Collaborative Research: Revenue Management for Port Operations and other Multistage Service Systems

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

This award will contribute to the Nation's prosperity and welfare by providing rigorous revenue management solutions to improve the competitiveness of multistage service systems. Such systems are challenging to analyze due to the interactions between different stages of operations and the lack of system status transparency to customers. The focus of this project is on revenue management for ports, a problem that has attracted limited attention despite the importance of ports to the economy. This project will develop modern revenue management strategies for port terminal operations that involve both berthing and loading/unloading stages and more generally, will examine pricing policies for multistage service systems. The results of this project will advance the modernization of operations in the port industry and promote the digitalization of the industry. This award will support the involvement of graduate students in advancing the research agenda. This project will develop static and dynamic pricing policies that maximize the long-run average profit of multistage service systems with customers that are price and congestion sensitive. The project considers both observable and unobservable service systems depending on what information is available to customers. The project will provide tools for the service provider to decide what information to provide to potential customers and to assess the benefits of dynamic pricing relative to simpler static pricing. The project will involve the development of exact and approximate algorithms for determining optimal policies that practitioners can use to solve a broad class of real-world problems. The project will use a case study on port terminal operations that will be used to validate the methods and which terminal operators can use to build their own models and algorithms to obtain optimal pricing strategies. The analytical results and numerical algorithms involve Markov decision processes, sample-path arguments, stochastic comparisons, matrix-analytic methods, changes of variables, and linear programming. 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.