Improved Approximation Guarantees for Lower-Bounded Facility Location

Ahmadian, Sara; Swamy, Chaitanya

doi:10.1007/978-3-642-38016-7_21

Cited by 37 publications

(40 citation statements)

References 17 publications

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“…This follows from the fact that the cable types obey economies of scale. 1 2 ) is a constant and it will be fixed later. Intuitively, b i is the demand at which it gets more economical to use a cable type i + 1 rather than a cable type i.…”

Section: Near-optimum Layered Solutionmentioning

confidence: 99%

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Combinatorial approximation algorithms for buy-at-bulk connected facility location problems

Bley

Rezapour

2016

Discrete Applied Mathematics

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a b s t r a c tWe consider generalizations of the connected facility location problem, where clients connect to open facilities via access trees that are shared by multiple clients. For each edge of these trees, a combination of several cable types can be chosen, according to the demand carried by the edge. The task is to choose open facilities, a Steiner tree among them as a core network, and access trees with appropriate cable types on their edges to connect clients to the open facilities in such a way that the sum of the facility opening costs and cable costs for the core and access networks is minimal. Problems of this type arise widely in the planning of access and distribution networks in telecommunications, logistics, or energy networks.In this paper, we introduce and analyze two fundamental versions of these problems. In the Buy-at-Bulk version of the problem, each access cable type has a fixed setup cost and a fixed capacity, whereas in the Deep-Discount problem version, each cable type has unlimited capacity but a traffic-dependent variable cost in addition to its fixed setup cost. We derive the first constant-factor approximation algorithms for these problems, using different algorithmic and analytical techniques.

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Section: Near-optimum Layered Solutionmentioning

confidence: 99%

“…The factor was improved to 82.6 by Ahmadian and Swamy [1], using a modification of the Svitkina's algorithm and a more careful analysis. We note that the approaches of both papers [20,1] require all lower bounds to be uniform.…”

Section: Introductionmentioning

confidence: 99%

Combinatorial approximation algorithms for buy-at-bulk connected facility location problems

Bley

Rezapour

2016

Discrete Applied Mathematics

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“…We need to jointly choose a subset of the facilities to open and assign each client to an open facility in order to minimize the sum of the facilityopening and the client-assignment costs [1]. Recent works [2]- [6] introduced the concept of demand threshold level that each facility is required to overcome in order to open. The suggested solutions included bicriteria guarantees, heuristic algorithms, linear rounding techniques and branch-and-cut-schemes, as the problem was shown to be NP-hard.…”

Section: Related Workmentioning

confidence: 99%

“…Existing works in the literature have mainly assumed that the revenue of a provider is proportional to it's overall demand. Thus, each open provider is required to serve a certain minimum amount of demand in order to survive [5]- [6]. This assumption has simplified the market problem.…”

Section: Introductionmentioning

confidence: 99%

Surviving in a competitive market of information providers

Poularakis

2013

2013 Proceedings IEEE INFOCOM

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As the processing and transport capacity of the information and communication technologies (ICT) infrastructure increased vastly the last few years, the bottleneck of the information exchange process moved to the end points of the process, i.e. the consumers and the producers of information. On one hand there is the limited time that a consumer has to access the information and on the other hand there is the minimum utility level that a provider needs to provide to the society of consumers to cover it's investment cost. In this paper we present a novel decision model for a set of competing providers that wish to enter a market. It may happen that due to the competition, some competitors will not be able to cover their investment cost and therefore will disappear. We analyze the optimum way of forming the market, in order to maximize the aggregate utility of it. We show that this problem is NP-complete and present a linear programming rounding heuristic algorithm to solve it. Besides, we study a game where every player (provider) is to choose whether to join the market or not. We compute the price of anarchy of the game and present a heuristic algorithm that belongs to the family of best response dynamic algorithms. Systematic experiments on a real world data set have demonstrated the effectiveness of our proposed approach.

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“…A remarkable feature of the reduction is that the roles of facilities and clients are reversed in the CFL instance. The approximation ratio was later improved to 82.6 by Ahmadian and Swamy [2]. Both algorithms require the lower bounds to be uniform, and getting an O(1)-approximation for LBFL with general lower bounds remained an open problem, as discussed in both [27] and [2].…”

Section: Introductionmentioning

confidence: 99%

On Facility Location with General Lower Bounds

Shi

2019

Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms

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In this paper, we give the first constant approximation algorithm for the lower bounded facility location (LBFL) problem with general lower bounds. Prior to our work, such algorithms were only known for the special case where all facilities have the same lower bound: Svitkina [27] gave a 448-approximation for the special case, and subsequently Ahmadian and Swamy [2] improved the approximation factor to 82.6.As in [27] and [2], our algorithm for LBFL with general lower bounds works by reducing the problem to the capacitated facility location (CFL) problem. To handle some challenges caused by the general lower bounds, our algorithm involves more reduction steps. One main complication is that after aggregation of clients and facilities at a few locations, each of these locations may contain many facilities with different opening costs and lower bounds. To handle this issue, we introduce and reduce our LBFL problem to an intermediate problem called the transportation with configurable supplies and demands (TCSD) problem, which in turn can be reduced to the CFL problem.

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Improved Approximation Guarantees for Lower-Bounded Facility Location

Cited by 37 publications

References 17 publications

Combinatorial approximation algorithms for buy-at-bulk connected facility location problems

Combinatorial approximation algorithms for buy-at-bulk connected facility location problems

Surviving in a competitive market of information providers

On Facility Location with General Lower Bounds

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