<p>Online retail sales and grocery/food orders have been breaking records every year. As a result, third-party delivery companies have found an opportunity to get their share from the growing transportation network. Electric vehicles (EVs) are becoming a preferable choice for such large delivery systems due to their environmental benefits. However, EVs have limited-service ranges; therefore, intra-route facilities are needed for EVs to stay operational. These facilities offer charging stations for EVs and storage areas for requests, e.g., food and packages. In this dissertation, we propose a novel <em>Pickup and Delivery Problem</em> (PDP) with EVs and transfers. There are requests to be picked up and delivered. EVs leave their origin depot, serve requests, and return to their destination depot. Unlike the generic PDP, intra-route facilities allow EVs to exchange requests. Thus, a request can be transported by more than one vehicle. In this dissertation, three new problems are introduced, and the following research questions are investigated: 1) "How valuable is to include intra-route facilities and allow transfers in a pickup and delivery network with EVs?", 2) "What is the cost of locating intra-route facilities randomly rather than finding the best locations while creating the routes for EVs?", and 3) "How much can drones improve the delivery speed in a pickup and delivery network with EVs and transfers?". A <em>Mixed-integer Linear Programming</em> (MILP) model and a <em>Simulated Annealing</em> (SA) algorithm are developed and compared with each other to answer the first question. For the second question, a MILP model is formulated; however, due to unreasonable computational runtimes, a SA algorithm and an <em>Adaptive Large Neighborhood Search</em> (ALNS) algorithm are proposed. Finally, a MILP model is developed for the hybrid-fleet problem. The overall results highlight that intra-route facilities shorten the total traveled distance in the PDP network by allowing exchanges and recharging.</p>

<p dir="ltr">Engineering Technology (ET) programs are pivotal in preparing graduates for the demands of the modern workforce, particularly in quality management systems (QMS). This study examines the alignment between QMS knowledge and experience gained by graduates in ABET-accredited ET programs and the expectations of industry. Through a dual-survey approach, targeting both industry leaders and academic educators, the research elucidates current QMS practices in industry, the scope of QMS education, and the resulting preparedness of graduates for professional roles. Findings indicate a discernible gap between industry expectations and current academic offerings in QMS education. While industry professionals rely on established QMS frameworks such as ISO 9001 and Lean Six Sigma, academic programs often limit their coverage to theoretical underpinnings rather than hands-on, practical applications. This discrepancy highlights the need for a more robust, application-oriented curriculum that bridges theoretical knowledge with real-world practice. Recommendations include a call for greater integration of practical QMS training within academic programs and stronger partnerships between academia and industry to foster educational content that aligns with professional QMS applications.</p>

<div>This research analyzes over 60,000 awarded highway contracts from 18 states throughout the United States. Analysis was performed on the state and aggregate level. The contracts were awarded from the years 2008 through 2018. Statistical analysis utilizing Pearson's Correlation and Ordinary Least Squares regression for each sample was performed to identify each variables relationship between the budget and awarded values.</div><div>The research examined effects of economic indicators, contractor descriptors and yearly/seasonal adjustments These variables included DBE Participation Goal, Number of Bidders, Project Dollar Value, Project Duration, Unemployment Rate, S&P 500 Index, Volatility Index, quarter, and year of project award. The results were examined by using a combination of simple statistical summaries and econometric coefficients called a cost vector. <br></div><div>Summary statistics observed Bid Difference at 8.5% below the Engineer's Estimate. The study observed DBE Participation Goals averaged 3.74% of the value of contracts, with an observed average of 4.5 bidders per contract. <br></div><div>The research determined that 55% of observed states had a positive significant correlation with DBE Participation Goal and Bid Difference. This correlation translated to nearly $80 million in additional cost. In addition, the research determined that all 19 groups in this study had a negative significant correlation with the Number of Bidders. The correlation translated to a savings of nearly $500 million. <br></div>