<p> Nitrogen-based fertilizer industry in United States is undergoing major changes the demand for which is primarily driven by agriculture. Traditionally, this industry sources anhydrous ammonia through imports from Canada and U.S.-Gulf, the latter comprises bulk of imports, or produces domestically to be supplied as is or converted into urea or UAN variations of nitrogen-based fertilizer with various combinations with other minerals. </p><p> With change in composition of crops and increasing acreage of crops that are fertilizer intensive, there is an increased demand for nitrogen-based fertilizer in order to promote foliar growth as a standalone form, for example Urea, or in combination, for example Di-ammonium phosphate (DAP). Second compelling reason for change in industry is reduction in prices of natural-gas, in part due to oil exploration, that makes it cheaper to produce anhydrous ammonia domestically. Anhydrous ammonia is perquisite for making other types of nitrogen-based fertilizer and highly energy intensive. Thus, lower natural-gas prices provide incentive for domestic firms to either expand existing fertilizer plants or opens up the possibility of new entrants. Many companies/firms have recently announced their plans to expand existing plants or open new units, exerting competitive pressure on an industry that already has lot of surplus capacity but highly competitive in terms of production costs and technology used. It is to be noted that natural-gas prices are volatile; therefore, any commitment to expand or open new plant is subject to volatility in demand, natural-gas prices, and import price of fertilizers. </p><p> The purpose of this dissertation is to analyze spatial competition among U.S. nitrogen-based fertilizer plants and their respective market boundaries. This dissertation also derives the structure of the supply chain for nitrogen-based fertilizer in the United States (at macro level); and the stochastic spatial-optimization model to account for risk in random variables. Locational information is used to account for spatial nature of problem, and linear and mixed-integer based optimization techniques are applied to arrive at current and most likely future cases. </p><p> Combination of linear optimization, and mixed-integer, and geographical information systems helps in determining regional areas where competition is expected to be ruinous and most intense; and provide insights on viability of newly announced fertilizer plants that are most likely to be successful and significantly impact the structure of overall supply chain. </p>

<p> This dissertation presents a methodology for the joint optimization of a variety of pavement construction and management activities for segment and system problems under multiple budget constraints. The objective of pavement management is to minimize the total discounted life time costs for the agency and the highway users by finding optimal policies. The scope of the dissertation is focused on continuous time and continuous state formulations of pavement condition. We use a history-dependent pavement deterioration model to account for the influence of history on the deterioration rate. </p><p> Three topics, representing different aspects of the problem are covered in the dissertation. In the first part, the subject is the joint optimization of pavement design, maintenance and rehabilitation (M&R;) strategies for the segment-level problem. A combination of analytical and numerical tools is proposed to solve the problem. In the second part of the dissertation, we present a methodology for the joint optimization of pavement maintenance, rehabilitation and reconstruction (MR&R;) activities for the segment-level problem. The majority of existing Pavement Management Systems (PMS) do not optimize reconstruction jointly with maintenance and rehabilitation policies. We show that not accounting for reconstruction in maintenance and rehabilitation planning results in suboptimal policies for pavements undergoing cumulative damage in the underlying layers (base, sub-base or subgrade). We propose dynamic programming solutions using an augmented state which includes current surface condition and age. In the third part, we propose a methodology for the joint optimization of rehabilitation and reconstruction activities for heterogeneous pavement systems under multiple budget constraints. Within a bottom-up solution approach, Genetic Algorithm (GA) is adopted. The complexity of the algorithm is polynomial in the size of the system and the policy-related parameters. </p>

Thesis (Ph. D.)--Carleton University, 1988. / Also available in electronic format on the Internet.

