cost and processing time become prohibitive for large fleets
Solution Approach 1:
The patent performs preliminary actions by pre-defining resource classes and their characteristics, and by structuring use requests with all necessary parameters specified in advance. The system prepares the allocation evaluation by having use requests already formatted with resource class, time period, and location information before the actual allocation decision is made. This preliminary structuring of data eliminates the need for complex real-time processing during allocation, reducing processing time while maintaining comprehensive fleet management capabilities.
Data Source
AI summary
A method for resource management includes: providing a representation of a set of resources; obtaining a set of use requests, wherein a use request can be fulfilled by allocating a resource to the use request; wherein the resources are categorized into resource classes, so that resources in a resource class are interchangeable with respect to fulfilling a use request; wherein a use request can be fulfilled for a specific use period requested by the use request, by allocating a resource to a resource class requested by the use request; wherein a target distribution of resources for use requests is determined on the basis of an optimization parameterization; wherein the optimization parameterization is based on weightings of allocations of resources to use requests; wherein, further, to determine the target distribution, a starting solution is created that represents a distribution of resources for use requests, is provided.