基于改进粒子群算法的流程工业生产调度方法研究与实现
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摘要
流程工业生产调度是流程企业生产运行的指挥中心,因此提高调度的质量和效率对提高流程企业的经济效益和社会效益将起到重要的作用。它具有生产规模大、工艺复杂多变、不确定性、非线性、多目标和多约束等特点。高效的算法研究对解决流程工业生产调度问题将起到至关重要的作用,粒子群算法(Particle Swarm Optimization,PSO)具有通用性强、不依赖于问题信息和群体搜索等特点,从提出至今已经得到了广泛的应用。本文结合PSO算法来研究流程工业生产调度问题。本文的主要工作归纳如下:
1.针对基本PSO局部搜索能力差、搜索精度低和易陷入局部最优等缺点,提出了一种改进的粒子群算法(IPSO)。IPSO给出了一种新的判断种群是否陷入局部最优的标准,当陷入局部最优时对当前最优解进行高斯变异,使种群跳出局部最优;迭代过程中采用logistic混沌变异使种群保持多样性,提高算法的全局搜索能力。
2.针对具有连续生产过程和间歇生产过程的生产车间,以日产值最大化为目标函数,建立了一个基于统一时间离散化带有限中间存储的多产品多批次静态调度模型,给出了流程工业生产调度问题的IPSO算法,并对某电化厂离子膜车间的调度实例进行了求解,分析调度结果后证明了模型的可行性和算法的有效性。
3.考虑流程工业生产过程具有动态性及不确定性,通过各个约束条件的边界控制策略,建立一个以不确定事件为驱动的反应式动态调度模型。模型根据设备故障、计划变更和价格变化等不确定性因素进行调度修改或重调度。通过某电化厂离子膜车间调度实例验证了模型的有效性。
4.基于上述理论成果,结合实际企业的需求,设计与实现了流程工业智能生产计划与调度优化系统。
最后,对论文的研究工作进行了总结,展望与分析了粒子群算法与流程工业生产调度问题的应用前景。
Process industry scheduling is the commanding center of process industry enterprise. Therefore,the improvement of the quality and the efficiency of production scheduling will play an important role in increasing the economic and social benefits of enterprises.The process industry is characteristic of large scale,complex technology,uncertainty,nonlinear, multi-objective,multi-constraint and so on.Efficient algorithms will play a crucial role in solving such problems.PSO(Particle Swarm Optimization) is one of the group intelligent optimization algorithms proposed by Kennedy and Eberhart in 1995,which has features of versatility,independent of the problem of information,capable of group search and so on.It has a wide range of applications since it has been proposed.This paper tries to combine the scheduling problem of process industry with PSO algorithm.Main works of this paper are summarized as follows:
1.Since the local search ability of PSO is not satisfactory,and its search precision is low and liable to fall into local optimum,an improved particle swarm algorithm is proposed in the paper.In IPSO first,a new criteria has been suggested in order to judge whether the populations would fall into local optimization.That is,when the particles are clustered together,the current optimal solution can make gauss mutation so that the species can leap out of local optimum. Through logistic chaotic mutation during interaction,the diversity of population is maintained, and the global search ability is improved as well.
2.The static scheduling model with the intermediate storage of continuous and batch process of combining multi-product multi-batch based on uniform time discrimination has been established,which takes day-production maximization as the object function.IPSO has been carried out to solve some problems emerging in it.Besides,such a model is applied to a practical problem of ion-exchange membrane electro-chemical plant,analysis of the results has proved that the model is applicable and the algorithm is effective.
3.Considering the fact that the process of process industry production is dynamic with many uncertainties.Therefore,a reactive dynamic scheduling model is established through various constraints of the border control strategy,which is driven by uncertain events.According to the uncertainties of equipment failures,plan shifts,price changes and so on,the model determines to modify the scheduling result or re-scheduling.The established model is applied to ion-exchange membrane electro-chemical plant and the results have showed the feasibility and effectiveness of dynamic scheduling model.
