不确定环境下模具制造多项目动态调度建模与仿真
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摘要
模具是工业生产中不可或缺的基础工艺装备,主要用于高效大批量生产工业产品中的有关零部件和制件,是制造装备业的重要组成部分。模具制造水平的高低,已经成为衡量一个国家产品制造水平高低的重要标志。伴随着全球制造业向我国转移的大趋势,广东逐渐成为世界制造业的重要基地之一,模具需求量日益增加。然而,与发达工业国家相比,我国模具工业的综合水平仍存在一定的差距,这种差距在企业生产管理方面表现得尤为明显。
模具制造属于面向资源的单件工程订货型生产方式。模具生产过程特别强调交叉与并行,自始至终存在着双向信息的传递,属于典型的项目式运作模式。模具的生产管理具有多企业、多项目共享资源,协调管理的典型需求。模具生产过程中存在许多不确定因素(如任务工期不确定、可再生资源发生故障、随机插入新订单等),这些不确定因素的出现往往导致实际生产无法遵循开始制订的基准调度计划,只有采取行之有效的预防方法和科学的动态调度与控制策略,才能优化资源配置,尽量消除不确定因素的影响,确保项目顺利进行。
本文在国家自然科学基金(50675039、50875051)和国家863计划资助项目(2006AA04Z132)的联合资助下研究不确定环境下的模具制造多项目动态调度问题。从模具制造的实际情况出发,分析了不确定因素的描述方法,并详细探讨了模具制造多项目动态调度的决策机制,以及相关调度模型的构建、仿真与求解方法。论文研究成果的应用,将有利于提高模具企业的生产管理水平,从而提升我国模具行业的整体竞争力。
论文的主要工作包括如下几个方面：
1、分析了模具的制造方式与特点,阐述了不确定因素和突发事件对模具制造多项目调度计划的影响,在此基础上构建了一种不确定环境下的模具制造多项目预测—反应式调度模型；
2、针对模具制造项目执行过程中任务工期不确定的情况,提出了一种模具制造多项目预测—反应式调度模型求解算法。首先采用启发式微粒群算法,并结合基于关键链技术的时间缓冲设置方法求取模具制造多项目调度的预测计划(基准调度计划)。在调度计划执行过程中,以任务拖期为作为反应调度的触发事件,应用基于复合最优模型的改进微粒群算法,并结合修改后的串行调度生成方案和并行调度生产方案求解反应调度模型,以缓解局部任务拖期对整体调度计划的影响；
3、针对模具制造项目执行过程中任务工期不确定和项目调度计划具有固定资源流约束的情况,提出了一种度量资源流网络稳定性的方法,并设计了一种基于优先规则的微粒群算法来构建稳定的资源流网络,在此基础上提出了一种具有固定资源流约束的模具制造多项目预测—反应式调度模型求解算法；
4、针对可再生资源发生故障与任务工期不确定这两种不确定因素同时出现对多项目调度计划的干扰问题,提出了一种模具制造多项目预测—反应式调度模型求解算法。采用生灭过程理论对可再生资源的不确定性进行了分析与建模,提出了一种基于两阶段优化的启发式算法来求解稳定的基准调度计划。根据实际情况,对常用的串行调度生成方案进行了修改,在此基础上提出了一种基于混沌微粒群算法的反应调度算法；
5、从多目标优化的角度出发,系统地研究了可随机插单、可再生资源发生故障以及任务工期不确定等多种不确定因素共同影响下的模具制造多项目动态调度问题。提出了一种基于最优解评估选取方法,并将孤立点搜索策略与精英归档策略相结合的改进多目标微粒群算法进行反应调度模型求解；
6、开发了一套《不确定环境下的模具制造多项目动态调度仿真系统》,便于设置不同的环境,采用各种不同的算法和数据,以及配置不同的算法参数进行模具制造多项目调度仿真,从而对上述调度模型的合理性与有效性进行分析与验证,同时也有利于对相关调度算法的性能进行系统地分析与比较。
Moulds and Dies (M&D), mainly used for efficient mass production of industry product parts and components, are indispensably basic craft equipment for industrial production; they serve as an essential constituent for total equipment manufacturing industry. Simply put, the level of M&D manufacturing has become an important indicator for measuring a country's product manufacturing level. Accompanied by the trend that global manufacturing industry is transferring to China, Guangdong province has been gradually becoming one of most important manufacturing bases in the world with the ever-growing demand for M&D. As opposed to the average level of M&D industry of developed industrial countries, however, the level of M&D industry of our nation, especially that in the enterprise production management area, is still lagging behind.