<p> The recent emergence of social media and online retailing become increasingly important and continue to grow. More and more people use social media to share their real life to the digital world, at the same time, browse the virtual Internet to buy the real products. In the process, a huge amount of data is generated and we investigate the data and crowdsourcing for areas of the public transportation and last-mile delivery for online orders in the perspective of data analytics and operations optimization. </p><p> We first focus on the transit flow prediction by crowdsourced social media data. Subway flow prediction under event occurrences is a very challenging task in transit system management. To tackle this challenge, we leverage the power of social media data to extract features from crowdsourced content to gather the public travel willingness. We propose a parametric and convex optimization-based approach to combine the least squares of linear regression and the prediction results of the seasonal autoregressive integrated moving average model to accurately predict the NYC subway flow under sporting events. </p><p> The second part of the thesis focuses on the last-mile same-day delivery with store fulfillment problem (SDD-SFP) using real-world data from a national retailer. We propose that retailers can take advantage of their physical local stores to ful?ll nearby online orders in a direct-to-consumer fashion during the same day that order placed. Optimization models and solution algorithms are developed to determine store selections, fleet-sizing for transportation, and inventory in terms of supply chain seasonal planning. In order to solve large-scale SDD-SFP with real-world datasets, we create an accelerated Benders decomposition approach that integrates the outer search tree and local branching based on mixed-integer programming and develops optimization-based algorithms for initial lifting constraints. </p><p> In the last part of the dissertation, we drill down SDD-SFP from supply chain planning to supply chain operation level. The aim is to create an optimal exact order ful?llment plan to specify how to deliver each received customer order. We adopt crowdsourced shipping, which utilizes the extra capacity of the vehicles from private drivers to execute delivery jobs on trips, as delivery options, and define the problem as same-day delivery with crowdshipping and store fulfillment (SDD-CSF). we develop a set of exact solution approaches for order fulfillment in form of rolling horizon framework. It repeatedly solves a series of order assignment and delivery plan problem following the timeline in order to construct an optimal fulfillment plan from local stores. Results from numerical experiments derived from real sale data of a retailer along with algorithmic computational results are presented. </p><p>

<p>Given the complexity of transportation systems, generating optimal routing decisions is a critical issue. This thesis focuses on how routing decisions can be computed by considering the distribution of travel time and associated risks. More specifically, the routing decision process is modeled in a way that explicitly considers the dependence between the travel times of different links and the risks associated with the volatility of travel time. Furthermore, the computation of this volatility allows for the development of the travel time derivative, which is a financial derivative based on travel time. It serves as a value or congestion pricing scheme based not only on the level of congestion but also its uncertainties. In addition to the introduction (Chapter 1), the literature review (Chapter 2), and the conclusion (Chapter 6), the thesis consists of two major parts: </p><p> In part one (Chapters 3 and 4), the travel time distribution for transportation links and paths, conditioned on the latest observations, is estimated to enable routing decisions based on risk. Chapter 3 sets up the basic decision framework by modeling the dependent structure between the travel time distributions for nearby links using the copula method. In Chapter 4, the framework is generalized to estimate the travel time distribution for a given path using Gaussian copula mixture models (GCMM). To explore the data from fundamental traffic conditions, a scenario-based GCMM is studied. A distribution of the path scenario representing path traffic status is first defined; then, the dependent structure between constructing links in the path is modeled as a Gaussian copula for each path scenario and the scenario-wise path travel time distribution is obtained based on this copula. The final estimates are calculated by integrating the scenario-wise path travel time distributions over the distribution of the path scenario. In a discrete setting, it is a weighted sum of these conditional travel time distributions. Different estimation methods are employed based on whether or not the path scenarios are observable: An explicit two-step maximum likelihood method is used for the GCMM based on observable path scenarios; for GCMM based on unobservable path scenarios, extended Expectation Maximum algorithms are designed to estimate the model parameters, which introduces innovative copula-based machine learning methods. </p><p> In part two (Chapter 5), travel time derivatives are introduced as financial derivatives based on road travel times&mdash;a non-tradable underlying asset. This is proposed as a more fundamental approach to value pricing. The chapter addresses (a) the motivation for introducing such derivatives (that is, the demand for hedging), (b) the potential market, and (c) the product design and pricing schemes. Pricing schemes are designed based on the travel time data captured by real time sensors, which are modeled as Ornstein-Uhlenbeck processes and more generally, continuous time auto regression moving average (CARMA) models. The risk neutral pricing principle is used to generate the derivative price, with reasonably designed procedures to identify the market value of risk. </p>