4.Based on the above theoretical results and combined with enterprise's needs,this paper has designed and realized the process industry intelligent production planning and the scheduling optimization system.
Finally,a summary is made to analyze the application prospects of particle swarm optimization and process industry scheduling problem in the future.
1.针对基本PSO局部搜索能力差、搜索精度低和易陷入局部最优等缺点,提出了一种改进的粒子群算法(IPSO)。IPSO给出了一种新的判断种群是否陷入局部最优的标准,当陷入局部最优时对当前最优解进行高斯变异,使种群跳出局部最优;迭代过程中采用logistic混沌变异使种群保持多样性,提高算法的全局搜索能力。
2.针对具有连续生产过程和间歇生产过程的生产车间,以日产值最大化为目标函数,建立了一个基于统一时间离散化带有限中间存储的多产品多批次静态调度模型,给出了流程工业生产调度问题的IPSO算法,并对某电化厂离子膜车间的调度实例进行了求解,分析调度结果后证明了模型的可行性和算法的有效性。
3.考虑流程工业生产过程具有动态性及不确定性,通过各个约束条件的边界控制策略,建立一个以不确定事件为驱动的反应式动态调度模型。模型根据设备故障、计划变更和价格变化等不确定性因素进行调度修改或重调度。通过某电化厂离子膜车间调度实例验证了模型的有效性。
4.基于上述理论成果,结合实际企业的需求,设计与实现了流程工业智能生产计划与调度优化系统。
最后,对论文的研究工作进行了总结,展望与分析了粒子群算法与流程工业生产调度问题的应用前景。
Process industry scheduling is the commanding center of process industry enterprise. Therefore,the improvement of the quality and the efficiency of production scheduling will play an important role in increasing the economic and social benefits of enterprises.The process industry is characteristic of large scale,complex technology,uncertainty,nonlinear, multi-objective,multi-constraint and so on.Efficient algorithms will play a crucial role in solving such problems.PSO(Particle Swarm Optimization) is one of the group intelligent optimization algorithms proposed by Kennedy and Eberhart in 1995,which has features of versatility,independent of the problem of information,capable of group search and so on.It has a wide range of applications since it has been proposed.This paper tries to combine the scheduling problem of process industry with PSO algorithm.Main works of this paper are summarized as follows:
1.Since the local search ability of PSO is not satisfactory,and its search precision is low and liable to fall into local optimum,an improved particle swarm algorithm is proposed in the paper.In IPSO first,a new criteria has been suggested in order to judge whether the populations would fall into local optimization.That is,when the particles are clustered together,the current optimal solution can make gauss mutation so that the species can leap out of local optimum. Through logistic chaotic mutation during interaction,the diversity of population is maintained, and the global search ability is improved as well.
2.The static scheduling model with the intermediate storage of continuous and batch process of combining multi-product multi-batch based on uniform time discrimination has been established,which takes day-production maximization as the object function.IPSO has been carried out to solve some problems emerging in it.Besides,such a model is applied to a practical problem of ion-exchange membrane electro-chemical plant,analysis of the results has proved that the model is applicable and the algorithm is effective.
3.Considering the fact that the process of process industry production is dynamic with many uncertainties.Therefore,a reactive dynamic scheduling model is established through various constraints of the border control strategy,which is driven by uncertain events.According to the uncertainties of equipment failures,plan shifts,price changes and so on,the model determines to modify the scheduling result or re-scheduling.The established model is applied to ion-exchange membrane electro-chemical plant and the results have showed the feasibility and effectiveness of dynamic scheduling model.
4.Based on the above theoretical results and combined with enterprise's needs,this paper has designed and realized the process industry intelligent production planning and the scheduling optimization system.
Finally,a summary is made to analyze the application prospects of particle swarm optimization and process industry scheduling problem in the future.
引文
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