6,482 research outputs found
Essays on Integer Programming in Military and Power Management Applications
This dissertation presents three essays on important problems motivated by military and power management applications. The array antenna design problem deals with optimal arrangements of substructures called subarrays. The considered class of the stochastic assignment problem addresses uncertainty of assignment weights over time. The well-studied deterministic counterpart of the problem has many applications including some classes of the weapon-target assignment. The speed scaling problem is of minimizing energy consumption of parallel processors in a data warehouse environment. We study each problem to discover its underlying structure and formulate tailored mathematical models. Exact, approximate, and heuristic solution approaches employing advanced optimization techniques are proposed. They are validated through simulations and their superiority is demonstrated through extensive computational experiments. Novelty of the developed methods and their methodological contribution to the field of Operations Research is discussed through out the dissertation
Multi-robot task allocation for safe planning under dynamic uncertainties
This paper considers the problem of multi-robot safe mission planning in
uncertain dynamic environments. This problem arises in several applications
including safety-critical exploration, surveillance, and emergency rescue
missions. Computation of a multi-robot optimal control policy is challenging
not only because of the complexity of incorporating dynamic uncertainties while
planning, but also because of the exponential growth in problem size as a
function of the number of robots. Leveraging recent works obtaining a tractable
safety maximizing plan for a single robot, we propose a scalable two-stage
framework to solve the problem at hand. Specifically, the problem is split into
a low-level single-agent planning problem and a high-level task allocation
problem. The low-level problem uses an efficient approximation of stochastic
reachability for a Markov decision process to handle the dynamic uncertainty.
The task allocation, on the other hand, is solved using polynomial-time forward
and reverse greedy heuristics. The safety objective of our multi-robot safe
planning problem allows an implementation of the greedy heuristics through a
distributed auction-based approach. Moreover, by leveraging the properties of
the safety objective function, we ensure provable performance bounds on the
safety of the approximate solutions proposed by these two heuristics. Our
result is illustrated through case studies
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