Optimization of Dynamic and Multi-Objective Flexible Job-Shop Scheduling Based on Parallel Hybrid Algorithm

This paper aims to develop a dynamic, real-time scheduling strategy under interference that can minimize the negative impact of interference on production scheduling without sacrificing the production efficiency. Taking the minimal cost and makespan as the objectives of the optimization function, the author put forward a parallel hybrid optimization algorithm for production rescheduling under interference, aiming to strike a balance between processing cost and scheduling disturbance. The benchmark test results show that the proposed algorithm achieved better accuracy than the NSGA-II and the AMOSA, and its accuracy has nothing to do with the distribution shape of the objective function or the continuity of the interference. In other words, the proposed algorithm enjoys strong computing stability. In the simulation tests, the proposed algorithm reached the global convergence state before reaching the maximum runtime, and consumed less time than the contrastive algorithms under the same problem scale. The research findings shed new light on the optimal scheduling of multi-objective FJSP under disturbance.