An Algorithmic Approach to Online Multi-Facility Location Problems

Markarian, Christine; Kassar, Abdul-Nasser; Yunis, Manal M.

doi:10.5220/0010212200290035

Cited by 3 publications

(2 citation statements)

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“…Motivated by natural real-world scenarios, OFL has been studied in a number of heterogeneous settings, such as commodities in online scenarios [31], [57] and services offered by multiple facilities [58]. Many online optimization problems, such as Steiner problems and other variants of OFL, share similar characteristics, so it would be interesting to define and study them in these settings.…”

Section: Discussion and Open Problemsmentioning

confidence: 99%

“…This bound holds for even a simple line. Castenow et al [57] Markarian et al [58] first defined the Multi-Facility Location in the Online Setting problem (OMFL), in which we are given a collection of clients, a collection of facilities, and a positive integer k. Opening a facility is associated with a facility opening cost that the algorithm pays, and assigning a client to a facility has a price equal to the distance from the client to the facility. The target is to assign every client, instantly, to a minimum k open facilities while keeping the overall assignment and facility costs to a minimum.…”

Section: B: Commoditiesmentioning

confidence: 99%

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Online Algorithmic Study of Facility Location Problems: A Survey

Markarian

2024

IEEE Access

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Facility Location problems are one of the most prominent optimization problems in computer science, operations research, and combinatorial optimization. Their simple yet intrinsic structure has led to their widespread application in diverse fields, such as leasing, clustering, and urban planning. In facility location problems, potential sites for opening facilities, like warehouses, each have an associated opening cost. Client locations, such as stores, require service from a facility, incurring a cost proportional to their distance from them when assigned. Identifying optimal facility locations to minimize total costs associated with opening facilities and client assignment is the aim. This paper focuses on the online setting, in which client locations are revealed to the algorithm incrementally, unlike the classical offline setting, in which all client locations are given in advance. In this dynamic context, often referred to as the online algorithm scenario, immediate decisions are made to determine whether the newly disclosed client should be assigned to an existing facility or necessitate the establishment of a new facility. To gauge the effectiveness of online algorithms, the concept of competitive analysis is employed. This approach assesses the worst-case performance of online algorithms by comparing them to an optimal algorithm equipped with complete knowledge of forthcoming input sequences. Despite the considerable significance of the Facility Location problem and the substantial body of literature on it within the online algorithm framework, this article represents the first comprehensive summary of these research findings. It explores the algorithmic techniques employed, delves into practical applications, and identifies pertinent research gaps within this domain.

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Section: Discussion and Open Problemsmentioning

confidence: 99%

Section: B: Commoditiesmentioning

confidence: 99%