An improved approximation algorithm for resource allocation

Authors:
Gruia Calinescu;Amit Chakrabarti;Howard Karloff;Yuval Rabani
Affiliations:
Illinois Institute of Technology, Chicago, IL;Dartmouth College, Hanover, NH;AT&T Labs––Research, NJ;The Hebrew University of Jerusalem, Israel
Venue:
ACM Transactions on Algorithms (TALG)
Year:
2011

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Abstract

We study the problem of finding a most profitable subset of n given tasks, each with a given start and finish time as well as profit and resource requirement, that at no time exceeds the quantity B of available resource. We show that this NP-hard Resource Allocation problem can be (1/2 − ϵ)-approximated in randomized polynomial time, which improves upon earlier approximation results.