The relative worst order ratio applied to seat reservation

Authors:
Joan Boyar;Paul Medvedev
Affiliations:
University of Southern Denmark, Odense M, Denmark;University of Toronto, Toronto, Ontario, Canada
Venue:
ACM Transactions on Algorithms (TALG)
Year:
2008

Citing 7
Cited 5

Amortized efficiency of list update and paging rules

Communications of the ACM
Best-fit bin-packing with random order

Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
Concrete Math

Concrete Math
The Accommodating Function: A Generalization of the Competitive Ratio

SIAM Journal on Computing
Tight bounds on the competitive ratio on accommodating sequences for the seat reservation problem

Journal of Scheduling - Special issue: On-line algorithm part I
The relative worst order ratio for online algorithms

ACM Transactions on Algorithms (TALG)
The relative worst-order ratio applied to paging

Journal of Computer and System Sciences

On the relative dominance of paging algorithms

Theoretical Computer Science
Comparing First-Fit and Next-Fit for online edge coloring

Theoretical Computer Science
List factoring and relative worst order analysis

WAOA'10 Proceedings of the 8th international conference on Approximation and online algorithms
Comparing online algorithms for bin packing problems

Journal of Scheduling
A new variable-sized bin packing problem

Journal of Scheduling

Quantified Score

Hi-index	0.00

Visualization

Abstract

The seat reservation problem is the problem of assigning passengers to seats on a train with n seats and k stations enroute in an online manner. The performance of algorithms for this problem is studied using the relative worst order ratio, a fairly new measure for the quality of online algorithms, which allows for direct comparisons between algorithms. This study has yielded new separations between algorithms. For example, for both variants of the problem considered, using the relative worst order ratio, First-Fit and Best-Fit are shown to be better than Worst-Fit.