A Quantum-inspired Iterated Greedy algorithm for permutation flowshops in a collaborative manufacturing environment

Abstract

In this study, an effective Quantum-inspired Iterated Greedy algorithm (QIG) is proposed for permutation flowshops, which is the foundation for solving the problems with uncertainties in a collaborative manufacturing environment. A hybrid representation is developed to construct a Q-job by combining a job with a Q-bit. Q-Job permutations represent solutions, which can be evaluated directly. Hence, no representative conversion is needed, and the efficiency is enhanced. Based on Particle Swarm Optimisation, a new rotation gate is investigated to dynamically update Q-bits, so that the perturbation strength is modified adaptively. Experimental results show that the proposed rotation gate is effective and QIG significantly outperforms other existing algorithms for the considered problem.

Keywords:

• Quantum-inspired Iterated Greedy algorithm
• permutation flowshops
• collaborative manufacturing
• total flowtime

Acknowledgement

This work is supported by the National Natural Science Foundation of China (Grants Nos. 60973073 and 60873236), the National High Technology Research and Development Programme of China (863 programme, Grant No. 2008AA04Z103), the Research and Innovation Programme of JiangSu (Grant No. CX09B_053Z) and the Scientific Research Foundation of Graduate School of Southeast University (Grant No. YBJJ0930).