SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted ANALYSIS OF CONTAINERIZED GRAIN, DRIED DISTILLERS’ GRAINS, AND OTHER FEED COMMODITIES EXPORTS AND INLAND MOVEMENT(North Dakota State University, 2025-05) Helmi, Wesam; Mattson, Jeremy; Vachal, KimberlyContainerized shipping has become an increasingly important method for transporting bulky products. This research investigates different topic areas associated with containerized grain, Dried Distillated Grain, and other feed (DDG&oF) shipping. It covers both inland movement and exports from the United States. The first part of the research provides a descriptive analysis of U.S. containerized DDG&oF shipping trends, using the PIERS database. This part examines variations by origin, port of departure, commodity type, and destination country. The results are visualized using charts and spatial maps and show the amount of shipped grains, DDG&oF in containers versus bulk shipments. In addition, it shows when the containerized grain started declining, in which commodities, and for which exporting ports. This part of the research aims to provide an understanding of shifts that occurred in recent years. The second part of the research is a prescriptive analysis using a Linear Programming transshipment model to optimize the inland transportation of containerized grain. Using data from the Surface Transportation Board and Freight Analysis Framework, the optimization model minimizes total logistics costs by considering transportation, operational, and freight expenses in both rail and highway modes. It also evaluates potential new inland terminal locations and uses sensitivity analysis to test the robustness of the model under varying conditions. Third, the research explores the mode of choice between container vessels and bulk carriers for exporting DDG and other feed (DDG&oF), a major U.S. agricultural export. A Beta regression model is applied to PIERS data, focusing on shipments to top destination countries. This analysis identifies the most influential key factors that affect transportation mode decisions, such as shipping prices, exchange rate, Index of Industrial Production, and more. The findings address gaps in current literature regarding DDG&oF modal choice and contribute new insights into optimizing international agricultural logistics. In addition, the study applied multicollinearity analysis to ensure the correlation of independent variables. Together, studies provide a comprehensive analysis of containerized grain export trends, logistics optimization strategies, and shipper decision-making in modal selection. The research offers practical implications for improving cost-efficiency, network design, and policy considerations in the evolving landscape of U.S. agricultural exports.13 0Item Restricted Computational intelligence approaches applied to various domains(Saudi Digital Library, 2023-03-04) Alibrahim, Hussain; Ludwig, SimoneOver the past decade, machine learning has revolutionized a wide range of fields, from self-driving cars to speech recognition, web search, and even the human genome. However, the success of machine learning algorithms depends on a thorough understanding of the problem, mechanisms, properties, and constraints. This dissertation explores various aspects of machine learning, including hyperparameter optimization, nature-inspired algorithms for semi-supervised learning, image encryption using Particle Swarm Optimization with a logistic map and image originality. In the first chapter, three models - Genetic Algorithm, Grid Search, and Bayesian Optimization - are compared to improve classification accuracy for neural network models. The objective is to build a neural network model with a set of hyperparameters that can improve classification accuracy for data mining tasks, which aim to discover hidden relationships between input and output data to predict accurate outcomes for new data. The second chapter focuses on using nature-inspired algorithms, such as Particle Swarm Optimization (PSO) and Anti Bee Colony (ABC), to correctly cluster unlabelled data in semi-supervised learning problems. Two hybrid versions of K-means clustering, one with PSO and the other with ABC, are developed. The third chapter uses PSO to develop an image encryption algorithm using the logistic map to aid in the encryption process. The optimization problem is formulated by converting the image encryption problem into an optimization problem. In the final chapter, a new algorithm is developed using different techniques such as classification, optimization, and image analysis to detect whether an image is original or has been edited and modified. Overall, this dissertation investigates a variety of machine learning techniques and their practical applications across numerous fields. The techniques have the potential to be applied in diverse areas, such as biology, meteorology, healthcare, and finance.19 0Item Restricted Timing and Marketing Mix Decisions Under New Product Diffusion With Dual-Market Structure and Repeat Purchases(2022) Alenzy, Muhammad; Erkoc, MuratOver the last three decades, the success rate for new products in the marketplace has been one in ten. Although this rate has been increasing slightly in recent years, it is still below 30% as of the end of 2021. The lack of a significant market to adopt the product is considered the leading cause of the failure. It has been established that newly introduced products in the marketplace encounter early adopters before they are accepted by main adopters, the larger market. The time-to-market to introduce the new product to the main adopters emerges as a pivotal decision to achieve higher demand and traction levels, as they represent a larger population with respect to the early market size. Previous research reports that the transition from the early market to the main market is challenging due to the heterogeneity in the adoption attributes of the two segments, which could lead to sales slowdowns if not foreseen and planned previously. Although product managers leverage pricing and advertising to help their products cross the slowdown and successfully diffuse to the main market, price declines and market penetration could negatively influence this transitional process. Consequently, several trade-offs arise when planning a new product introduction in a dual-market structure, given the two markets' heterogeneity. For instance, delaying the time to enter the main market could enable the firm to increase prices when selling the product solely to the early market, given their lower price sensitivity; however, it would decrease the early market size. And decreasing the size of the early market could decrease their word-of-mouth influence on the main market to adopt the new product. Hence, the advertisement spending level in the main market increases, and accordingly, profit margins decrease. Further complications arise with the existence of repeat purchases, for example, service-based products, as most of the literature body on new product diffusion, especially the dual-market diffusion, assumes a single purchase transaction from both markets’ adopters. Under such settings, it is commonly accepted that customers who subscribe to the service every period will end up repeating the subscription or canceling, which is known as the churn rate. Churn rates would also be affected by the heterogeneity in the dual-market structure where early market churn rate would vary from the main market’s. Customer churn has attracted significant attention from researchers and managers in recent years after the rise of service-based firms, as they form about 80% of the US gross domestic product and demonstrate the relationship between a service firm’s customer churn rate and its long-term profits. We investigate the underlying trade-offs when planning a new product introduction with a dual-market structure and repeat purchases which, to the best of our knowledge, no previous work in either the marketing or the operations management literature has analyzed the profitability of a product under the combined effect of such conditions and underlying trade-offs. We contribute to the current literature by jointly optimizing the time-to-main-market, pricing, and advertisement spending across the life cycle of newly introduced products under these conditions. We introduce an integer nonlinear programming model that optimizes these critical decisions simultaneously to maximize total profit across the product's life cycle. The model optimizes the decisions in two outcomes: (1) when the product is introduced solely to the early market and (2) when both markets coexist and are introduced to the product at the same time. The demand mapping is built by extending the Bass Diffusion framework to the dual-market structure and repeat purchases. We conduct extensive comparative computations with multiple periods and multi-level parametric combinations and reveal that delaying the time to enter the main market is a persistent optimal timing strategy that maximizes the profit function in various parametric settings. Additionally, the communication level between the two markets notably impacted different performance metrics when investigated independently and under the interplay effect with other model parameters.21 0