Multi-period hub set covering problems with flexible radius: A modified genetic solution

Ebrahimi-zade, Amir; Hosseini-Nasab, Hasan; Zare-Mehrjerdi, Yahya; Zahmatkesh, Alireza

doi:10.1016/j.apm.2015.09.064

Cited by 25 publications

(19 citation statements)

References 34 publications

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“…Younus et al [15] presented a CBIR system by combination of K-means clustering and PSO. First they extracted color histogram and moment, co-occurrence matrices and wavelet transform features.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Related Workmentioning

confidence: 99%

“…As a critical view to the works discribed above, it is important to notice that, most of them use color features such as color histogram in and [15], color moment in [16] and so on. Although color features have been important in researches, they are unable to describe images efficiently for databases with image in same colours.…”

Section: Related Workmentioning

confidence: 99%

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

Sezavar

Farsi

Mohamadzadeh

2019

IJE

View full text Add to dashboard Cite

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“…Therefore, "hub location" plays a crucial role and it is also an important research content in this paper. Hub location consists of two sub-problems: (1) HSCP (Hub Set Covering Problem), and the main research is to minimize the construction cost and transportation cost under the unlimited number of hubs [22][23][24][25]. (2) HMCP (Hub Maximal Covering Problem).…”

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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“…A few research has been conducted on multi-period hub location problems. In hub covering problems, only Ebrahimi-zade et al [15] presented a mathematical formulation for multi-period hub setcovering problem, in which the coverage range is a decision variable. They considered parameters such as travel cost, opening and closing costs of hubs, hubs covering costs, and the income of closing hubs are changed periodically.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Model for Bi-objective Maximal Hub Covering Problem with Periodic Variations of Parameters

Khosravian

Nookabadi

Moslehi

2019

IJE

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A B S T R A C TT he problem of maximal hub covering as a challenging problem in operation research. Transportation programming seeks to find an optimal location of a set of hubs to reach maximum flow in a network. Since the main structure's parameters of the problem such as origin-destination flows, costs and travel time, change periodically in the real world applications, new issues arise in handling it. In this paper, to deal with the periodic variations of parameters, a bi-objective mathematical model is proposed for the single allocation multi-period maximal hub covering problem. T he ε-constraint approach has been applied to achieve non-dominated solutions. Given that the single-objective problem found in the εconstraint method is computationally intractable. Benders decomposition algorithm by adding valid inequalities is developed to accelerate the solution process. Finally, the proposed method is carried out by CAB data set, and the results confirm the efficiency of it regarding optimality and running time.

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Multi-period hub set covering problems with flexible radius: A modified genetic solution

Cited by 25 publications

References 34 publications

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

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

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

Mathematical Model for Bi-objective Maximal Hub Covering Problem with Periodic Variations of Parameters

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