Quantum Genetic Algorithm for Hybrid Flow Shop Scheduling Problems to Minimize Total Completion Time

This paper investigates the application of the quantum genetic algorithm (QGA) for Hybrid flow shop problems (HFSP) with the objective to minimize the total completion time. Since HFSP has shown to be NP-hard in a strong sense when the objective is to minimize the makespan in case of two stages, an efficient QGA is proposed to solve the problem. A real number representation is used to convert the Q-bit representation to job permutation for evaluating the solutions and quantum rotation gate is employed to update the population. Two different types of crossover and mutation operators are investigated to enhance the performance of QGA. The experimental results indicate that QGA is capable of producing better solutions in comparison with conventional genetic algorithm (GA) and quantum algorithm (QA).

[1]  K. S. Swarp,et al.  Unit Connuitment Solution Methodology Using Genetic Algorithm , 2002, IEEE Power Engineering Review.

[2]  Yueh-Min Huang,et al.  Proportionate flexible flow shop scheduling via a hybrid constructive genetic algorithm , 2008, Expert Syst. Appl..

[3]  Jong-Hwan Kim,et al.  Quantum-inspired evolutionary algorithms with a new termination criterion, H/sub /spl epsi// gate, and two-phase scheme , 2004, IEEE Transactions on Evolutionary Computation.

[4]  Ceyda Oguz,et al.  A Genetic Algorithm for Hybrid Flow-shop Scheduling with Multiprocessor Tasks , 2005, J. Sched..

[5]  Funda Sivrikaya-Serifoglu,et al.  Multiprocessor task scheduling in multistage hybrid flow-shops: a genetic algorithm approach , 2004, J. Oper. Res. Soc..

[6]  Shiwei Ma,et al.  A Quantum-Inspired Immune Algorithm for Hybrid Flow Shop with Makespan Criterion , 2009 .

[7]  Jatinder N. D. Gupta,et al.  Two-Stage, Hybrid Flowshop Scheduling Problem , 1988 .

[8]  Pei-Chann Chang,et al.  Two-phase sub population genetic algorithm for parallel machine-scheduling problem , 2005, Expert Syst. Appl..

[9]  Jan Karel Lenstra,et al.  PREEMPTIVE SCHEDULING IN A TWO-STAGE MULTIPROCESSOR FLOW SHOP IS NP-HARD , 1996 .

[10]  Jong-Hwan Kim,et al.  Quantum-Inspired Evolutionary Algorithms With a New Termination Criterion , H Gate , and Two-Phase Scheme , 2009 .

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  Jong-Hwan Kim,et al.  Quantum-inspired evolutionary algorithm for a class of combinatorial optimization , 2002, IEEE Trans. Evol. Comput..

[13]  Günter Rudolph,et al.  Convergence analysis of canonical genetic algorithms , 1994, IEEE Trans. Neural Networks.

[14]  Ajit Narayanan,et al.  Quantum computing for beginners , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).