SBIR/STTR Research Award
(2) Time-dependent Robust Optimization for Medical Products (TROMP)
Award amount
$149,949Program and phase
SBIR · Phase IAward year
2020Award details
- Awarding agency
- Department of Defense
- Branch
- Navy
- Recipient company
- ADVENTIUM ENTERPRISES, LLC
- Proposal award date
- 2020-07-13
- Contract end date
- 2020-12-14
- UEI
- CJHNQPYZWBA3
- Solicitation number
- 20.4
- Topic code
- N204-A02
Research abstract
On this project, we will develop the Time-dependent Robust Optimization for Medical Products (TROMP) system. TROMP will enable medical logistics stakeholders to generate, evaluate, and implement dynamic, reliable, and robust plans and configurations for delivery of medical supplies, support, and care, supporting Navy and broader Department of Defense Medical Logistics (MEDLOG) operations. TROMP will enable MEDLOG planners, medical providers, and other stakeholders to specify a problem, generate a space of feasible and optimal solutions (rather than a single point solution), and explore how that space changes as problem constraints are modified. TROMP will implement these capabilities by translating MEDLOG problem constraints, objectives, and uncertainties for submission to Adventium’s GRAINS solver. Developed by Adventium on DARPA’s CASCADE program, GRAINS employs oriented matroids for reasoning about the geometric structure of problem solutions. Using GRAINS will enable TROMP to generate spaces of feasible solutions rather than simple point solutions, to compose or contrast the optimal solution spaces for competing objectives, and to generate robust, multi-period solutions to complex logistic problems.
Technology keywords
Not reported
Other awards by this recipient
Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.
- GUMBO Graphical User Interface
- VOLTA Maturation for AF/DoD Embedded Systems Analysis (V-MESA)
- INsight to Diverse Information using Graphs and Ontologies (INDIGO)
- Continuous Human Operator Requirements Analysis of Linkage (CHORAL)
- Fault Recovery Instruction Generation using Automata Derived from Traditional Engineering Models (FRIGATE)
Awards with the same topic code
Verified by an exact topic code match. This is not a text-similarity match.
- A Machine Learning Driven IOT Wireless Sensor Network for Digital Logistics in Remote and Fragile Environments
- SHIPMATE: Secure Hyper Intelligent Predictive Maintenance Analytics with Tactical Enhancement
- A Machine Learning Driven Wireless IOT Sensor Network for Remote and Fragile Environments
- Digital Logistics Challenges for Fragile, Remote Systems
- 1 - Embedded Condition Monitoring for Digital Logistics and Maintenance