An Adaptive Population-based Iterative Greedy Algorithm for Optimizing the Maximum Completion Time of Hybrid Flow Shop

Hybrid flow-shop scheduling problem, HFSP is the most common scheduling problem in actual production, the improvement and innovation of its intelligent optimization algorithm has important research value and practical significance. In this paper, we propose an adaptive population-based iterated greedy algorithm (SIGA) to solve the objective function of maximum completion time in production scheduling. Firstly, the NEH (Nawaz-Enscore-Ham) algorithm is used to improve the quality of the initial population; secondly, the destruction and construction operations of the population iterative greedy algorithm are applied to further optimize the population and use the disturbance factor to achieve the adaptive nature of the algorithm to the arithmetic cases; finally, an optimization rate of 86.6% is experimentally derived to obtain a smaller maximum completion time.