Arriving on Time
Y. Y. Fan,
R. E. Kalaba and
J. E. Moore
Additional contact information
Y. Y. Fan: University of California
R. E. Kalaba: University of Southern California
J. E. Moore: University of Southern California
Journal of Optimization Theory and Applications, 2005, vol. 127, issue 3, No 5, 497-513
Abstract:
Abstract This research proposes a procedure for identifying dynamic routing policies in stochastic transportation networks. It addresses the problem of maximizing the probability of arriving on time. Given a current location (node), the goal is to identify the next node to visit so that the probability of arriving at the destination by time t or sooner is maximized, given the probability density functions for the link travel times. The Bellman principle of optimality is applied to formulate the mathematical model of this problem. The unknown functions describing the maximum probability of arriving on time are estimated accurately for a few sample networks by using the Picard method of successive approximations. The maximum probabilities can be evaluated without enumerating the network paths. The Laplace transform and its numerical inversion are introduced to reduce the computational cost of evaluating the convolution integrals that result from the successive approximation procedure.
Keywords: Optimal routing; stochastic shortest path problems; dynamic programming; convolution integrals (search for similar items in EconPapers)
Date: 2005
References: Add references at CitEc
Citations: View citations in EconPapers (21)
Downloads: (external link)
http://link.springer.com/10.1007/s10957-005-7498-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:joptap:v:127:y:2005:i:3:d:10.1007_s10957-005-7498-5
Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2
DOI: 10.1007/s10957-005-7498-5
Access Statistics for this article
Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull
More articles in Journal of Optimization Theory and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().