Solving job shop scheduling problem using a hybrid parallel micro genetic algorithm

Authors:
Rubiyah Yusof;Marzuki Khalid;Gan Teck Hui;Syafawati Md Yusof;Mohd Fauzi Othman
Affiliations:
Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia International Campus, Jalan Semarak, 54100 Kuala Lumpur, Malaysia;Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia International Campus, Jalan Semarak, 54100 Kuala Lumpur, Malaysia;Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia International Campus, Jalan Semarak, 54100 Kuala Lumpur, Malaysia;Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia International Campus, Jalan Semarak, 54100 Kuala Lumpur, Malaysia;Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia International Campus, Jalan Semarak, 54100 Kuala Lumpur, Malaysia
Venue:
Applied Soft Computing
Year:
2011

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Abstract

The effort of searching an optimal solution for scheduling problems is important for real-world industrial applications especially for mission-time critical systems. In this paper, a new hybrid parallel GA (PGA) based on a combination of asynchronous colony GA (ACGA) and autonomous immigration GA (AIGA) is employed to solve benchmark job shop scheduling problem. An autonomous function of sharing the best solution across the system is enabled through the implementation of a migration operator and a ''global mailbox''. The solution is able to minimize the makespan of the scheduling problem, as well as reduce the computation time. To further improve the computation time, micro GA which works on small population is used in this approach. The result shows that the algorithm is able to decrease the makespan considerably as compared to the conventional GA.