The Origin and Development of Fuzzy Geometric Programming

Cao, Bing-Yuan; Wang, Peihua

doi:10.1080/16168658.2019.1643078

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…Neutrosophy is a branch of philosophy, it is a generalization of an intuitionistic set, fuzzy set, para-consistent set, dialetheist set, paradoxist set, and a tautological set. neutrosophic set and its properties are discussed briefly in [1,29]. The problems which cannot be solved by fuzzy logic can be tackled by neutrosophic logic.…”

Section: Neutrosophic Setsmentioning

confidence: 99%

Computational intelligence optimization approach based on particle swarm optimizer and neutrosophic set for abdominal CT liver tumor segmentation

Anter

Hassenian

2018

Journal of Computational Science

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In this paper, an improved segmentation approach for abdominal CT liver tumor based on neutrosophic sets (NS), particle swarm optimization (PSO), and fast fuzzy C-mean algorithm (FFCM) is proposed. To increase the contrast of the CT liver image, the intensity values and high frequencies of the original images were removed and adjusted firstly using median filter approach. It is followed by transforming the abdominal CT image to NS domain, which is described using three subsets namely; percentage of truth T, percentage of falsity F, and percentage of indeterminacy I. The entropy is used to evaluate indeterminacy in NS domain. Then, the NS image is passed to optimized FFCM using PSO to enhance, optimize clusters results and segment liver from abdominal CT. Then, these segmented livers passed to PSOFCM technique to cluster and segment tumors. The experimental results obtained based on the analysis of variance (ANOVA) technique, Jaccard Index and Dice Coefficient measures show that, the overall accuracy offered by neutrosophic sets is accurate, less time consuming and less sensitive to noise and performs well on non-uniform CT images.

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Section: Neutrosophic Setsmentioning

confidence: 99%

Computational intelligence optimization approach based on particle swarm optimizer and neutrosophic set for abdominal CT liver tumor segmentation

Anter

Hassenian

2018

Journal of Computational Science

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“…These two types of fuzzy numbers, i.e., trapezoidal and triangular, are widely applied in decision making and planning type problems. The algebraic operations on fuzzy numbers are well-known and have been reviewed in many references [43,44]. Therefore, they are not repeated in this paper.…”

Section: Fuzzy Setsmentioning

confidence: 99%

Hybrid fuzzy-stochastic approach for multi-product, multi-period, and multi-resource master production scheduling problem: case of a polyethylene pipe and fitting manufacturer

2018

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Master production scheduling is an e ective phase of production planning, which ends in production scheduling and magnitude of di erent products in a company. This problem requires investigating a wide range of parameters, regarding demand, manufacturing resource usage, and costs. Uncertainty is an intrinsic characteristic of these parameters. In this paper, a model is developed for master production scheduling under uncertainty, in which demands, as time-dependent variables, are considered as stochastic variables, while cost and utilization parameters, with cognitive ambiguity, are expressed as fuzzy numbers. A hybrid approach is also proposed to solve the extended model. The application of the proposed method is examined in a practical problem of a polyethylene pipe and tting Co. in Iran. The result showed a high degree of applicability.

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Geometric programming solution of second degree difficulty for carbon ejection controlled reliable smart production system

2022

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Smart manufacturing systems should always aim to be fully sustainable while simultaneously being as reliable as possible which is difficult to reach. Furthermore, climate change especially by carbon emission ejection in the industry is a significant topic and carbon emission ejection should be controlled and reduced to save the environment. Contributing towards a greener environment in a positive manner is done by reducing the amount of insufficient items that are produced in a smart production system which also can be reached with a higher reliability in the system. Therefore, this study models a smart reliable production system with controlled carbon ejection. To solve the proposed smart production system in this study, a geometric programming approach with a degree of difficulty level two is used which results into optimum results that are quasi-closed. Furthermore, numerical experiments are conducted in order to validate the proposed model and prove that by using a higher degree geometric programming approach, an optimal solution is found. The numerical results do not only show optimal solutions but also that the smart production system with controlled carbon ejection is reliable.

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The Origin and Development of Fuzzy Geometric Programming

Cited by 3 publications

References 30 publications

Computational intelligence optimization approach based on particle swarm optimizer and neutrosophic set for abdominal CT liver tumor segmentation

Computational intelligence optimization approach based on particle swarm optimizer and neutrosophic set for abdominal CT liver tumor segmentation

Hybrid fuzzy-stochastic approach for multi-product, multi-period, and multi-resource master production scheduling problem: case of a polyethylene pipe and fitting manufacturer

Geometric programming solution of second degree difficulty for carbon ejection controlled reliable smart production system

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