面向饲料加工的排产优化方法研究

为了提高饲料企业在成本和质量上的优势，需要采用更加科学的方法制定排产计划。首先根据饲料加工排产的特点构建了基于批量组织生产的排产模型；其次，针对布谷鸟搜索(cuckoo search，CS)算法收敛速度慢与局部搜索能力弱的问题，提出不同的改进策略形成改进CS算法求解了排产模型，改进算法运用NEH方法、Logistic混沌映射方法以及随机方法生成初始解，使用了动态改变步长的策略以平衡算法探索能力与开发能力，增加基于差分进化的交叉阶段以增强最优解的挖掘能力。采用改进CS算法，以最小化总流经时间为求解目标，在40个Taillard测试集实例和实际饲料排产数据上进行了实验，验证了改进CS算法的寻优能力。结果证明了改进CS算法在求解流水线式生产车间排产问题上的有效性。

Study on Production Scheduling Optimization Method for Feed Processing

In order to improve the cost and quality advantages of feed enterprises, more scientific methods can be used to formulate production scheduling plans. Firstly, according to the characteristics of feed processing scheduling, a scheduling model based on batch organization production is constructed. Secondly, aiming at the problems of slow convergence speed and weak local search ability of cuckoo algorithm, an improved cuckoo optimization algorithm with different improvement strategies is formed. The NEH method, the Logistic chaotic mapping method and the random method were employed to create initial solution. The strategy of dynamically changing the step size is used to balance the exploration ability and exploitation ability of the algorithm. The crossover stage based on differential evolution is added to enhance the mining ability of the optimal solution. The improved cuckoo search algorithm is used to solve 40 Taillard test problems and an actual feed scheduling problem with minimization of total flow time. The experimental results show the effectiveness of the proposed algorithm in solving flow shop scheduling problem.