An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

Flexible job shop scheduling problem (FJSP) is the core decision-making problem of intelligent manufacturing production management. The Harris hawk optimization (HHO) algorithm, as a typical metaheuristic algorithm, has been widely employed to solve scheduling problems. However, HHO suffers from premature convergence when solving NP-hard problems. Therefore, this paper proposes an improved HHO algorithm (GNHHO) to solve the FJSP. GNHHO introduces an elitism strategy, a chaotic mechanism, a nonlinear escaping energy update strategy, and a Gaussian random walk strategy to prevent premature convergence. A flexible job shop scheduling model is constructed, and the static and dynamic FJSP is investigated to minimize the makespan. This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP. To verify the effectiveness of GNHHO, this study tests it in 23 benchmark functions, 10 standard job shop scheduling problems (JSPs), and 5 standard FJSPs. Besides, this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP. The optimized scheduling scheme demonstrates significant improvements in makespan, with an advancement of 28.16% for static scheduling and 35.63% for dynamic scheduling. Moreover, it achieves an average increase of 21.50% in the on-time order delivery rate. The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms.