A Quantum-inspired Iterated Greedy algorithm for permutation flowshops in a collaborative manufacturing environment
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.
quantum-inspired iterated greedy algorithm permutation flowshops collaborative manufacturing total flowtime