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Title: Hybridised ant colony optimisation for job shop problem
Keywords: ant, colony, optimisation, job, shop, scheduling
Issue Date: 2-Jun-2006
Citation: FOO SIANG LYN (2006-06-02). Hybridised ant colony optimisation for job shop problem. ScholarBank@NUS Repository.
Abstract: This thesis addresses the adaptation, hybridisation and application of a metaheuristic, Ant Colony Optimisation (ACO), to the Job Shop Problem (JSP). The objective is to minimise the makespan of JSP.In this thesis, a more superior ACO pheromone model is proposed to eliminate the negative bias in the search that is found in existing pheromone models. The incorporation of active/non-delay/parameterised schedule generation and local search phase in ACO further intensifies the search. The hybridisation of ACO with Genetic Algorithms (GA) presents a potential means to further exploit the power of recombination where the best solutions generated by implicit recombination via a distribution of antsa?? pheromone trails, are directly recombined by genetic operators to obtained improved solutions.A computational experiment is performed on the proposed pheromone model to verify its learning capability. The performance of the hybridised ACO is also computationally tested on 2 sets of intensely-researched JSP benchmark problems.
Appears in Collections:Master's Theses (Open)

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