The transportation system is particularly vulnerable to disruptive events, while at the same time it is the primary sector for preparedness management and mitigation. The objective of this research is to quantify the changes in vehicle movement during non-recurrent events (Hurricane Irma 2017, Hurricane Michael 2018, and the COVID-19 pandemic in 2020) by comparing with recurrent period for different categories of vehicles, with an emphasis on freight vehicles. This research sought to identify where and when different classes of vehicles were traveling leading up to hurricane landfall and post-storm re-entry. Moreover, this study aims to understand the impact of the pandemic based on different decision made by government and how this decision was affected by the changes in the daily number of cases. The most significant findings showed that the transportation system is very exposed to disruptive events and needs considerable time to recover and adapt. In addition, it was found that freight vehicle transport experience significant changes after the evacuation and the last phases of the pandemic. The less impacted vehicles are those who belong to vehicle category 9 . This category did not have many days with significant changes. On the other hand, the most affected categories were vehicles in category 5 for evacuations and vehicles in categories 5 and 8 for the pandemic. These findings indicate the vehicle category is a parameter that should be taken into consideration in various emergency event management. The guidance of each vehicle group should have a unique design in order to increase management success by the competent authorities. / Includes bibliography. / Thesis (MS)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

Many potentially useful applications that broadly fall under the umbrella of payload transportation operations have been proposed for aerial towed-cable (ATC) systems, namely the precise capture and delivery of payloads. There remain outstanding issues concerning the dynamics and control of ATC systems that are inhibiting the near-term demonstration of these applications. The development of simplified representations of ATC systems that retain the important dynamics, yet are simple enough for use in control system development is limited. Likewise, little research exists into the development of controllers for ATC systems, especially the development of towing strategies and cable-based control techniques for rendezvous and payload transportation. Thus, this thesis presents novel research into the development of control strategies and simulation facilities that redress these two major anomalies, thereby overcoming a number of hitherto unresolved issues. The primary objective of this thesis is to develop innovative non-linear optimal control systems to manoeuvre a cable towed beneath an aircraft to transport payloads both to and from surface locations. To appropriately satisfy this objective, accurate and efficient modelling capabilities are proposed, yielding the equations of motion for numerous models of the ATC system. A series of techniques for improving the representativeness of simple dynamical models were developed. The benefits of using these procedures were shown to be significant and possible without undue complexity or computational expense. Use of such techniques result in accurate simulations and allow representative control systems to be designed. A series of single and multi-phase non-linear optimal control problems for ATC systems are then formally proposed, which were converted into non-linear programming problems using direct transcription for expedient solution. The possibility of achieving accurate, numerous instantaneous rendezvous of the cable tip with desired surface locations on the ground, in two and three-dimensions, is successfully demonstrated. This was achieved through the use of deployment and retrieval control of the cable and/or aircraft manoeuvring. The capability of the system to safely and accurately transport payloads to and from the surface via control of the cable and/or aircraft manoeuvring is also established. A series of parametric studies were conducted to establish the impact that various parameters have on the ability of the system to perform various rendezvous and payload transportation operations. This allowed important insights into to the nature of the system to be examined. In order for the system to perform rendezvous and payload transportation operations in the presence of wind gusts, a number of simple closed loop optimal feedback controllers were developed. These feedback controllers are based on the linear quadratic regulator control methodology. A preliminary indication of the robustness of ATC systems to wind gusts is provided for through a succession of parametric investigations. The performance of the closed-loop system demonstrates that precise and robust control of the ATC system can be achieved for a wide variety of operating conditions. The research presented in this thesis will provide a solid foundation for further advancing the development of aerial tether payload transportation technology.

