A modified NSGA-II solution for a new multi-objective hub maximal covering problem under uncertain shipments

Zade, Amir Ebrahimi; Sadegheih, Ahmad; Lotfi, Mohammad Mehdi

doi:10.1007/s40092-014-0076-4

Cited by 38 publications

(10 citation statements)

References 30 publications

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“…Constraints set (5) state that in each period, node i can be allocated to node k, if node k is served as a hub. Constraints set (6) show that in each period, the path i → k → l → j will be established where node i is allocated to hub k and node j is allocated to hub l. Constraints set (7) ensure that in each period, the path i → k → l → j is established, where traveling time is less than the coverage radius of β. Constraints set (8) and (9) indicate the possibility of creating and closing hubs over different periods.…”

Section: Notations and Parametersmentioning

confidence: 99%

“…Jabalameli et al [5] proposed two mathematical formulas for MHCP with a single allocation and developed a simulated annealing algorithm to solve it. Ebrahimi-zade et al [6] presented a non-linear multiobjective model for MHCP by considering uncertainty. The model was provided for single and multiple allocation types and it was also linearized.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Notations and Parametersmentioning

confidence: 99%

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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“…Both the CGMCLP and CGMCLAP are integer non-linear programming models. It should be noted that non-linear models have real applications such as cell formation and single and multiple allocation maximal hub covering problems (see Zade et al 2014;Shirazi et al 2014).…”

Section: Formulation Of the Cgmclp Modelmentioning

confidence: 99%

“…Known as covering models, these are very popular among researchers because of their applications in real-world problems. There are many studies that have incorporated extensions of the covering location problem (see Berman et al 2009;Zade et al 2014;Javadi and Shahrabi 2014).…”

Section: Introductionmentioning

confidence: 99%

General form of a cooperative gradual maximal covering location problem

2017

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Cooperative and gradual covering are two new methods for developing covering location models. In this paper, a cooperative maximal covering location-allocation model is developed (CMCLAP). In addition, both cooperative and gradual covering concepts are applied to the maximal covering location simultaneously (CGMCLP). Then, we develop an integrated form of a cooperative gradual maximal covering location problem, which is called a general CGMCLP. By setting the model parameters, the proposed general model can easily be transformed into other existing models, facilitating general comparisons. The proposed models are developed without allocation for physical signals and with allocation for nonphysical signals in discrete location space. Comparison of the previously introduced gradual maximal covering location problem (GMCLP) and cooperative maximal covering location problem (CMCLP) models with our proposed CGMCLP model in similar data sets shows that the proposed model can cover more demands and acts more efficiently. Sensitivity analyses are performed to show the effect of related parameters and the model's validity. Simulated annealing (SA) and a tabu search (TS) are proposed as solution algorithms for the developed models for large-sized instances. The results show that the proposed algorithms are efficient solution approaches, considering solution quality and running time.

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“…Feature extraction has been an important task in signal processing areas, image, video and speech processing [6][7]. Also, the main part of any image retrieval system is feature extraction.…”

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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A modified NSGA-II solution for a new multi-objective hub maximal covering problem under uncertain shipments

Cited by 38 publications

References 30 publications

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

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

General form of a cooperative gradual maximal covering location problem

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

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