Result: Meta-heuristic algorithms for solving a fuzzy single-period problem

Title:
Meta-heuristic algorithms for solving a fuzzy single-period problem
Authors:
ALLAH TALEIZADEH, Ata, BARZINPOUR, Farnaz, WEE, Hui-Ming
Source:
Mathematical and computer modelling. 54(5-6):1273-1285
Publisher Information:
Kidlington: Elsevier, 2011.
Publication Year:
2011
Physical Description:
print, 40 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Optimisation et calcul variationnel numériques, Numerical methods in optimization and calculus of variations, Méthodes de calcul scientifique (y compris calcul symbolique, calcul algébrique), Methods of scientific computing (including symbolic computation, algebraic computation), Algorithme génétique, Genetic algorithm, Algoritmo genético, Analyse assistée, Computer aided analysis, Análisis asistido, Analyse numérique, Numerical analysis, Análisis numérico, Calcul variationnel, Variational calculus, Cálculo de variaciones, Gestion stock, Inventory control, Administración depósito, Mathématiques appliquées, Applied mathematics, Matemáticas aplicadas, Modèle mathématique, Mathematical model, Modelo matemático, Modèle stochastique, Stochastic model, Modelo estocástico, Méthode heuristique, Heuristic method, Método heurístico, Méthode optimisation, Optimization method, Método optimización, Performance algorithme, Algorithm performance, Resultado algoritmo, Programmation mathématique, Mathematical programming, Programación matemática, Recuit simulé, Simulated annealing, Recocido simulado, 49XX, 65K10, 65Kxx, Meta-heuristic, Multi-product multi-constraint, Single-period problem
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Department of Industrial Engineering, Iran University of Science and Technology, Iran, Islamic Republic of
Department of Industrial & Systems Engineering, Chung Yuan Christian University, 32023 Chungli, Tawain, Province of China
ISSN:
0895-7177
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=24266316
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.24266316
Database:
PASCAL Archive

Further Information

Single-period problem (SPP) is a classical stochastic inventory model that has become very popular recently. In this research, we developed a SPP with fuzzy environment. The demand of each product is considered as LR-fuzzy variables (ranking fuzzy numbers based on the left and right deviation degrees), and multiple constraints (including service level, batch order, budget, space and upper limit for each order). The aim of this paper is to maximize the total expected profit under incremental discount strategy. Five hybrid intelligent algorithms based on fuzzy simulation (FS) and meta-heuristic methods are presented; they are bees colony optimization (BCO), harmony search (HS), particle swarm optimization (PSO), genetic algorithm (GA) and simulated annealing (SA). Three numerical examples are presented to illustrate the performance of the algorithms. Our study shows that the BCO-FS hybrid method performs better than the HS-FS, GA-FS, PSO-FS, and SA-FS hybrid methods.