O ônibus ainda é o modo de transporte público mais usado nas cidades brasileiras, mesmo aquelas que contam com sistemas sobre trilhos extensos. Já centenários, os sistemas de ônibus urbanos ainda se debatem na peleja diária da competição com os demais veículos e na busca de uma imagem mais favorável perante a seus usuários e à população. O advento do ferramental de Sistemas Inteligentes de Transporte (ITS) ofereceu a oportunidade de que esses sistemas pudessem contar com instrumentos de Planejamento, Programação, Monitoração e Controle Operacional próximos àqueles que os sistemas metroferroviários já dispõem há muitas décadas. Entre os vários avanços proporcionados pelo emprego de ITS no campo operacional, destacam-se as intervenções operacionais em tempo real, isto é, a aplicação de medidas que buscam oferecer ou restabelecer a regularidade e a eficiência da operação dos ônibus urbanos frente a contingências que eles enfrentam diariamente. Este trabalho objetiva fazer uma resenha das diversas opções de intervenções estudadas no campo acadêmico e executar simulações de três modalidades dessas estratégias. Foi construída uma rede reproduzindo um trecho de Corredor de ônibus na cidade de São Paulo e simulada a operação de uma linha de alta demanda, com base nos dados reais da operação, do trânsito (semáforos) e velocidades de percurso dos ônibus. A simulação, por sua vez, permitiu apurar os benefícios propiciados pelas intervenções testadas, chegando aos ganhos de tempos nas viagens e o possível aumento de oferta de veículos sem aumento da frota. Além de comparar os resultados entre os tipos de intervenção, foi verificada a sensibilidade dos resultados de cada estratégia a mudanças na intensidade de sua aplicação.Os resultados enquadram-se na faixa dos resultados obtidos nos diversos experimentos acadêmicos analisados. Dentro das expectativas, a intervenção das Meias Viagens (\"deadheading\") apresentou melhores resultados nos ganhos de tempos de viagem variando entre 8,5% e 12,9%, secundados pelas Paradas Limitadas (\"skip-stop\") variando entre 2,9% e 4,7%, enquanto a Linha Expressa apresentou resultados menores, motivados, principalmente, pelas limitações da extensão do trecho analisado. / Buses are still the most common transit mode in Brazilian cities even in those that count with extensive rail systems. Century old city bus operations still struggle daily to compete with other road vehicles while trying to gain a better image before its users and the general public. The arrival of Intelligent Transportation System (ITS) solutions brought to these systems the opportunity of counting on Operational Planning, Programing, Monitoring and Control like those already used by rail transit for decades. Among several improvements possible using ITS real time intervention during operation stands out. This enables the application of measures to offer or restore regularity and efficiency of bus services in face of contingencies faced daily. This paper aims to review different options of intervention reported in academic publications and to perform simulations of three of these strategies. A network was modeled duplicating a real Sao Paulo bus corridor section on which a high demand bus line operation, based on actual operation data, traffic, traffic lights and bus speeds, was studied. The simulation, in turn, allowed us to assess the benefits of each alternative intervention tested by determining travel time gains and the increase in bus service supply without increasing the operating fleet. Besides comparing results between intervention types the sensitivity of each one\'s results to strategy application intensity was also reached. The final experiments results fall within the range of those obtained in several other academic studies analyzed. Within the expectations, the deadheading intervention presented better results in travel time gains ranging from 8.5% to 12.9%, followed by the skip-stop, ranging from 2, 9% and 4.7%, while the Express Line presented lower results motivated, mainly, by the limitations of the extension of the analyzed section

