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An Online Variant of Set Cover Inspired by Happy Clients

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Innovative Intelligent Industrial Production and Logistics (IN4PL 2020, IN4PL 2021)

Abstract

Over the past decades, the concept of leasing has been widely spread in many industries. Companies have been leasing their resources rather than buying them. Small businesses were now able to join the markets without the need for upfront costs. Companies were now able to get access to the most up-to-date equipments with significantly lower costs. Despite its popularity, leasing was first introduced as a theoretical model in 2005. Following this model, many well-known optimization problems were studied in the leasing setting. This paper is devoted to continuing the algorithmic study on leasing in the context of the well-known Set Cover problem (SC). SC is one of the most well-studied NP-hard optimization problems in combinatorics, operations research, and computer science, appearing as sub-problems in many real-world applications. In this paper, we conduct a study from an online algorithmic perspective. That is, unlike the classical offline setting in which the entire input sequence is given to the algorithm all at once, the input sequence is given to the online algorithm in portions over time. In particular, we study an online leasing variant of Set Cover, known as the Online Set Cover Leasing with Happiness Costs problem (Leasing OSC-HC). We design the first online algorithm for Leasing OSC-HC and analyze its performance in the competitive analysis framework in which the online algorithm is evaluated against the optimal offline solution over all possible instances of a given problem.

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Markarian, C. (2023). An Online Variant of Set Cover Inspired by Happy Clients. In: Smirnov, A., Panetto, H., Madani, K. (eds) Innovative Intelligent Industrial Production and Logistics. IN4PL IN4PL 2020 2021. Communications in Computer and Information Science, vol 1855. Springer, Cham. https://doi.org/10.1007/978-3-031-37228-5_8

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  • DOI: https://doi.org/10.1007/978-3-031-37228-5_8

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