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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 3064724, 13 pages
Research Article

A Hybrid Quantum Evolutionary Algorithm with Improved Decoding Scheme for a Robotic Flow Shop Scheduling Problem

1School of Management, Xi’an University of Science and Technology, Xi’an 710054, China
2Université Bourgogne Franche-Comté, UTBM, OPERA, 90100 Belfort, France

Correspondence should be addressed to Weidong Lei; nc.ude.tsux@gnodiewiel

Received 8 October 2016; Accepted 28 February 2017; Published 16 April 2017

Academic Editor: Calogero Orlando

Copyright © 2017 Weidong Lei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We aim at solving the cyclic scheduling problem with a single robot and flexible processing times in a robotic flow shop, which is a well-known optimization problem in advanced manufacturing systems. The objective of the problem is to find an optimal robot move sequence such that the throughput rate is maximized. We propose a hybrid algorithm based on the Quantum-Inspired Evolutionary Algorithm (QEA) and genetic operators for solving the problem. The algorithm integrates three different decoding strategies to convert quantum individuals into robot move sequences. The Q-gate is applied to update the states of Q-bits in each individual. Besides, crossover and mutation operators with adaptive probabilities are used to increase the population diversity. A repairing procedure is proposed to deal with infeasible individuals. Comparison results on both benchmark and randomly generated instances demonstrate that the proposed algorithm is more effective in solving the studied problem in terms of solution quality and computational time.