Online computation and competitive analysis
Online computation and competitive analysis
On-line single-server dial-a-ride problems
Theoretical Computer Science
Developments from a June 1996 seminar on Online algorithms: the state of the art
Developments from a June 1996 seminar on Online algorithms: the state of the art
Online Dial-a-Ride Problems: Minimizing the Completion Time
STACS '00 Proceedings of the 17th Annual Symposium on Theoretical Aspects of Computer Science
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Online dial-a-ride problem with time-windows under a restricted information model
AAIM'06 Proceedings of the Second international conference on Algorithmic Aspects in Information and Management
On the power of lookahead in on-line vehicle routing problems
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
On-Line algorithms, real time, the virtue of laziness, and the power of clairvoyance
TAMC'06 Proceedings of the Third international conference on Theory and Applications of Models of Computation
On the online dial-a-ride problem with time-windows
AAIM'05 Proceedings of the First international conference on Algorithmic Applications in Management
Noah: a dynamic ridesharing system
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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In on-line dial-a-ride problems, servers are traveling in some metric space to serve requests for rides which are presented over time. Each ride is characterized by two points in the metric space, a source, the starting point of the ride, and a destination, the end point of the ride. Usually it is assumed that at the release of such a request complete information about the ride is known. We diverge from this by assuming that at the release of such a ride only information about the source is given. At visiting the source, the information about the destination will be made available to the servers. For many practical problems, our model is closer to reality. However, we feel that the lack of information is often a choice, rather than inherent to the problem: additional information can be obtained, but this requires investments in information systems. In this paper we give mathematical evidence that for the problem under study it pays to invest.