Author:
Christine Markarian
Affiliation:
Department of Engineering and Information Technology, University of Dubai, Dubai, UAE
Keyword(s):
Online Set Cover, Rating, Optimization, Online Algorithms, Competitive Analysis, Randomized Rounding.
Abstract:
In this paper, we introduce the Online Set Cover With Rated Subsets problem (OSC-RS), a generalization of the well-known Online Set Cover problem, in which we are given a universe of elements and a collection of subsets of the universe, each associated with a subset cost and a rating cost. In each step, the algorithm is given a request containing elements from the universe. The algorithm serves a request by assigning it to a number of purchased subsets that jointly cover the requested elements. The algorithm pays the subset costs associated with the subsets purchased and for each request, it pays the sum of the rating costs associated with the subsets assigned to the request. The aim is to serve all requests as soon as revealed, while minimizing the total subset and rating costs paid. OSC-RS is motivated by intrinsic client-service-providing scenarios in which service providers are rated and their ratings are included in the decision-making process, so as higher-rated service provide
rs are associated with lower rating costs. That is, the decisions about serving clients take into account the quality of the services provided. We propose the first online algorithm for OSC-RS and evaluate it using the standard notion of competitive analysis. The latter compares the performance of the online algorithm to that of an optimal offline algorithm that is assumed to know all the input sequence in advance.
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