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Theory and practice of uncertain programming
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TLDR
This book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem.Abstract:
Real-life decisions are usually made in the state of uncertainty such as randomness and fuzziness. How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.read more
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Book
Uncertainty Theory
TL;DR: Mathematicians, researchers, engineers, designers, and students in the field of mathematics, information science, operations research, industrial engineering, computer science, artificial intelligence, and management science will find this work a stimulating and useful reference.
Some Research Problems in Uncertainty Theory
TL;DR: In this article, a new uncertain calculus is proposed and applied to uncertain difierential equation, flnance, control, flltering and dynamical systems based on the uncertainty theory.
Fuzzy Process, Hybrid Process and Uncertain Process
TL;DR: In order to construct fuzzy counterparts of Brownian motion and stochastic calculus, some basic concepts of fuzzy process are proposed, including fuzzy calculus and fuzzy difierential equation, which are extended to hybrid process and uncertain process.
Journal ArticleDOI
Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles
TL;DR: A comprehensive review of Uncertainty-Based Multidisciplinary Design Optimization (UMDO) theory and the state of the art in UMDO methods for aerospace vehicles is presented.
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Optimal Cloudlet Placement and User to Cloudlet Allocation in Wireless Metropolitan Area Networks
TL;DR: An algorithm is devised that enables the placement of the cloudlets at user dense regions of the WMAN, and assigns mobile users to the placed cloudlets while balancing their workload, which indicates that the performance of the proposed algorithm is very promising.
References
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Journal ArticleDOI
Uncertain solid transportation problems
TL;DR: This paper deals with two of the ways in which uncertainty can appear in theSolid transportation problem: Interval solid transportation problem and fuzzy solid transportationProblem, where data problem are expressed as intervals instead of point values.
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Interactive fuzzy programming for multi-level linear programming problems with fuzzy parameters
TL;DR: In this paper, interactive fuzzy programming for multi-level linear programming problems with fuzzy parameters is presented, and a satisfactory solution is derived efficiently by updating the satisfactory degrees of decision makers with considerations of overall satisfactory balance among all levels.
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Genetic-algorithm-based reliability optimization for computer network expansion
TL;DR: The results demonstrate that GANE is very effective (in both accuracy and computation time) and applies to a wide range of problems, but it does not guarantee the optimal results for every problem.
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A class of fuzzy random optimization: expected value models
Yian-Kui Liu,Baoding Liu +1 more
TL;DR: The linearity of a scalar value expected value operator of fuzzy random variable is discussed, and a fuzzy random simulation approach is suggested to evaluate the expected value of a fuzzyrandom variable.
Book
Possibilistic Data Analysis for Operations Research
英夫 田中,Peijun Guo +1 more
TL;DR: Theoretical Possibility Models Theory of Possibilistic Systems Based on Exponential Possibility Distributions Identification of Possibility Distributionions Possible Regression Analysis Possibiliistic Portfolio Selection Problems Discriminant Analysis Based on Possibility Discriminative Analysis Rough Set Analysis.