Integrated Logistics and Operational Planning for Anticipated Threat Mitigation under Contested Logi
Abstract
The main objective of the proposed project is to create and study optimization models that integrate logistics planning into operati,onal planning for anticipated threat mitigation under contested environments. The unique features of integrated logistics and operat,ional planning at the tactical level present a number of challenges to operations research models and solution methodologies. First,, with an enlarged space of decision making, integrated optimization often results in a large-scale optimization model that is comput,ationally challenging to solve. Second, the anticipatory nature of tactical planning makes it necessary to make decisions under unce,rtainty, including both the exogenous uncertainty and endogenous uncertainty. Third, under a military combat environment with a comp,etitive and intelligent adversary, it is crucial in tactical planning to recognize, model, and address the adversary?s behaviors. Ho,wever, in a military combat setting the adversary?s information may only bepartially revealed through the network interdiction feedb,acks observed from the past interactions. Therefore, one must employ a mechanism to sequentially learn the adversary?s information w,hile attempting to achieve the best military objectives by interacting with them over time.To address these multi-faceted challenges,, this project is proposed to develop modeling and analytical toolkits to address integrated logistics and operational planning for,threat target mitigation under contested logistics environments by: (i) Developing and evaluating deterministic and stochastic optim,ization models for integrated tactical-level logistics and operational planning for anticipated threat target mitigation under conte,sted logistics environments, sequentially interacting with the weapon-target assignment problems at the operational level over a pla,nning horizon; (ii) Creating meaningful network interdiction models with sequential learning functionalities that leverage partial a,dversarial information revealed from the adversary?s network interdiction feedbacks; (iii) Developing decomposition-based exact and,heuristic algorithms to produce high-quality solutions under realistic computational budget for large-scale problem instances.The pr,imary goal of this research is to create knowledge (models and algorithms) that will lead to decision-support tools for naval logist,ical and operational planning for anticipated threat mitigation. This research will demonstrate the modeling power and computational, capability of mathematical optimization, including integer programming, stochastic programming, and approximate dynamic programming,, in applications related to naval planning and resource allocation. This project will also help engage with naval practitioners and, foster long-term collaborations between academia and naval industry and practice.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 08, 2022
- Source ID
- N000142212762
Entities
People
- Yongjia Song
Organizations
- Clemson University
- Office of Naval Research
- United States Navy