17965070. Ally-Adversary Bimodal Resource Allocation Optimization simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
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 17965070 titled 'Ally-Adversary Bimodal Resource Allocation Optimization
Simplified Explanation
The patent application describes mechanisms for generating a resource allocation in an omnichannel distribution network using an ally-adversary bimodal inventory optimization (BIO) computer model. The model simulates worst-case and best-case scenarios of resource demand and availability to generate a predicted consumption for the resource, which is then used to recommend resource allocations 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 for the distribution network.
Potential Applications
This technology could be applied in various industries such as retail, e-commerce, logistics, and supply chain management to optimize resource allocations in omnichannel distribution networks.
Problems Solved
1. Efficient resource allocation in complex omnichannel distribution networks. 2. Balancing worst-case and best-case scenarios to make informed decisions.
Benefits
1. Improved inventory management. 2. Enhanced operational efficiency. 3. Better utilization of resources.
Potential Commercial Applications
Optimizing inventory allocation in retail stores, improving order fulfillment in e-commerce platforms, streamlining logistics operations in supply chain management.
Possible Prior Art
Prior art in inventory optimization models, supply chain management systems, and distribution network optimization tools may exist. However, specific details would need to be researched to identify relevant prior art.
Unanswered Questions
How does the ally-adversary model handle uncertainties in demand forecasting and inventory data?
The ally-adversary model likely incorporates probabilistic methods to account for uncertainties in demand forecasting and inventory data, adjusting resource allocations accordingly.
What computational resources are required to run the ally-adversary bimodal inventory optimization (BIO) model efficiently?
The computational resources needed to run the BIO model efficiently may vary depending on the scale of the distribution network and the complexity of the demand forecasting data. High-performance computing systems or cloud-based solutions may be necessary for large-scale applications.
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.
