Collaborative Last-Mile Delivery with Drones in Rural America

Inland waterway use for freight transport presents significant environmental benefitscompared to other transportation modes, contributing to sustainability, and reduced ecological impact. Despite the United States' well-developed inland waterways system, a significant portion remains underutilized. This proposal aims to address the challenges hindering the full potential of inland waterway freight transport and realize its environmental advantages. The research approach involves a combination of in-depth interviews and surveys to comprehensively explore the barriers to adoption and collaboration strategies that can lead to prioritized investments in infrastructure, enhanced intermodal connectivity, and awareness about the environmental benefits and long-term sustainability of water transportation. The recruitment of participants will be achieved through a two-fold strategy. Firstly, online outreach and professional networks will be leveraged to engage relevant stakeholders, including professionals from shippers, manufacturing, supply chain, and government agencies. Secondly, a snowball sampling and referral approach will be implemented, starting with initial participants who will refer other colleagues and contacts within their networks, thereby expanding the participant pool and accessing diverse perspectives. The expected deliverables include a comprehensive literature review on inland waterway freight transport, identifying trends, innovations, and challenges in the field. A final synthesis report will present the research findings, including methodology, results, and recommendations for policymakers, stakeholders, and industry players. A white paper will summarize the research outcomes in accessible language for the general public, highlighting the environmental benefits and the potential positive impacts of waterway transportation on local communities and the economy.

"The research project will involve the following tasks, each with its corresponding expected deliverables described in the task description: Literature Review: Conduct a comprehensive literature review to explore existingresearch on drone integration in healthcare delivery, optimization frameworks for last-mile delivery, and relevant methodologies. The expected Deliverable includes a literature review report summarizing key findings, research gaps, and potential areas for improvement. Model Development: Formulate the optimization problem for collaborative last-mile delivery with drones, considering uncertainties and operation-specific constraints. Develop a mathematical model to represent the delivery network and incorporate uncertain factors. The expected Deliverable is an optimization formulation including mathematical equations describing the decision variables, objective function, and constraints. Model Implementation: Solve the formulated optimization problem using computer programming and optimization solvers (e.g., Python and Gurobi). Approximation algorithms will be designed and developed as needed. The expected deliverables are the codes for the model and algorithms with detailed descriptions and pseudocodes. Evaluation and Analyses: Generate synthetic data representing realistic scenarios, including environmental conditions, delivery demands, and resource availability. Conduct testing scenarios to assess the effectiveness of the developed model and algorithms, making necessary adjustments and enhancements as required. The expected deliverables are synthetic dataset and results showcasing algorithm performance, efficiency, and effectiveness. Documentation and Dissemination: Prepare comprehensive quarterly technical reports detailing the research methodology, modeling, algorithms, and evaluation results. Also, disseminate research findings to foster future collaborations and engagement with relevant communities. Document any assumptions, limitations, and recommendations for future research. The expected deliverables are technical reports and peer-reviewed journal articles summarizing the research process, methodology, key findings, and recommendations to showcase the researchoutcomes and impact."

"The main goal of this research proposal is to develop an innovative optimization framework that can adeptly manage uncertain input parameters in collaborative last-mile delivery using drones, while also incorporating operation-specific constraints such as staffing shortages. Given the practical considerations at hand, the proposed framework is expected to address a robust optimization with possibly non-linear terms, which necessitates the development of novel solution algorithms and approximation techniques to ensure computationally feasible solutions in real-world scenarios. The initial year of this research will primarily center around the development of a prototype for the mathematical model and solution algorithms. This prototype will serve as a proof of concept and will undergo testing using randomly generated synthetic data. However, the subsequent stages of the research, including further refinement of the model and algorithms, extensive simulation modeling, and the collection of real-world data, empirical analyses, and collaborations with industry practitioners, will be postponed to future research endeavors."

"The proposed research is expected to yield several significant results and deliverables that contribute to the field of healthcare logistics. These expected results and resulting products during the first year of research include: 1. Optimization Framework: the implementation of a practical decision aid tool that incorporates mathematical models and solution algorithms to optimize drone selection, routing, and strategic resource allocation in real-world scenarios. This tool will allow users to input specific delivery scenarios and obtain optimized solutions for drone-based healthcare logistics in rural areas. The decision support tool will facilitate efficient decision-making, enhancing the accessibility and timely delivery of medical supplies. 2. Performance Evaluation and Impact Assessment: randomly generated synthetic data and benchmark instances will be used to evaluate the performance, efficiency, and effectiveness of the developed optimization framework and approximation algorithms. The results will demonstrate the capabilities and benefits of the proposed approach in terms of delivery speed, cost-effectiveness, and resource allocation in rural healthcare logistics."