摘要
制造过程生产与能耗的协同优化是目前智能优化制造的热点研究问题之一,为解决离散制造过程调度优化问题,建立了以加工时间最短和能耗最低为目标的生产与能耗协同优化调度模型;提出了一种基于自适应变异、交叉概率因子的改进差分进化算法,求解优化调度问题;通过建立基于操作的编码方式,采用升序排序规则实现了连续算法在离散优化调度问题中的应用;通过仿真验证了该算法的有效性,并对比了粒子群算法、遗传算法与改进算法的性能。结果表明通过该算法得到的目标解明显优于另外两种算法,验证了算法的优越性。
The collaborative optimization of manufacturing process and energy consumption is one of the hot research issues in intelligent optimization manufacturing. To solve the scheduling optimization problem of discrete manufacturing process, a coordinated scheduling optimization of production and energy consumption with the shortest processing time and lowest energy consumption is established. The model proposes an improved differential evolution algorithm based on adaptive mutation and crossover probability factor to solve the optimal scheduling problem. By establishing the operation-based coding method, the application of continuous algorithm in discrete optimization scheduling problem is realized by using ascending ordering rules. The effectiveness of the proposed algorithm is verified by simulation, and the performances of particle swarm optimization algorithm, genetic algorithm and the improved algorithm are compared. The results show that the target solution obtained by this algorithm is significantly better than the other two algorithms, which verifies the superiority of the algorithm.
作者
陈文杰
王艳
Chen Wenjie;Wang Yan(Engineering Research Center of Interact of Things Technology Applications Ministry of Education,Jiangnan University,Wuxi 214122,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2018年第11期4367-4374,4386,共9页
Journal of System Simulation
基金
国家自然科学基金(61572238)
江苏省杰出青年基金(BK20160001)
关键词
协同优化调度
差分进化算法
自适应算子
离散制造过程
collaborative optimization scheduling
differential evolution algorithm
self-adaptive operator
discrete manufacturing process
