Resource Capacity Management via Dynamic Allocation
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Solution Overview
Problem
Existing capacity management systems face inefficiencies due to scheduled resources being idle when demand is low and inability to accept service requests when all scheduled resources are utilized, and there are challenges in finding resources that meet specific time and location requirements for service requests.
Innovation Solution
Implementing on-demand resource allocation systems that assess the availability and characteristics of resources in proximity to the origination point, using historical data to determine the probability of resource acceptance and aggregating individual probabilities to generate a cumulative acceptance probability for optimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If scheduled resources are used to ensure service capacity, then service reliability is improved, but resource idle time increases when demand is low
Solution Approach 1:
The system dynamically adjusts resource allocation between scheduled and on-demand modes based on real-time demand conditions. Resources transition from fixed scheduled allocation to flexible on-demand allocation, optimizing both service reliability and resource utilization efficiency
Solution Approach 2:
The system changes the operational parameters of resources by switching between different allocation modes (scheduled vs. on-demand). This parameter change allows the same resources to serve different functions based on demand levels, reducing idle time while maintaining service capacity
2Productivity
If on-demand resources are used to reduce idle time, then resource utilization efficiency is improved, but ability to meet specific time and location requirements deteriorates
Solution Approach 1:
The system uses feedback from historical data and real-time resource status to assess acceptance probability. This feedback mechanism enables the system to predict and compensate for variations in service delivery, maintaining time and location accuracy while using flexible on-demand resources
Solution Approach 2:
The system replaces traditional mechanical resource assignment with a probabilistic assessment model. By using historical data and algorithms to evaluate resource acceptance probability, the system achieves precise service delivery without rigid scheduling constraints
3Adaptability or versatility
If cumulative acceptance probability assessment is implemented, then resource allocation optimization is improved, but system complexity increases
Solution Approach 1:
The system creates a universal assessment framework that handles multiple resource types and service scenarios through a single cumulative probability model. This multi-functional approach simplifies complexity by providing a unified method for resource allocation optimization across diverse situations
Data Source
AI summary
A system and method for capacity management based on location and status of flexible (e.g., non-scheduled) service resources. Features are included to determine if the system can accept a request for a pick-up point (e.g., a restaurant, a quick order fulfillment center, grocery store, network location) based on the density, type and characteristics of the delivery resources in proximity (e.g., time and/or distance) to the pick-up point. The system also leverages historic delivery performance of the resources to assess whether to offer service or a service level for a given request.