A linear programming-based method for job shop scheduling

We present a decomposition heuristic for a large class of job shop scheduling problems. This heuristic utilizes information from the linear programming formulation of the associated optimal timing problem to solve subproblems, can be used for any objective function whose associated optimal timing problem can be expressed as a linear program (LP), and is particularly effective for objectives that include a component that is a function of individual operation completion times. Using the proposed heuristic framework, we address job shop scheduling problems with a variety of objectives where intermediate holding costs need to be explicitly considered. In computational testing, we demonstrate the performance of our proposed solution approach

A simulation framework for the analysis of reusable launch vehicle operations and maintenance

During development of a complex system, feasibility initially overshadows other concerns, in some cases leading to a design which may not be viable long-term. In particular for the case of Reusable Launch Vehicles, Operations&Maintenance comprises the majority of the vehicle's LCC, whose stochastic nature precludes direct analysis. Through the use of simulation, probabilistic methods can however provide estimates on the economic behavior of such a system as it evolves over time. Here the problem of operations optimization is examined through the use of discrete event simulation. The resulting tool built from the lessons learned in the literature review simulates a RLV or fleet of vehicles undergoing maintenance and the maintenance sites it/they visit as the campaign evolves over a period of time. The goal of this work is to develop a method for uncovering an optimal operations scheme by investigating the effect of maintenance technician skillset distributions on important metrics such as the achievable annual flight rate and maintenance man hours spent on each vehicle per flight. Using these metrics, the availability of technicians for each subsystem is optimized to levels which produce the greatest revenue from flights and minimum expenditure from maintenance.MSCommittee Chair: Mavris, Dimitri; Committee Member: Edwards, Stephen; Committee Member: Volovoi, Vital

Agile load transportation systems using aerial robots

In this dissertation, we address problems that can occur during load transport using aerial robots, i.e., small scale quadrotors. First, detailed models of such transportation system are derived. These models include nonlinear models of a quadrotor, a model of a quadrotor carrying a fixed load and a model of a quadrotor carrying a suspended load. Second, the problem of quadrotor stabilization and trajectory tracking with changes of the center of gravity of the transportation system is addressed. This problem is solved using model reference adaptive control based on output feedback linearization that compensates for dynamical changes in the center of gravity of the quadrotor. The third problem we address is a problem of a swing-free transport of suspended load using quadrotors. Flying with a suspended load can be a very challenging and sometimes hazardous task as the suspended load significantly alters the flight characteristics of the quadrotor. In order to deal with suspended load flight, we present a method based on dynamic programming which is a model based offline method. The second investigated method we use is based on the Nelder-Mead algorithm which is an optimization technique used for nonlinear unconstrained optimization problems. This method is model free and it can be used for offline or online generation of the swing-free trajectories for the suspended load. Besides the swing-free maneuvers with suspended load, load trajectory tracking is another problem we solve in this dissertation. In order to solve this problem we use a Nelder-Mead based algorithm. In addition, we use an online least square policy iteration algorithm. At the end, we propose a high level algorithm for navigation in cluttered environments considering a quadrotor with suspended load. Furthermore, distributed control of multiple quadrotors with suspended load is addressed too. The proposed hierarchical architecture presented in this doctoral dissertation is an important step towards developing the next generation of agile autonomous aerial vehicles. These control algorithms enable quadrotors to display agile maneuvers while reconfiguring in real time whenever a change in the center of gravity occurs. This enables a swing-free load transport or trajectory tracking of the load in urban environments in a decentralized fashion

Applying the Multiple Multidimensional Knapsack Assignment Problem to a Cargo Allocation and Transportation Problem with Stochastic Demand

The US military relies on airlift to not only deploy and sustain U.S. armed forces anywhere in the world but also to rapidly mobilize humanitarian efforts and supplies. Operations already impacted by the limited capacity of aircraft also fall prey to dynamic requirements and differing priorities of multiple global locations. A growing concern for the modern military budget is how to provide airlift functions expediently and economically while mitigating the costs of shortfalls and overages. Utilizing fiscal year 2017-2018 cargo data published by the 618th Air Operations Center and modeling this problem as a multiple multidimensional knapsack assignment problem (MMKAP), this work investigates how categorical assumptions about demand affect aircraft allocation and assesses the economic penalties associated with shorting or exceeding demand in the event of mis-estimation given a stochastic demand. This work starts with the general formulation of a new variant of the MMKAP and applies the MMKAP to a notional military airlift example with two supply, two demand nodes, two item types, and three aircraft types. After a deterministic solution is found, the effects of a stochastic demand are explored using different cost models and random draws from distribution functions based on reported cargo shipment data. This research concludes that there are levels at which demand expectations can be set to mitigate economic penalties given a fixed cost penalty and a variable cost penalty