Solving large-scale maximum expected covering location problems by genetic algorithms: A comparative study

Aytuğ, Haldun; Saydam, Cem

doi:10.1016/s0377-2217(01)00260-0

Cited by 80 publications

(37 citation statements)

References 25 publications

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“…That is the case of Lee and Lee (2010), with a tabu search heuristic to solve a generalized hierarchical covering FLP. Similarly, Aytug and Saydam (2002) and Shavandi and Mahlooji (2006) solved the same problem with a genetic algorithm. Other examples are as follows: Berman et al (2007), who developed a greedy algorithm for a generalized maximal covering location problem; and Sakakibara et al (2012), who simultaneously solve the storage and delivery problem using a relax-and-fix heuristic.…”

Section: Related Literaturementioning

confidence: 99%

The Effect of Environmental Criteria on Locating a Biorefinery: A Green Facility Location Problem

Serrano-Hernández

Faulín

Belloso

et al. 2017

dmms

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Abstract. Underestimating facility location decisions may penalize business performance over the time. These penalties have usually been studied from the economic point of view, analyzing its impact on profitability. Additionally, the concern about obtaining sustainability is gaining importance, leading to a search for renewable energy sources to reduce greenhouse gas emissions. However, little attention has been paid to choosing a location considering environmental criteria. Thus, this work aims at determining a biorefinery location considering its impacts on natural resources. Therefore, a mixed integer linear programming (MILP) model has been developed, taking into account crop location and biomass production seasonality to obtain a proper location that minimizes environmental impact.

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Section: Related Literaturementioning

confidence: 99%

The Effect of Environmental Criteria on Locating a Biorefinery: A Green Facility Location Problem

Serrano-Hernández

Faulín

Belloso

et al. 2017

dmms

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Section: Expected Coverage Modelsmentioning

confidence: 99%

“…At first it may seem that the model needs a constraint of the form in order to ensure that y im is set to 1 for the correct values of m; that is, for the k smallest values of m, where k is the number of opened facilities that cover i. However, such a constraint is not necessary since the objective function coefficient is larger for smaller values of m; the model will automatically set y im = 1 for the k smallest values of m. proposes a heuristic for MEXCLP based on node exchanges, and several metaheuristics have been proposed subsequently; see, e.g., Aytug andSaydam (2002), and Rajagopalan et al (2007).The primary criticism that has been leveled at the MEXCLP concerns the assumption of a uniform system-wide availability probability, since availability might vary based on geographic area or on the demand assigned to each facility. ReVelle and Hogan (1989) address this concern in the Maximum Availability Location Problem (MALP), a chance-constrained version of MCLP.…”

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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 advent of cheaper and faster computing during the 1990s, availability of fast commercial solvers such as CPLEX [4] and the development of meta-heuristics facilitated researchers in developing probabilistic, and therefore, more realistic models [5][6][7][8][9][10][11]. For earlier developments readers are referred to the reviews by Shilling, Jayaraman, and Barkhi [12] and Owen and Daskin [13].…”

Section: Literature Reviewmentioning

confidence: 99%

“…There is strong evidence that carefully crafted meta-heuristics in the location domain produce excellent results [5,8,9,11,35,[38][39][40][41][42]. Most models in the literature that are solved using meta-heuristics have one objective such as to find the maximum coverage with a given set of servers.…”

Section: The Algorithmmentioning

confidence: 99%