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|Title:||A Hybrid Particle Swarm and Ant Colony Optimizer for Multi-attribute Partnership Selection in Virtual Enterprises|
|Keywords:||Ant colony optimization|
Fuzzy analytical hierarchy process
Multi-attribute partnership selection
Particle swarm optimization
|Citation:||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|
|Appears in Collections:||Staff Publications|
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