Food Waste Management Networks: Novel Methods for Overcoming Emerging Logistics Challenges

The U.S. produces an estimated 63 million tons of food waste per year. Interest from state and local governments in diverting unused food from landfills to alternative treatment facilities is growing. However, this emerging food waste (FW) stream will face logistics challenges as diversion networks expand. Current methods for evaluating challenges are insufficient for providing solutions for network development because they do not explore the impacts of variability in the food waste management system. This dissertation aims to fill this knowledge gap by exploring three key research areas. First, variability in FW generation from different types of commercial generators is characterized. Empirically collected data is combined with the prevailing FW estimation method to characterize how generator attributes, temporal variability, and spatial heterogeneity in FW generation could impact development of diversion networks. Results show that representing FW generation from commercial sources in New York State with a single annual value is likely inadequate for policy and planning purposes due to the uncertainty surrounding anticipated FW generation. Second, two transportation models are presented to understand how variability in spatial locations and generation rates affects FW collection. Results indicate that in residential systems with uniform generation rates, increasing spatial density of participants is critical to reducing service costs. In commercial systems, the inherent heterogeneity of food waste generation rates is important to reduce costs for initial collection services. Finally, material inputs and digestate management are incorporated into a FW treatment facility siting method. Results show that digestate transportation distance is critical for ensuring that land application of digestate does not overload nearby farm fields with phosphorus. This dissertation contributes to the body of scientific knowledge for waste management through the creation of novel, generalizable methods that investigate the impacts of variability on logistics decisions to inform development of effective food waste management networks

A Pattern Analysis of Daily Electric Vehicle Charging Profiles: Operational Efficiency and Environmental Impacts

Plug-in Electric Vehicles (PEVs) are considered one solution to reducing GHG emissions from private transport. Additionally, PEV adopters often have free access to public charging facilities. Through a pattern analysis, this study identifies five distinct clusters of daily PEV charging profiles observed at the public charging stations. Empirically observed patterns indicate a significant amount of operational inefficiency, where 54% of the total parking duration PEVs do not consume electricity, preventing other users from charging. This study identifies the opportunity cost in terms of GHG emissions savings if gasoline vehicles are replaced with potential PEV adopters. The time spent in parking without charging by current PEV users can be used by these potential PEV users to charge their PEVs and replace the use of gasoline. The results suggest that reducing inefficient station use leads to significant reductions in emissions. Overall, there is significant variability in outcomes depending on the specific cluster membership