Novel formulations and VNS-based heuristics for single and multiple allocation p-hub maximal covering problems

Janković, Olivera; Mišković, Stefan; Stanimirović, Zorica; Todosijević, Raca

doi:10.1007/s10479-017-2508-1

Cited by 22 publications

(9 citation statements)

References 36 publications

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“…The whole process is iterated until a predefined stopping condition (e.g., maximum number of iterations or maximum CPU time) is met. Many variants of VNS have been derived from the Basic VNS scheme and applied to different optimization problems (for a survey, see, e.g., Hansen et al, ; for recent publications, see Irawan et al, ; Janković et al, ; Pinto et al, ; Brimberg et al, ; Chagas et al, ; Gruler et al, ).…”

Section: Basic Variable Neighborhood Search For the Opmpmentioning

confidence: 99%

Less is more approach: basic variable neighborhood search for the obnoxious p‐median problem

Mladenović

Alkandari

Pei

et al. 2019

Int Trans Operational Res

Self Cite

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The goal of the less is more approach (LIMA) for solving optimization problems that has recently been proposed in Mladenović et al. (2016) is to find the minimum number of search ingredients that make a heuristic more efficient than the currently best. In this paper, LIMA is successfully applied to solve the obnoxious p‐median problem (OpMP). More precisely, we developed a basic variable neighborhood search for solving the OpMP, where the single search ingredient, the interchange neighborhood structure, is used. We also propose a new simple local search strategy for solving facility location problems, within the interchange neighborhood structure, which is in between the usual ones: first improvement and best improvement strategies. We call it facility best improvement local search. On the basis of experiments, it appeared to be more efficient and effective than both first and best improvement. According to the results obtained on the benchmark instances, our heuristic turns out to be highly competitive with the existing ones, establishing new state‐of‐the‐art results. For example, four new best‐known solutions and 133 ties are claimed in testing the set with 144 instances.

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Section: Basic Variable Neighborhood Search For the Opmpmentioning

confidence: 99%

Less is more approach: basic variable neighborhood search for the obnoxious p‐median problem

Mladenović

Alkandari

Pei

et al. 2019

Int Trans Operational Res

Self Cite

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“…(2) HMCP (Hub Maximal Covering Problem). Its main research content is to determine the optimum hub location and arrange the most reasonable number of hubs in the region to achieve the maximum utilization of the hub under the minimum operating costs [26][27][28][29]. In addition, in practice, many problem parameters are characterized by high uncertainty, and the research on the hub location under uncertain environments is also receiving increasing attention [30][31][32][33][34][35][36].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition, in practice, many problem parameters are characterized by high uncertainty, and the research on the hub location under uncertain environments is also receiving increasing attention [30][31][32][33][34][35][36]. Janković et al [26] proposed a new mixed-integer programming model to study single-capacity and multi-capacity maximum coverage problems with binary and partial coverage criteria. The experimental data show that the model has obvious advantages in the solution quality and running time significance.…”

Section: Literature Reviewmentioning

confidence: 99%

An Empirical Study on the Innovation Sharing Express Box: Collaborative Consumption and the Overlay Network Design

Liu

Tsai

et al. 2018

Sustainability

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Sharing express boxes is an effective disposal method for obsolete express packages. Its appearance also represents an unstoppable trend in the development of green logistics. This paper takes the sharing express box as research object and conducts two-stage research. In the first stage, the collaborative consumption theory and calculus are used to analyze the economic benefits that sharing express boxes bring to operators, that is, to demonstrate the feasibility of this business model from an economic perspective. In the second stage, the design of the overlay network is studied from the management science perspective. Firstly, an optimal coverage model is established considering the characteristics of the sharing express box, and cleaning costs, relocation costs, etc. are all integrated into the model. Secondly, the genetic algorithm is used to solve the model. A numerical example is described to illustrate the feasibility of the proposed method. In addition, sensitivity analysis investigates the effect of hub coverage change on the results. Finally, the theoretical guidance gained from this paper can be beneficial to the sustainable development of sharing express boxes.

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“…Two basic natural-inspired algorithms are genetic algorithm (GA) and particle swarm optimization (PSO) [11]. Many intelligence particle swarm algorithms have been introduced in recent years such as inclined Planes optimization (IPO) [12], gravitational search algorithm (GSA) and other methods which have been successful in optimization tasks. Grasshopper optimization algorithm (GOA) [13] is one of the new-introduced methods which is able to successfully solve complex optimization problems [13].…”

Section: Introductionmentioning

confidence: 99%

A Modified Grasshopper Optimization Algorithm Combined with Convolutional Neural Network for Content Based Image Retrieval

Sezavar

Farsi

Mohamadzadeh

2019

IJE

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A B S T R A C TNowadays, with huge progress in digital imaging, new image processing methods are needed to manage digital images stored on disks. Image retrieval has been one of the most challengeable fields in digital image processing which means searching in a big database in order to represent similar images to the query image. Although many efficient researches have been performed for this topic so far, there is a semantic gap between human concept and features extracted from the images and it has become an important problem which decreases retrieval precision. In this paper, a convolutional neural network (CNN) is used to extract deep and high-level features from the images. Next, an optimization problem is defined in order to model the retrieval system. Heuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) have shown an effective role in solving the complex problems. A recent introduced heuristic algorithm is Grasshopper Optimization Algorithm (GOA) which has been proved to be able to solve difficult optimization problems. So, a new search method, modified grasshopper optimization algorithm (MGOA) is proposed to solve modeled problem and to retrieve similar images efficiently, despite of total search in database. Experimental results showed that the proposed system named CNN-MGOA achieves superior accuracy compared to traditional methods.

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Novel formulations and VNS-based heuristics for single and multiple allocation p-hub maximal covering problems

Cited by 22 publications

References 36 publications

Less is more approach: basic variable neighborhood search for the obnoxious p‐median problem

Less is more approach: basic variable neighborhood search for the obnoxious p‐median problem

An Empirical Study on the Innovation Sharing Express Box: Collaborative Consumption and the Overlay Network Design

A Modified Grasshopper Optimization Algorithm Combined with Convolutional Neural Network for Content Based Image Retrieval

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