Please use this identifier to cite or link to this item: https://doi.org/10.1002/9781118161883.ch13
Title: A Hybrid Particle Swarm and Ant Colony Optimizer for Multi-attribute Partnership Selection in Virtual Enterprises
Authors: Niu, S.H.
Ong, S.K. 
Nee, A.Y.C.
Keywords: Ant colony optimization
Fuzzy analytical hierarchy process
Multi-attribute partnership selection
Particle swarm optimization
Virtual enterprises
Issue Date: 22-Aug-2011
Source: Niu, S.H.,Ong, S.K.,Nee, A.Y.C. (2011-08-22). A Hybrid Particle Swarm and Ant Colony Optimizer for Multi-attribute Partnership Selection in Virtual Enterprises. Evolutionary Computing in Advanced Manufacturing : 289-326. ScholarBank@NUS Repository. https://doi.org/10.1002/9781118161883.ch13
Abstract: With an increasing level of global competition, enterprises would need to focus on their core strengths for survival. Virtual enterprise (VE) is an important form of enterprise alliance to cope with global competition and mass customization. In this form of alliance, the selection of the right partners is crucial. In this research, a mathematical model based on Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) is presented, and five factors, namely, cost, time, quality, risk, and reputation are considered in this model. A hybrid PSO-ACO algorithm is proposed to obtain the optimal solution for partner selection for a VE. The fuzzy analytical hierarchy process is adopted to resolve the subjective and imprecise information, as well as the vague preferences in the partner selection process. © 2011 Scrivener Publishing LLC. All rights reserved.
Source Title: Evolutionary Computing in Advanced Manufacturing
URI: http://scholarbank.nus.edu.sg/handle/10635/67821
ISBN: 9780470639245
DOI: 10.1002/9781118161883.ch13
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