Anti-covering Problems

Carrizosa, Emilio; G.-Tóth, Boglárka

doi:10.1007/978-3-319-13111-5_6

Cited by 6 publications

(3 citation statements)

References 70 publications

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“…Equally, he developed a new model when less than optimal sites are employed in a dispersive pattern called the Disruptive Anti-Covering location model. Carrizosa and Tóth (Carrizosa and Tóth, 2015) analyzed and solved the continual Anti-Covering Location Problem. They formulated a bi-objective optimization model which minimizes a sum of "facility -individual" interactions, and a sum of "facilityfacility" interactions.…”

Section: The Anti-covering Location Problemmentioning

confidence: 99%

Locating dangerous goods with constant and variable impact radius

Dimitrijević¹,

Nikolic²,

Vukadinović³

et al. 2016

Vojnotehni?ki glasnik

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Making decisions about dangerous goods positioning is crucial when it is necessary to minimize environmental risks. In this paper, a specific problem of locating various kinds of dangerous goods (with different characteristics) has been considered. Such goods should be located in a known discrete set of potential storage sites, under condition of the minimum safety distance (MSD) between selected locations. The existence of the MSD is a consequence of the possibility that dangerous goods transfer their undesirable effects to the objects in the neighborhood. The objective here is to maximize the quantity of different kinds of dangerous goods stored meanwhile respecting MSDs. For some dangerous goods, the MSD may be determined as a constant value, which depends only on the dangerous goods' characteristics. On the other hand, the MSD may vary depending on quantity and characteristics of particular dangerous goods. Mixed integer linear programming models are proposed for these two types of MSDs. The spirit of the anti-covering location problem (ACLP) is present in the proposed formulations and thus these models can be viewed as a modification and extension of the ACLP. Finally, a randomly generated numerical example has been used to verify and illustrate the proposed models.

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Section: The Anti-covering Location Problemmentioning

confidence: 99%

Locating dangerous goods with constant and variable impact radius

Dimitrijević¹,

Nikolic²,

Vukadinović³

et al. 2016

Vojnotehni?ki glasnik

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“…In addition, the QCpLP can also be used to model situations in which a negative interaction between obnoxious (or undesirable) facilities exist and as a consequence, it is desirable to locate them far from each other. For instance, dangerous plants producing or handling hazardous materials are a source of potential damage to population and their negative impact can be measured as a function of the distances between them (Carrizosa and Tóth 2015). A negative interaction among facilities also arises when the facilities compete for the same market and locating facilities as far away from each other is beneficial to mitigate cannibalization and to optimize their competitive market advantage (Curtin andChurch 2006, Lei andChurch 2013).…”

Section: Introductionmentioning

confidence: 99%

An Exact Algorithm for Large-scale Non-convex Quadratic Facility Location

Zetina,

Contreras,

Jayaswal

2021

Preprint

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We study a general class of quadratic capacitated p-location problems facility location problems with single assignment where a non-separable, non-convex, quadratic term is introduced in the objective function to account for the interaction cost between facilities and customer assignments. This problem has many applications in the field of transportation and logistics where its most well-known special case is the single-allocation hub location problem and its many variants. The non-convex, binary quadratic program is linearized by applying a reformulation-linearization technique and the resulting continuous auxiliary variables are projected out using Benders decomposition. The obtained Benders reformulation is then solved using an exact branch-and-cut algorithm that exploits the underlying network flow structure of the decomposed separation subproblems to efficiently generate strong Pareto-optimal Benders cuts. Additional enhancements such as a matheuristic, a partial enumeration procedure, and variable elimination tests are also embedded in the proposed algorithmic framework. Extensive computational experiments on benchmark instances (with up to 500 nodes) and on a new set of instances (with up to 1,000 nodes) of four variants of single-allocation hub location problems confirm the algorithm's ability to scale to large-scale instances.

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“…This problem can be defined on a bounded continuous region or a discrete set of sites (Carrizosa & Tóth, 2015). When defined on a bounded continuous domain it is generally assumed that all facilities must be located within the region and be at least r-distance from the boundary and at least r-distance from each other.…”

Section: Introductionmentioning

confidence: 99%