Multi-Objective Model to Improve Network Reliability Level under Limited Budget by Considering Selection of Facilities and Total Service Distance in Rescue Operations

Sudden disasters may damage facilities, transportation networks and other critical infrastructures, delay rescue and bring huge losses. Facility selection and reliable transportation network play an important role in emergency rescue. In this paper, the reliability level between two points in a network is defined from the point of view of minimal edge cut and path, respectively, and the equivalence of these two definitions is proven. Based on this, a multi-objective optimization model is proposed. The first goal of the model is to minimize the total service distance, and the second goal is to maximize the network reliability level. The original model is transformed into a model with three objectives, and the three objectives are combined into one objective by the method of weighting. The model is applied to a case, and the results are analyzed to verify the effectiveness of the model

Reconstituting the Federalism Battle in Energy Transportation

This article explores the growing federalism tensions in efforts to expand the nation’s energy transportation infrastructure — the electric transmission lines, natural gas pipelines, natural gas import and export terminals and related infrastructure that power the U.S. electricity and transportation systems. It uses two illustrations — one involving an interstate electric transmission line (subject to state jurisdiction) and one involving and an interstate natural gas pipeline (subject to federal jurisdiction) — to highlight how the clear jurisdictional lines between federal and state authority over these projects created decades ago is no longer adequate for today’s energy needs. We believe that many of the recent efforts by states and federal agencies to re-draw these jurisdictional battle lines in the context of particular projects have been counterproductive. They have they encouraged interest groups to entrench their respective positions in favor of state or federal regulatory power. They also have thwarted comprehensive and efficient energy planning, and have stood in the way of greater integration of new technologies and more diverse energy resources. Ultimately, we conclude that federal regulators — which have historically been much more attuned to federal and national energy needs in making project siting decisions — must be more proactive in addressing state interests and concerns associated with multi-state energy transport projects in cases where federal siting authority trumps that of the states. Likewise, for projects where the states possess primary regulatory authority that acts as a potential veto point over projects that promote federal and regional energy needs, a more significant federal role in evaluating those federal and regional needs is warranted

ACCIDENT ANALYSIS, RISK AND RELIABILITY MODELING OF MARINE TRANSPORTATION SYSTEMS

Ph.DDOCTOR OF PHILOSOPH

INCORPORATING TRAVEL TIME RELIABILITY INTO TRANSPORTATION NETWORK MODELING

Travel time reliability is deemed as one of the most important factors affecting travelers’ route choice decisions. However, existing practices mostly consider average travel time only. This dissertation establishes a methodology framework to overcome such limitation. Semi-standard deviation is first proposed as the measure of reliability to quantify the risk under uncertain conditions on the network. This measure only accounts for travel times that exceed certain pre-specified benchmark, which offers a better behavioral interpretation and theoretical foundation than some currently used measures such as standard deviation and the probability of on-time arrival. Two path finding models are then developed by integrating both average travel time and semi-standard deviation. The single objective model tries to minimize the weighted sum of average travel time and semi-standard deviation, while the multi-objective model treats them as separate objectives and seeks to minimize them simultaneously. The multi-objective formulation is preferred to the single objective model, because it eliminates the need for prior knowledge of reliability ratios. It offers an additional benefit of providing multiple attractive paths for traveler’s further decision making. The sampling based approach using archived travel time data is applied to derive the path semi-standard deviation. The approach provides a nice workaround to the problem that there is no exact solution to analytically derive the measure. Through this process, the correlation structure can be implicitly accounted for while simultaneously avoiding the complicated link travel time distribution fitting and convolution process. Furthermore, the metaheuristic algorithm and stochastic dominance based approach are adapted to solve the proposed models. Both approaches address the issue where classical shortest path algorithms are not applicable due to non-additive semi-standard deviation. However, the stochastic dominance based approach is preferred because it is more computationally efficient and can always find the true optimal paths. In addition to semi-standard deviation, on-time arrival probability and scheduling delay measures are also investigated. Although these three measures share similar mathematical structures, they exhibit different behaviors in response to large deviations from the pre-specified travel time benchmark. Theoretical connections between these measures and the first three stochastic dominance rules are also established. This enables us to incorporate on-time arrival probability and scheduling delay measures into the methodology framework as well

