Scheduling of Dynamic Multi-Objective Flexible Enterprise Job-Shop Problem Based on Hybrid QPSO

Abstract

In view of the importance of flexible job-shop scheduling problem (FJSP) in actual production, this paper constructs a mathematical model of fuzzy FJSP and then proposes a mixed quantum algorithm based on local optimization strategy and improved optimization rotation angle. For improving the production process, a double chain coding method was designed with two gene chains, which respectively represent the machine selection and the process sequencing. Next, the hybrid quantum particle swarm optimization (QPSO) was introduced to ensure the scheduling efficiency. Finally, the prototype system of the proposed strategy was simulated by using some actual examples. The results show that the proposed algorithm can quickly form an adjusted plan that has minimal difference from the original plan. INDEX TERMS Flexible job-shop scheduling, quantum particle swarm optimization, quantum gate rotation angle, double chain quantum coding.

A QPSO algorithm based on hierarchical weight and its application in cloud computing task scheduling

“…It can be seen that it is the average of the local optimal value of each particle position, which determines the update of particle position. In the calculation process of mb, the weight of the local optimal value pb i (t) of each particle is the same, as shown in formula (12), the proportion of each particle's position in the calculation of mb is 1, that is, each particle has the same influence on the final average optimal position mb decision. This is not in line with the group intelligent decision-making strategy.…”

“…The algorithm has good robustness and convergence. In order to improve the evolution of quantum individuals and the ability to converge to the optimal solution of the QPSO algorithm, Chen W proposed a mixed quantum algorithm based on local optimization strategy and improved optimization rotation angle in reference [12].…”

A multipopulation evolutionary framework with Steffensen’s method for dynamic multiobjective optimization problems

“…In Eq. (11), F(x i , t) is the objective value of x i in the tth environment, F(x i , t) − F(x i , t) is the Euclidean distance between F(x i , t) and F(x i , t). P * t is a set of points which are evenly distributed along the ideal PF in the tth environment, and |P * t | is the number of the members in P * t .…”

“…With multiple conflicting objectives, multiobjective optimization problems (MOPs) [1] have been successfully solved by various evolutionary algorithms (EAs), such as NSGA-II [2], SPEA2 [3], MOPSO [4], MOEA/D [5], ACO [6], and so forth. Since lots of real-world MOPs need to be optimized in dynamic environments [7][8][9][10][11], how to extend multiobjective evolutionary algorithms (MOEAs) to solve dynamic multiobjective optimization problems (DMOPs) has attracted more and more attention. For static MOEAs, the goal is to find accurate and well-distributed Pareto-optimal fronts (PFs).…”

Research on Adaptive Job Shop Scheduling Problems Based on Dueling Double DQN

“…In recent years, the adaptive production scheduling problem with an uncertain environment has been an active research area [27][28][29][30][31][32]. Yang and Wang [33] presented a new adaptive neural network and heuristics hybrid approach for job-shop scheduling.…”