混合改进麻雀算法解决柔性车间调度问题

A Hybrid Improved Sparrow Algorithm for Solving Flexible Job-Shop Scheduling Problems

摘要: 针对柔性作业车间调度问题(flexible job shop scheduling problem, FJSP)，提出一种混合改进麻雀算法对其求解，旨在求得最小最大完工时间。首先，采用基于极限调度完工时间最小化的工序机器双层编码方式初始化，解决FJSP离散化问题；其次，用多种策略改进麻雀算法，在跟随者阶段采用LEVY飞行、自适应参数调整进行改进，跟随者阶段融入鲸鱼螺旋狩猎行为，并采用柯西扰动等方法提高求解质量，加大勘测搜索空间；然后，引入遗传算子加强算法跳出局部最优的能力；最后，根据标准算例数据和实际车间生产数据对算法进行仿真模拟，证明了应用混合改进麻雀算法在求解FJSP问题的可行性、优越性和高效性，助力车间的智能化管控。

Abstract: For flexible job shop scheduling problem (FJSP), a hybrid improved sparrow algorithm is proposed to solve it, aiming at minimizing the maximized completion time. Firstly, the initialization of the process machine two-layer coding method based on the minimization of the completion time of limit scheduling is used to solve the FJSP discretization problem; secondly, the sparrow algorithm is improved with the established strategy, which is improved by using LEVY flights and adaptive parameter tuning in the follower phase, and the follower phase is integrated into the whale spiral hunting behavior, and Cauchy’s perturbation and other methods are used to improve the solution quality and increase the survey search space; Then, the genetic operator is introduced to strengthen the ability of the algorithm to jump out of the local optimum; finally, the algorithm is simulated according to the standard arithmetic data and the actual workshop production data, which proves the feasibility, superiority and high efficiency of applying the hybrid improved sparrow algorithm in solving the FJSP problem, and assists the intelligent control of the workshop.