O planejamento operacional de um sistema de transporte de longa distância implica resolver um problema de otimização de rede dinâmica, visando a efetuar, de forma eficaz e eficiente, o atendimento às demandas de cargas, utilizando a capacidade de transporte disponível. A metodologia de solução proposta utiliza a abordagem de Rede de Filas Logísticas, a qual substitui o processo de otimização global da rede (usualmente utilizando Programação Linear Inteira) por um modelo de Programação Dinâmica Estocástica, Aproximada e Adaptativa, que permite a resolução de uma série de subproblemas delimitados no tempo, reduzindo sensivelmente a quantidade de variáveis envolvidas. Este método permite a utilização de modelos matemáticos mais realistas em horizontes de planejamento mais amplos. O presente trabalho estende os modelos encontrados na Literatura, aplicando o método a problemas de maior complexidade, incluindo a consideração de frotas heterogêneas de veículos, janelas de início de atendimento, utilização de terceiros transportadores e penalidades pelo não atendimento das demandas. São apresentados exemplos de problemas experimentais submetidos com sucesso à técnica desenvolvida. O trabalho inclui ainda o delineamento de um Sistema de Apoio à Decisão incorporando a metodologia proposta. / Operational planning of a long haul transportation system implies to solve a dynamic network optimization problem, aiming to perform the freight movements in an efficient and effective way, while utilizing the available transportation capacity. The proposed solution methodology utilizes the Logistic Queueing Network approach, replacing the network global optimization process through Integer Linear Programming by a model of Stochastic, Approximate and Adaptive Dynamic Programming, which allows the resolution of a sequence of sub- problems delimited in time, strongly reducing the quantity of variables involved. This method allows the utilization of more realistic mathematical models in a broader planning horizon. The research extends models found in the literature to solve more complex problems, including the consideration of heterogeneous fleet of vehicles, time windows, third party vehicles and penalties for not attendance of demands. Experimental problems solved successfully with the developed technique are presented. The work also presents the delineation of a Decision Support System incorporating the proposed methodology.

O planejamento operacional de um sistema de transporte de longa distância implica resolver um problema de otimização de rede dinâmica, visando a efetuar, de forma eficaz e eficiente, o atendimento às demandas de cargas, utilizando a capacidade de transporte disponível. A metodologia de solução proposta utiliza a abordagem de Rede de Filas Logísticas, a qual substitui o processo de otimização global da rede (usualmente utilizando Programação Linear Inteira) por um modelo de Programação Dinâmica Estocástica, Aproximada e Adaptativa, que permite a resolução de uma série de subproblemas delimitados no tempo, reduzindo sensivelmente a quantidade de variáveis envolvidas. Este método permite a utilização de modelos matemáticos mais realistas em horizontes de planejamento mais amplos. O presente trabalho estende os modelos encontrados na Literatura, aplicando o método a problemas de maior complexidade, incluindo a consideração de frotas heterogêneas de veículos, janelas de início de atendimento, utilização de terceiros transportadores e penalidades pelo não atendimento das demandas. São apresentados exemplos de problemas experimentais submetidos com sucesso à técnica desenvolvida. O trabalho inclui ainda o delineamento de um Sistema de Apoio à Decisão incorporando a metodologia proposta. / Operational planning of a long haul transportation system implies to solve a dynamic network optimization problem, aiming to perform the freight movements in an efficient and effective way, while utilizing the available transportation capacity. The proposed solution methodology utilizes the Logistic Queueing Network approach, replacing the network global optimization process through Integer Linear Programming by a model of Stochastic, Approximate and Adaptive Dynamic Programming, which allows the resolution of a sequence of sub- problems delimited in time, strongly reducing the quantity of variables involved. This method allows the utilization of more realistic mathematical models in a broader planning horizon. The research extends models found in the literature to solve more complex problems, including the consideration of heterogeneous fleet of vehicles, time windows, third party vehicles and penalties for not attendance of demands. Experimental problems solved successfully with the developed technique are presented. The work also presents the delineation of a Decision Support System incorporating the proposed methodology.