Doctor of Philosophy

dissertationThe Active Traffic and Demand Management (ATDM) initiative aims to integrate various management strategies and control measures so as to achieve the mobility, environment and sustainability goals. To support the active monitoring and management of real-world complex traffic conditions, the first objective of this dissertation is to develop a travel time reliability estimation and prediction methodology that can provide informed decisions for the management and operation agencies and travelers. A systematic modeling framework was developed to consider a corridor with multiple bottlenecks, and a series of close-form formulas was derived to quantify the travel time distribution under both stochastic demand and capacity, with possible on-ramp and off-ramp flow changes. Traffic state estimation techniques are often used to guide operational management decisions, and accurate traffic estimates are critically needed in ATDM applications designed for reducing instability, volatility and emissions in the transportation system. By capturing the essential forward and backward wave propagation characteristics under possible random measurement errors, this dissertation proposes a unified representation with a simple but theoretically sound explanation for traffic observations under free-flow, congested and dynamic transient conditions. This study also presents a linear programming model to quantify the value of traffic measurements, in a heterogeneous data environment with fixed sensors, Bluetooth readers and GPS sensors. It is important to design comprehensive traffic control measures that can systematically address deteriorating congestion and environmental issues. To better evaluate and assess the mobility and environmental benefits of the transportation improvement plans, this dissertation also discusses a cross-resolution modeling framework for integrating a microscopic emission model with the existing mesoscopic traffic simulation model. A simplified car-following model-based vehicle trajectory construction method is used to generate the high-resolution vehicle trajectory profiles and resulting emission output. In addition, this dissertation discusses a number of important issues for a cloud computing-based software system implementation. A prototype of a reliability-based traveler information provision and dissemination system is developed to offer a rich set of travel reliability information for the general public and traffic management and planning organizations

New England Overview: A Guide to Large-Scale Energy Infrastructure Issues in 2015

The report outlines how regional electricity and natural gas infrastructure decisions are made. It examines the current proposals to expand electricity transmission lines and natural gas pipelines into New England, as solutions to electricity and gas price and reliability issues, and briefly discusses the major implications of both

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Air Force Institute of Technology Research Report 2012

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Fair Resource Allocation in Macroscopic Evacuation Planning Using Mathematical Programming: Modeling and Optimization

Evacuation is essential in the case of natural and manmade disasters such as hurricanes, nuclear disasters, fire accidents, and terrorism epidemics. Random evacuation plans can increase risks and incur more losses. Hence, numerous simulation and mathematical programming models have been developed over the past few decades to help transportation planners make decisions to reduce costs and protect lives. However, the dynamic transportation process is inherently complex. Thus, modeling this process can be challenging and computationally demanding. The objective of this dissertation is to build a balanced model that reflects the realism of the dynamic transportation process and still be computationally tractable to be implemented in reality by the decision-makers. On the other hand, the users of the transportation network require reasonable travel time within the network to reach their destinations. This dissertation introduces a novel framework in the fields of fairness in network optimization and evacuation to provide better insight into the evacuation process and assist with decision making. The user of the transportation network is a critical element in this research. Thus, fairness and efficiency are the two primary objectives addressed in the work by considering the limited capacity of roads of the transportation network. Specifically, an approximation approach to the max-min fairness (MMF) problem is presented that provides lower computational time and high-quality output compared to the original algorithm. In addition, a new algorithm is developed to find the MMF resource allocation output in nonconvex structure problems. MMF is the fairness policy used in this research since it considers fairness and efficiency and gives priority to fairness. In addition, a new dynamic evacuation modeling approach is introduced that is capable of reporting more information about the evacuees compared to the conventional evacuation models such as their travel time, evacuation time, and departure time. Thus, the contribution of this dissertation is in the two areas of fairness and evacuation. The first part of the contribution of this dissertation is in the field of fairness. The objective in MMF is to allocate resources fairly among multiple demands given limited resources while utilizing the resources for higher efficiency. Fairness and efficiency are contradicting objectives, so they are translated into a bi-objective mathematical programming model and solved using the ϵ-constraint method, introduced by Vira and Haimes (1983). Although the solution is an approximation to the MMF, the model produces quality solutions, when ϵ is properly selected, in less computational time compared to the progressive-filling algorithm (PFA). In addition, a new algorithm is developed in this research called the θ progressive-filling algorithm that finds the MMF in resource allocation for general problems and works on problems with the nonconvex structure problems. The second part of the contribution is in evacuation modeling. The common dynamic evacuation models lack a piece of essential information for achieving fairness, which is the time each evacuee or group of evacuees spend in the network. Most evacuation models compute the total time for all evacuees to move from the endangered zone to the safe destination. Lack of information about the users of the transportation network is the motivation to develop a new optimization model that reports more information about the users of the network. The model finds the travel time, evacuation time, departure time, and the route selected for each group of evacuees. Given that the travel time function is a non-linear convex function of the traffic volume, the function is linearized through a piecewise linear approximation. The developed model is a mixed-integer linear programming (MILP) model with high complexity. Hence, the model is not capable of solving large scale problems. The complexity of the model was reduced by introducing a linear programming (LP) version of the full model. The complexity is significantly reduced while maintaining the exact output. In addition, the new θ-progressive-filling algorithm was implemented on the evacuation model to find a fair and efficient evacuation plan. The algorithm is also used to identify the optimal routes in the transportation network. Moreover, the robustness of the evacuation model was tested against demand uncertainty to observe the model behavior when the demand is uncertain. Finally, the robustness of the model is tested when the traffic flow is uncontrolled. In this case, the model's only decision is to distribute the evacuees on routes and has no control over the departure time