Result: Greedy random adaptive memory programming search for the capacitated clustering problem : Logistics: From theory to application

Title:
Greedy random adaptive memory programming search for the capacitated clustering problem : Logistics: From theory to application
Authors:
AHMADI, Samad, OSMAN, Ibrahim H
Source:
European journal of operational research. 162(1):30-44
Publisher Information:
Amsterdam: Elsevier, 2005.
Publication Year:
2005
Physical Description:
print, 1 p.1/4
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Flots dans les réseaux. Problèmes combinatoires, Flows in networks. Combinatorial problems, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Théorie programmation, Programming theory, Optimisation essaim particule, Swarm intelligence, Optimización enjambre partícula, Problème localisation, Location problem, Problema localización, Quartier voisinage, Neighbourhood, Barrio vecindad, Recherche aléatoire, Random search, Investigación aleatoria, Recherche locale, Local search, Busca local, Métaheuristique recherche construction guidée, Métaheuristique, Metaheuristics, Programmation mémoire adaptative, Recherche adaptative randomisée glouton, Recherche densité, Adaptive memory programming, Ant colony optimization, Capacitated clustering (p-median) problem, Density search, Greedy randomized adaptive search procedure, Guided construction search metaheuristic
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
School of Computing, De Montfort University, The Gateway, Leicester LE1 9BH, United Kingdom
Center for Advanced Mathematical Studies and School of Business, American University of Beirut, Beirut, Lebanon
ISSN:
0377-2217
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=16436324
Rights:
Copyright 2005 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:
Computer science; theoretical automation; systems

Operational research. Management
Accession Number:
edscal.16436324
Database:
PASCAL Archive

Further Information

In the capacitated clustering problem (CCP), a given set of n weighted points is to be partitioned into p clusters such that, the total weight of the points in each cluster does not exceed a given cluster capacity. The objective is to find a set of p centers that minimises the total scatter of points allocated to these centers. In this paper, we propose a merger of Greedy Random Adaptive Search Procedure (GRASP) and Adaptive Memory Programming (AMP) into a new GRAMPS framework for the CCP. A learning process is kept in charge of tracking information on the best components in an elite set of GRAMPS solutions. The information are strategically combined with problem-domain data to restart the construction search phase. At early stage of constructions, priorities are given to problem-domain data and progressively shifted towards generated information as the learning increases. GRAMPS is implemented with an efficient local search descent based on a restricted λ-interchange neighbourhood. Extensive experiments are reported on on a standard set of bench-marks from the literature and on a new set of large instances. The results show that GRAMPS has an efficient learning mechanism and is competitive with the existing methods in the literature.