Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/23662
Title: Dynamic Job Shop Scheduling Using Ant Colony Optimization Algorithm Based On A Multi-Agent System
Authors: ZHOU RONG
Keywords: Dynamic job shop scheduling, ant colony optimization algorithm, multi-agent system
Issue Date: 26-Feb-2008
Source: ZHOU RONG (2008-02-26). Dynamic Job Shop Scheduling Using Ant Colony Optimization Algorithm Based On A Multi-Agent System. ScholarBank@NUS Repository.
Abstract: Ant Colony Optimization (ACO) algorithm is applied to a series of dynamic job shop scheduling problems (DJSSPs) for its unique property of simulating the optimization mechanism of real-world forage ants, which dynamically optimize the routes between their nest and a food source. The experimental results show that ACO can perform effectively in the given DJSSPs; the adaptation mechanism of ACO can significantly improve its overall performance for DJSSPs with proper dynamism; increasing the sizes of the minimal number of iterations and the ants per iteration does not necessarily improve the overall performance; finally, ACO can outperform several common dispatching rules in certain domains defined by machine utilization, variation of processing times, and performance measures. Experiments are carried out on a test-bed simulating a generic job shop as a discrete event system, which is then implemented as a multi-agent system. The steady-state performance of ACO is statistically analyzed.
URI: http://scholarbank.nus.edu.sg/handle/10635/23662
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