A comparison of Lagrangean and surrogate relaxations for the maximal covering location problem

Galvão, Roberto D.; Espejo, Luis Gonzalo Acosta; Boffey, Brian

doi:10.1016/s0377-2217(99)00171-x

Cited by 77 publications

(34 citation statements)

References 23 publications

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“…Satellite imaging scheduling, is that, under certain constraints, imaging parameters is determined by the algorithm, so that the value of the objective function is optimal (Galvao et al, 2000). In this paper, the objective function is the evaluation model of the quality of the multi-strip images, and the constraints are only that imaging attitudes is required to meet the angle constraints due to the mobility restrictions: T which meets the equation (5) is considered as the optimal starting imaging time of the first strip, and the corresponding imaging time-window and attitude of each strip is the optimal imaging parameter via scheduling.…”

Section: The Algorithm Of Multi-strip Imaging Scheduling Of the Agilementioning

confidence: 99%

An Optimal Imaging-Scheduling Algorithm for the Multi-Strip Imaging-Mode of the High-Resolution Agile Satellites Based on Certain Step-Size Search

Yang

Wang

Sheng

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The high flexibility of the agile satellite can significantly improve its ability to obtain data. Compared with ordinary satellite platforms, the imaging-scheduling is more important, and is the basis to ensure the efficiency and quality of data for the agile satellite. To be aimed at the multi-strip imaging-mode of the agile satellite, from the perspective of the image-quality, a imagingscheduling algorithm based on certain step-size search in the multi-strip imaging-mode of the agile satellite is proposed , and the influence of the selection of the imaging time-window on the multi-strip image quality is analyzed quantitatively through the experimental data. The results show that the optimal image data can be obtained through the image-scheduling algorithm.

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Section: The Algorithm Of Multi-strip Imaging Scheduling Of the Agilementioning

confidence: 99%

An Optimal Imaging-Scheduling Algorithm for the Multi-Strip Imaging-Mode of the High-Resolution Agile Satellites Based on Certain Step-Size Search

Yang

Wang

Sheng

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In relation to classical location problems we may cite the Lagrangean dual ascent heuristic developed by Guignard (1988) for the simple plant location problem, a comparison of Lagrangean and surrogate relaxations for the maximal covering location problem by Galvão et al (2000), the development of dual-based heuristics for a hierarchical covering location problem by Espejo et al (2003), a maximal covering location model in the presence of partial coverage by Karasakal and Karasakal (2004), and a branch-and-price approach to p-median location problems by Senne et al (2005).…”

Section: A Survey Of Subsequent Workmentioning

confidence: 99%

Hub Location Problems: The Location of Interacting Facilities

Kara

Taner

2011

International Series in Operations Research &Amp; Management Science

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), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. PrefaceThis book is the final result of a number of incidents that occurred to us while discussing location issues with colleagues at conferences. Frequently we attended presentations, in which the authors quoted the well-known references that helped to make the discipline what it is today. Upon further inquiry, though, it turned out that some of these colleagues had never actually read the original papers. We then discussed among ourselves which contributions could be credited with shaping the field. And, lo and behold, we found that we, too, had neglected to read some of the papers that form the foundation of our science. Whether it was laziness or other things that got in the way, it had become clear that something had to be done. Our first thought was to collect the original contributions (once we could agree on what they were) and reprint them. When discussing this possibility with a publisher, we immediately ran into a roadblock in the form of copyright. While this appeared to have stopped our enthusiastic effort dead in its tracks, we kept on collecting and reading what we considered original contributions.This went on until we met Camille Price, who suggested that, rather than reprinting the original contributions, we should invite some of the leaders in our field and ask them to describe the original contribution, explain and interpret it, and comment on the impact that it had to the field. This was, of course, an excellent idea, and the response by our colleagues to our pertinent requests was equally enthusiastic. What you hold in your hands is the result of this effort.In other words, the purpose of this book is to provide easy access to the main contributions to location theory. The book is organized as follows. The introductory chapter provides an overview of some of the many facets of location analysis. This is followed by contributions in the main three fields of inquiry: minisum, minimax, and covering problems. The next chapters are part of an ever-growing list of nonstandard location models: models including competitive components, those that locate undesirable facilities, those with probabilistic features, and those that allow interactions between facilities. The following chapters discuss solution techniques: after a discussion of exact and heuristic techniques, we devote an entire chapter to Weiszfeld's method, and another to Lagrangean techniques. The last chapters of this book deal with the spheres of influence that the facilities generate and that attract viii customers to them, an...

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“…The MCLP seeks to find the solution to the problem of locating facilities that maximizes the coverage of demand for services within a given acceptable service distance (or response time). Because the MCLP has been shown to be extremely combinatorially complex, a series of heuristic solution procedures have been developed (AdensoDiaz and Rodriguez 1997; Galvao et al 2000;Galvao and ReVelle 1996). Additionally, the MCLP can be seen as a variant formulation of other prominent location models including the p-Median model and the Location Set Covering model (Church and ReVelle 1976), and research has been conducted on the effects of data aggregation errors on solutions (Current and Schilling 1990).…”

Section: Covering Modelsmentioning

confidence: 99%

Determining Optimal Police Patrol Areas with Maximal Covering and Backup Covering Location Models

2007

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A comparison of Lagrangean and surrogate relaxations for the maximal covering location problem

Cited by 77 publications

References 23 publications

An Optimal Imaging-Scheduling Algorithm for the Multi-Strip Imaging-Mode of the High-Resolution Agile Satellites Based on Certain Step-Size Search

An Optimal Imaging-Scheduling Algorithm for the Multi-Strip Imaging-Mode of the High-Resolution Agile Satellites Based on Certain Step-Size Search

Hub Location Problems: The Location of Interacting Facilities

Determining Optimal Police Patrol Areas with Maximal Covering and Backup Covering Location Models

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