Research on Flexible Job Shop Rescheduling Problem Based on Improved Grey Wolf Algorithm

Abstract

Purposes In actual processing of factories, the varying health status of each machine can cause machine failures and subsequently affect the processing and construction time. This article focuses on the problem of flexible job shops with machine disturbances. In order to reduce the impact of machine failures on production planning, a rescheduling model for machine disturbances and breakpoint finding is proposed. A mathematical model is constructed with the optimization objectives of the minimum processing time and the minimum total delay time. Methods The improved grey wolf optimization algorithm (GWO-GA) is proposed as a global optimization search algorithm for solution. In order to improve the convergence rate of its gray wolf algorithm, the work piece coding iteration is introduced with an adaptive operator be added, and the POX crossover of genetic algorithm is used to iterate the machine coding. Findings The validation is conducted on the solid wood workshop data of piano manufacturing enterprises, and the results show that compared with genetic algorithm, NSGA, and PSO-GA, the improved grey wolf algorithm has advantages of high efficiency, good accuracy, and good practical value in solving this scheduling problem.

Keywords

• flexible workshop
• grey wolf algorithm
• machine disturbance
• reschedule