Uncertain goal programming models for bicriteria solid transportation problem

Chen, Lin; Peng, Jin; Zhang, Bo

doi:10.1016/j.asoc.2016.11.027

Cited by 94 publications

(40 citation statements)

References 32 publications

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“…Number Entropies with Right Order in Accuracy Number In conclusion, the above theoretical and experimental results show that the proposed parametric algorithm is simple and feasible, and it is an effective tool for knowledge measure. In future research, we will apply the constructed knowledge measure model to calculate the information volume of uncertain variables, study the information-based sorting and decision operators, and apply them to the research of management science decision making like references [19] [20], such as supply chain management and risk management.…”

Section: Experimental Example and Results Analysismentioning

confidence: 99%

A Knowledge Measure with Parameter of Intuitionistic Fuzzy Sets

Zhang¹,

Yuan²,

Zhang³

et al. 2018

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A new knowledge measure with parameter of intuitionistic fuzzy sets (IFSs) is presented based on the membership degree and the non-membership degree of IFSs, which complies with the extended form of Szmidt-Kacprzyk axioms for intuitionistic fuzzy entropy. And a sufficient and necessary condition of order property in the Szmidt-Kacprzyk axioms is discussed. Additionally, some numerical examples are given to illustrate the applications of the proposed knowledge measure and some conventional entropies and knowledge measures of IFSs. The experimental results show that the results of the parametric model proposed in this paper are more accurate than those of most of the classic models.

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Section: Experimental Example and Results Analysismentioning

confidence: 99%

A Knowledge Measure with Parameter of Intuitionistic Fuzzy Sets

Zhang¹,

Yuan²,

Zhang³

et al. 2018

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show abstract

“…By solving these equations, we can obtain the results of x 1 = µ/(2I), x 2 = µ/4, and µ = 4I 2 /(2 + I) (µ = 0 yields an infeasible solution for I > 0). Hence, the NN optimal solution is obtained by the results of Compared with existing uncertain optimization methods [1][2][3][4][5][6], the proposed NN-NP methods can obtain ranges of optimal solutions (usually NN solutions but not always) rather than the crisp optimal solutions of previous uncertain optimization methods [1][2][3][4][5][6], which are not really meaningful in indeterminate programming of indeterminate solutions in real situations [23,24]. The existing uncertain optimization solutions are the special cases of the proposed NN-NP optimization solutions.…”

Section: Examplementioning

confidence: 99%

“…However, uncertainty also exists in real problems. Hence, many researchers have proposed uncertain optimization methods, such as approaches using fuzzy and stochastic logics, interval numbers, or uncertain variables [1][2][3][4][5][6]. Uncertain programming has been widely applied in engineering, management, and design problems.…”

Section: Introductionmentioning

confidence: 99%

Neutrosophic Number Nonlinear Programming Problems and Their General Solution Methods under Neutrosophic Number Environments

Cui

2018

Axioms

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In practical situations, we often have to handle programming problems involving indeterminate information. Building on the concepts of indeterminacy I and neutrosophic number (NN) (z = p + qI for p, q ∈ R), this paper introduces some basic operations of NNs and concepts of NN nonlinear functions and inequalities. These functions and/or inequalities contain indeterminacy I and naturally lead to a formulation of NN nonlinear programming (NN-NP). These techniques include NN nonlinear optimization models for unconstrained and constrained problems and their general solution methods. Additionally, numerical examples are provided to show the effectiveness of the proposed NN-NP methods. It is obvious that the NN-NP problems usually yield NN optimal solutions, but not always. The possible optimal ranges of the decision variables and NN objective function are indicated when the indeterminacy I is considered for possible interval ranges in real situations.

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“…Grey correlation theory is a certain method, the grey relational analysis of various factors in the concept, to seek the numerical relationship between the various factors in the system [1]. Therefore, the grey correlation analysis to the development of a system change situation provides a quantitative measure, very suitable.…”

Section: Grey Correlation Analysismentioning

confidence: 99%

Intelligent Urban Sustainable Development Plan

Ku¹,

Liao²

2017

JAMP

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Based on the entropy weight method and the gray relational analysis method, we first calculate the weight of each index and the correlation coefficient between the indicators, get the urban intelligent growth index, and then calculate the annual growth rate of the composite index. We find the following results. First, Suzhou to improve its environment, the success rate of 109.67%. Saint Louis to improve its economy, the growth rate of 57.4%. Second, the sensitivity of the data analysis, each of the indicators is increased by 10%, 20%, 30%, other indicators remain unchanged. Recalculate the city's intelligent growth index, we find that the greater the volatility, the greater the potential. The total population of the city, built green area, the total length of the bus operating a greater potential, built-up area, the smallest regional GDP potential. Finally, we propose an improved model combining remote sensing with GIS to analyze urban expansion and farmland loss from time and space qualitatively and quantitatively.

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Uncertain goal programming models for bicriteria solid transportation problem

Cited by 94 publications

References 32 publications

A Knowledge Measure with Parameter of Intuitionistic Fuzzy Sets

A Knowledge Measure with Parameter of Intuitionistic Fuzzy Sets

Neutrosophic Number Nonlinear Programming Problems and Their General Solution Methods under Neutrosophic Number Environments

Intelligent Urban Sustainable Development Plan

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