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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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Journal ArticleDOI
Uncertain multi-objective optimization for the water–rail–road intermodal transport system with consideration of hub operation process using a memetic algorithm
Wenying Zhang,Xifu Wang,Kai Yang +2 more
TL;DR: A memetic algorithm (MA) is developed by combining a genetic algorithm and local intensification to solve the multi-objective optimization of water–rail–road (WRR)intermodal transport system under uncertainty by explicitly capturing intermodal hub operation activities.
Journal ArticleDOI
A Rough Programming Approach to Power-Balanced Instruction Scheduling for VLIW Digital Signal Processors
Shu Xiao,Edmund M-K. Lai +1 more
TL;DR: R rough set theory is used to characterize the imprecision inherent in the instruction-level power model that is obtained through empirical measurements and shows that the near-optimal schedules obtained are significantly better than those obtained through the mixed-integer programming approach.
Journal ArticleDOI
Constrained covering solid travelling salesman problems in uncertain environment
TL;DR: An RID-MGA heuristics is developed to solve the proposed model in trust measure and justify its performance by comparing some best known result of some benchmark problems and then solve experiment with some randomly generated data.
Journal ArticleDOI
A trust-based approach to selection of business services
Sufen Li,Yushun Fan,Xitong Li +2 more
TL;DR: A trust-based approach for selection of business services is proposed based on the formal definition of the SOBE and a fuzzy chance-constrained programming model is proposed by considering four kinds of factors: QoS attributes, trust relationship, physical distance and waiting time.
Proceedings ArticleDOI
Credibility programming approach to fuzzy portfolio selection problems
Jin Peng,Mok,Wai-Man Tse +2 more
TL;DR: A hybrid intelligent algorithm is designed to solve the portfolio selection problems in fuzzy environments by credibility programming approach based on credibility measure and its effectiveness is illustrated by numerical experiments.
References
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Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
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Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
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Dynamic Programming
TL;DR: The more the authors study the information processing aspects of the mind, the more perplexed and impressed they become, and it will be a very long time before they understand these processes sufficiently to reproduce them.
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The concept of a linguistic variable and its application to approximate reasoning—II☆
TL;DR: Much of what constitutes the core of scientific knowledge may be regarded as a reservoir of concepts and techniques which can be drawn upon to construct mathematical models of various types of systems and thereby yield quantitative information concerning their behavior.