Journal ArticleDOI
A chance constrained programming approach for uncertain p-hub center location problem
Yuan Gao,Zhongfeng Qin +1 more
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TLDR
A hybrid intelligent algorithm is designed to solve the model based on experts' subjective belief in the case of lack of data and numerical examples are presented to illustrate the application of this approach and the effectiveness of the algorithm.About:
This article is published in Computers & Industrial Engineering.The article was published on 2016-12-01. It has received 48 citations till now. The article focuses on the topics: Programming paradigm.read more
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Integer programming formulations of discrete hub location problems
TL;DR: In this paper, integer programming formulations for four types of discrete hub location problems are presented: the p-hub median problem, the uncapacitated hub location problem, p -hub center problems and hub covering problems.
Journal ArticleDOI
Sustainable multi-depot emergency facilities location-routing problem with uncertain information
TL;DR: This paper presents an exploration of the sustainable multi-depot emergency facilities location-routing problem with uncertain information and proposes a hybrid intelligent algorithm that integrates uncertain simulation and a genetic algorithm designed to solve the proposed model.
Journal ArticleDOI
Uncertain goal programming models for bicriteria solid transportation problem
TL;DR: It is proved that the expected value goal Programming model and chance-constrained goal programming model can be respectively transformed into the corresponding deterministic equivalents by taking advantage of some properties of uncertainty theory.
Journal ArticleDOI
Covering location problem of emergency service facilities in an uncertain environment
Bo Zhang,Jin Peng,Shengguo Li +2 more
TL;DR: In this paper, the location set covering problem in an uncertain environment is modeled as an uncertain location set cover problem, which is then solved by using the inverse uncertainty distribution of the covered demand.
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Minimum cost consensus models based on random opinions
TL;DR: Probabilistic planning based on a genetic algorithm is designed to resolve the minimum cost consensus models based on China’s urban demolition negotiation, which can better simulate the consensus decision-making process and obtain a satisfactory solution for the random optimization consensus models.
References
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Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Journal ArticleDOI
Chance-Constrained Programming
TL;DR: The paper presents a method of attack which splits the problem into two non-linear or linear programming parts, i determining optimal probability distributions, ii approximating the optimal distributions as closely as possible by decision rules of prescribed form.
MonographDOI
Genetic algorithms and engineering optimization
Mitsuo Gen,Runwei Cheng +1 more
TL;DR: This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and therefore expensive and labor-heavy process of designing and solving optimization problems.
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.
Book
Uncertainty Theory: A Branch of Mathematics for Modeling Human Uncertainty
TL;DR: Mathematicians, researchers, engineers, designers, and students in the field of mathematics, information science, operations research, system science, industrial engineering, computer science, artificial intelligence, finance, control, and management science will find this work a stimulating and useful reference.