<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>