多品种小批量生产模式下基于约束规划的生产调度方法研究

Nowadays, characteristic and variable demands of customers have gradually made the production mode shift from high-volume and repetitive to high-mix and low-volume. The complexity, uncertainty and turbulence of production process in high-mix and low-volume environment have brought great challenges to the production scheduling. The constraint model and optimizaton algorithm based on constraint programming method for production scheduling problem in high-mix and low-volume production will be investigated in this project: (1)the constraint model for the realistic problem will be established based on the analysis of the production characteristics in high-mix and low-volume environment. (2) the optimization algorithm will be designed. Firstly, a domain-analysis based heuristic rule will be present to improve the solution speed with the integration of constraint programming and mathematical programming. Then, the combination of constraint propagation and search strategy will be analysised based on DOE method, to find their scope of application and improve the stability of the algorithm. Finally, fuzzy theory will be applied to solve the fuzzy constraint optimized problem under uncertain environment and rescheduling strategy and optimization algorithm will be designed under dynamic environment. The production scheduling system for high-mix and low-volum enviromnet based on above theory and technique will be developed. With the integration of such system with enterprise’s information system (such as APS, MES system), the production planning and control problem which majority of enterprises are faced can be solved. As a result, the production management level of enterprises will be improved and the competitiveness of enterprises will be enhanced.

随着客户需求的不断个性化和多样化，制造模式从传统的大批量、重复型生产逐渐转变为多品种小批量生产，其生产过程的复杂性、不确定性及动态性给生产调度带来巨大挑战。本项目研究多品种小批量生产环境下基于约束规划的生产调度模型与算法：（1）通过对多品种小批量生产环境典型制造行业的生产特征分析，建立面向现实调度问题的约束模型。（2）在优化算法设计方面，提出基于域特征分析的启发式搜索方法，并将约束规划与数学规划的优势结合，提高求解速度；运用实验设计的方法分析约束传播与搜索技术的组合策略，寻找其适用范围，提高求解算法的稳定性；针对动态不确定的生产环境，结合模糊理论求解模糊约束优化问题，并提出重调度的策略，增强求解算法的实用性。基于上述理论与技术方法开发出适合于多品种小批量生产企业的生产调度系统，并与其生产信息系统（如APS、MES系统）结合，可望改善多品种小批量生产企业的生产管理水平，提升企业的竞争力。

随着客户需求的不断个性化和多样化，制造模式从传统的大批量、重复型生产逐渐转变为多品种小批量生产。品种的频繁变化导致车间布置需要动态调整，本项目研究了基于改进遗传算法的动态车间布局问题。生产过程的复杂性、不确定性及动态性给生产调度带来巨大挑战，本项目研究了多品种小批量生产环境下生产调度的模型与算法。针对多品种小批量生产模式下的复杂性，研究了带有顺序依赖切换时间的单机调度问题及多条工艺路线可选择的装配线平衡与调度问题；针对加工时间和交货期窗口的不确定性，研究了不确定条件下基于遗传算法的作业车间调度问题；针对生产实际中的动态因素，研究了动态环境下基于约束规划的车间重调度问题。本项目基于约束规划、数学规划、模糊理论等相关技术，考虑生产实际中的动态性和不确定性，构建系统模型；结合Benders分解法、遗传算法等方法设计高效的求解算法，并通过大量算例验证表明，本项目所建立的模型及算法具有明显的改进效果。本项目的研究成果应用于多品种小批量制造型企业，促进了企业布局和生产调度决策的科学性和有效性。