CAREER: Distribution Resource Elasticity: A New Hierarchical Approach for On-Demand Distribution Platforms

Rensselaer Polytechnic Institute, Troy NY

Investigators

Abstract

This Faculty Early Career Development (CAREER) grant will support research that addresses modern product and service distribution challenges through innovation and education, promoting both the progress of science and advancing national prosperity. The emergence of sharing economies has enabled individuals and small businesses to supplement traditional manufacturing and service markets with on-demand supply. This project focuses on novel methods to coordinate decentralized distribution resources on-demand through customized recommendations made to multiple suppliers simultaneously. Such methods will improve the efficiency of the supply network by tapping into otherwise underutilized or idling supply capacity. By increasing capacity through more flexible use of suppliers, this approach can impact both commercial and non-commercial supply networks, improving e-commerce profitability and enabling a new on-demand volunteer base. Collaborations with community nonprofits and on-demand grocery delivery systems for mobility-restricted clients will provide test cases to validate the developed methods and opportunities for positive societal impact. Undergraduate engineering students, trained in effective communication, will create activities informed by this research to inspire K-12 students to pursue engineering. Undergraduate and graduate students will interact with the research via new curriculum, classes, service learning, and research experiences. This award supports fundamental research in methods to coordinate decentralized suppliers in real-time for on-demand distribution platforms and to quantify the impact of supplier choice on platform efficiency, effectiveness, and equity. New bi-level optimization formulations will capture performance as a function of both the platform's decisions and suppliers' interdependent choices. New optimization models and algorithms will guide platform's interdependent supplier recommendations and categorize responses to outcomes created by supplier choice. Specialized exact approaches will exploit problem structure, while heuristic approaches will generate large-scale solutions quickly. To affect suppliers' selection behaviors, compensation decisions will be considered jointly with recommendation decisions. Iterative techniques will set compensation for rejected requests, determine how supplier performance should influence future platform decisions and utility estimates, when to deploy platform resources, and how to manage dynamically arriving requests and suppliers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.