scispace - formally typeset
Open AccessBook

Theory and practice of uncertain programming

Baoding Liu
Reads0
Chats0
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

Citations
More filters
Journal ArticleDOI

Uncertain multi-objective optimization for the water–rail–road intermodal transport system with consideration of hub operation process using a memetic algorithm

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

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

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

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
More filters
Book

Fuzzy sets

TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Journal ArticleDOI

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.
Book

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.
Journal ArticleDOI

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.