A Hybrid Approach Based on Artificial Neural Network(ANN) and Differential Evolution(DE) for Job-shop Scheduling Problem

摘要：

In this paper, we proposed a new hybrid approach, combining ANN and DE(Differential Evolution), for job-shop scheduling. Job-shop scheduling can be decomposed into a constraint satisfactory part and an optimization part for a specified scheduling objective. For this, an NN and DE-based hybrid scheduling approach is proposed in this paper. First, several specific types of neuron are designed to describe these processing constraints, detecting whether constraints are satisfied and resolving the conflicts by their feedback adjustments. Constructed with these neurons, the constraint neural network (CNN) can generate a feasible solution for the JSSP. CNN here corresponds to the constraint satisfactory part. A gradient search algorithm can be applied to guide CNN operations if an optimal solution needs to be found at a fixed sequence. For sequence optimization, a DE is employed. Through many simulation experiments and practical applications, it is shown that the approach can be used to model real production scheduling problems and to efficiently find an optimal solution. The hybrid approach is an ideal combination of the constraint analysis and the optimization scheduling method.

关键词： Job shop scheduling Artificial Neural Network Differential Evolution Constraint neural network

作者: Fuqing Zhao Jianhua Zou Yahong Yang

作者单位: Systems Engineering Institute, Xian Jiaotong University, Xian 710049,China School of Computer and Systems Engineering Institute, Xian Jiaotong University, Xian 710049,China State Key Laboratory fo School of Computer and Communication, Lanzhou University of Technology,Lanzhou 730050,China

会议类型: 国际会议

会议名称: 2010 International Conference on Advanced Mechanical Engineering(2010年先进机械工程国际学术会议 AME 2010)

会议地点: 洛阳

会议语种:英文

页码: 754-757

在线出版日期: 2010-09-04（万方平台首次上网日期，不代表论文的发表时间）