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Adaptive Distributed Allocation of Probabilistic Tasks (ADAPT)

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911NF-19-P-0009
Agency Tracking Number: A18B-007-0243
Amount: $149,993.46
Phase: Phase I
Program: STTR
Solicitation Topic Code: A18B-T007
Solicitation Number: 18.B
Timeline
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2018-12-14
Award End Date (Contract End Date): 2019-06-13
Small Business Information
12 Gill Street Suite 1400
Woburn, MA 01801
United States
DUNS: 967259946
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Georigy Levchuk
 Senior Principal Scientist
 (781) 496-2467
 georgiy@aptima.com
Business Contact
 Thomas McKenna
Phone: (781) 496-2443
Email: brouady@aptima.com
Research Institution
 Northeastern University
 Susan M. Dorsey Susan M. Dorsey
 
360 Huntington Avenue
Boston, MA 02115
United States

 (617) 373-3874
 Nonprofit College or University
Abstract

Logistic operations for all branches of the military have historically been a guessing game relying on the intuition and past experience of the responsible officials. Unfortunately, this strategy often means that resources are deployed prematurely to areas that do not have an immediate need, thereby denying those assets to other areas that could have used them immediately. Worse, resources can also be deployed late, past the time in which they can be used. By formulating a systematic method to schedule resource allocation for shared resources, these inefficiencies can be eliminated. Aptima proposes to develop Adaptive Distributed Allocation of Probabilistic Tasks (ADAPT), to approach this allocation problem using a decentralized dynamic adaptive planning technique. This solution will take uncertainty, risk, and networked resources into account during its resource assignments for mission needs. ADAPT will develop and test our resource assignment scheduling algorithm, to identify the most robust allocation schedule that meets the user identified needs, priorities, and preferences of the missions. This optimization will take several factors into account such as mission priority, fit of resource functionality to requested need, and time delay between request and fulfillment of resource need.

* Information listed above is at the time of submission. *

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