A Hybrid Quantum Estimation of Distribution Algorithm (Q-EDA) for Flow-Shop Scheduling

Intrinsically, the Permutation Flow-Shop Scheduling Problem (PFSSP) is a typical combinatorial optimization problem. It encompasses a strong scientific and engineering background and remains a NP-hard problem over decades. Scheduling and sequencing have played a vital role and had massive applications in modern industries and manufacturing systems. Therefore in order to improve and enhance the performance and efficiency of industrial manufacturing systems in present competitive era, it is worthwhile to develop effective scheduling techniques and approaches. In this paper, a hybrid approach is proposed which is based on standard Quantum Genetic Algorithm (QGA) and Estimation of Distribution Algorithm (EDA), aiming at permutation flow-shop scheduling problems (PFSSP). The quantum population is merged with population produced by EDA with a comparative criterion to ensure that the best individual will remain from both populations. The EDA is integrated with standard QGA to produced fitter populations and guide QGA to find promising solution space. Utilizing the advantages of both algorithms, a faster and efficient algorithm is developed, which has produced better results than previous similar approaches for medium scale problems.