International business machines corporation (20240135312). Ally-Adversary Bimodal Resource Allocation Optimization simplified abstract
Contents
- 1 Ally-Adversary Bimodal Resource Allocation Optimization
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Ally-Adversary Bimodal Resource Allocation Optimization - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Ally-Adversary Bimodal Resource Allocation Optimization
Organization Name
international business machines corporation
Inventor(s)
Shivaram Subramanian of Frisco TX (US)
Pavithra Harsha of Pleasantville NY (US)
Ali Koc of WHITE PLAINS NY (US)
Brian Leo Quanz of Yorktown Heights NY (US)
Mahesh Ramakrishna of East Brunswick NJ (US)
Dhruv Shah of New York NY (US)
Ally-Adversary Bimodal Resource Allocation Optimization - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135312 titled 'Ally-Adversary Bimodal Resource Allocation Optimization
Simplified Explanation
The patent application describes a mechanism for generating a resource allocation in an omnichannel distribution network using an ally-adversary bimodal inventory optimization (BIO) computer model. This model considers both worst-case and best-case scenarios of resource demand and availability to generate a predicted consumption for the resource and recommend resource allocations to different locations in the distribution network.
- Demand forecast data and current inventory data are obtained for a resource and the omnichannel distribution network.
- An ally-adversary bimodal inventory optimization (BIO) computer model is instantiated, including an adversary component simulating worst-case scenarios and an ally component simulating best-case scenarios.
- The BIO model is applied to the data to generate a predicted consumption for the resource.
- A resource allocation recommendation is generated based on the predicted consumption and output to a downstream computing system for further processing.
Potential Applications
This technology can be applied in various industries such as retail, e-commerce, logistics, and supply chain management to optimize resource allocation and distribution networks.
Problems Solved
1. Efficient resource allocation in omnichannel distribution networks. 2. Balancing worst-case and best-case scenarios for resource demand and availability.
Benefits
1. Improved inventory management. 2. Enhanced decision-making for resource allocation. 3. Optimal utilization of resources in a distribution network.
Potential Commercial Applications
Optimizing inventory management in retail stores, streamlining logistics operations in e-commerce companies, and improving supply chain efficiency for manufacturers.
Possible Prior Art
One possible prior art could be traditional inventory optimization models that do not consider both worst-case and best-case scenarios in resource allocation.
What are the potential limitations of this technology in real-world applications?
The technology may face challenges in accurately predicting resource demand and availability in dynamic environments, as real-world scenarios can be more complex than simulated models.
How scalable is this technology for large distribution networks?
The scalability of the technology may depend on the computational resources available and the complexity of the distribution network. Implementing the BIO model for large-scale networks may require significant computational power and data processing capabilities.
Original Abstract Submitted
mechanisms are provided for generating a resource allocation in an omnichannel distribution network. demand forecast data and current inventory data related to a resource and the omnichannel distribution network are obtained and an ally-adversary bimodal inventory optimization (bio) computer model is instantiated that includes an adversary component that simulates, through a computer simulation, a worst-case scenario of resource demand and resource availability, and an ally component that limits the adversary component based on a simulation of a limited best-case scenario of resource demand and resource availability. the bio computer model is applied to the demand forecast data and current inventory data, to generate a predicted consumption for the resource. a resource allocation recommendation is generated for allocating the resource to locations of the omnichannel distribution network based on the predicted consumption, which is output to a downstream computing system for further processing.
