Revenue Maximization in Online Dial-A-Ride

Authors Ananya Christman, Christine Chung, Nicholas Jaczko, Marina Milan, Anna Vasilchenko, Scott Westvold

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Ananya Christman
Christine Chung
Nicholas Jaczko
Marina Milan
Anna Vasilchenko
Scott Westvold

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Ananya Christman, Christine Chung, Nicholas Jaczko, Marina Milan, Anna Vasilchenko, and Scott Westvold. Revenue Maximization in Online Dial-A-Ride. In 17th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2017). Open Access Series in Informatics (OASIcs), Volume 59, pp. 1:1-1:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)


We study a variation of the Online-Dial-a-Ride Problem where each request comes with not only a source, destination and release time, but also has an associated revenue. The server's goal is to maximize its total revenue within a given time limit, T. We show that the competitive ratio is unbounded for any deterministic online algorithm for the problem. We then provide a 3-competitive algorithm for the problem in a uniform metric space and a 6-competitive algorithm for the general case of weighted graphs (under reasonable assumptions about the input instance). We conclude with an experimental evaluation of our algorithm in simulated settings inspired by real-world Dial-a-Ride data. Experimental results show that our algorithm performs well when compared to an offline version of the algorithm and a greedy algorithm.
  • online algorithms
  • dial-a-ride
  • competitive analysis
  • vehicle routing
  • metric space


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