M&D manufacturing is generally in the single-project order form with resource-oriented feature. Regarded as a typical project-based operation model, the M&D production process places a very special emphasis on cross and parallelism; inside the process always exists the two-way information transmission between a project and another. M&D production management features typical requirements for resource pooling and management coordination by multiple enterprises and projects. In practical M&D production exist many uncertainties, such as task delaying, renewable resources breaking-down and new projects arriving at random time, making it always impossible for production managers to follow the original baseline schedule (or initial schedule). Only by taking effective preventive approaches and scientific dynamic scheduling and controlling strategies can make it possible to optimize the resource allocation, to eliminate the influence of uncertainties and to ensure the project execution.
Supported by the National Natural Science Foundation of China (Grant No.50675039, 50875051) and the National High-Tech. R&D Program of China (Grant No.2006AA04Z132), our research was focused on uncertainty-based dynamic scheduling problem for multiple M&D manufacturing projects. From the practical M&D manufacturing situation, the description methods for uncertainty factors were herein analyzed. The following two issues have been discussed in detail as well, the decision-making mechanism of dynamic scheduling for multiple M&D manufacturing projects, and the construction, simulation and solution methods for the related scheduling models. Applications of the research achievements will be advantageous to increase production management levels of M&D corporations, and to improve M&D industry whole competitiveness of our country.
The research mainly includes the following aspects.
1. The manufacturing model of M&D and its characteristics were analyzed, and uncertainties and incidents impacting on multiple M&D manufacturing projects scheduling were elaborated. A predictive-reactive scheduling model for multiple M&D manufacturing projects under uncertainties was constructed.
2. Corresponding to the fact that task delaying frequently happens in multiple M&D manufacturing projects process, an algorithm was proposed to solve the predictive-reactive scheduling model. A stable baseline schedule was constructed by setting time buffers based on critical chain technology, therefore, to eliminate the effect of task delaying as far as possible. According to the actual situation, the commonly used serial schedule generation scheme and parallel schedule generation scheme were modified. On this basis, an improved particle swarm optimization was proposed to solve the reactive scheduling model.
3. The concepts of resource flow and resource flow network were introduced in this thesis. A method of measuring the stability of resource flow network was proposed. On this basis, a particle swarm optimization based on the priority rules was put forward to construct a stable resource flow network and to simplify the solving process of the resource flow network. Considering the frequent occurrence of tasks delaying during multiple M&D manufacturing projects execution, an algorithm was proposed to solve the predictive-reactive scheduling model with fixed resource flow constraint.
4. Considering the influence of renewable resources breaking-down and task delaying on scheduling for multiple M&D manufacturing projects, an algorithm was presented to solve the predictive-reactive scheduling model. Uncertain availabilities of renewable resources were analyzed with the theory of birth-death process, and a heuristic algorithm based on two-staged optimization was proposed to solve the stable baseline schedule. According to the actual situation, the commonly used serial schedule generation scheme was further modified. On this basis, a reactive scheduling algorithm based on chaotic particle swarm optimization was proposed.
5. From multi-objective optimization perspective, a multi-objective dynamic scheduling problem for multiple M&D manufacturing projects was systematically studied with considering the co-influence of such multiple uncertainties as random arrival of new projects, breaking-down of renewable resources and tasks delaying. An improved multi-objective particle swarm optimization algorithm was proposed to solve the reactive scheduling model; based on the assessment and selection method for optimal solution, the algorithm combines both the isolated points searching strategy and the elite archiving strategy.
6. A system-simulation software of dynamic scheduling for multiple M&D manufacturing projects under uncertainties was developed, with which it is convenient to simulate the dynamic scheduling process by setting up different environments. Using different algorithms as well as a large variety of data, and configuring various parameters for related algorithms, the system simulation software can analyze and verify the rationality and the effectiveness of the scheduling models mentioned above; in the meantime, such software can be also of great value for systematically analyzing and comparing the performance of the related scheduling algorithms.
模具制造属于面向资源的单件工程订货型生产方式。模具生产过程特别强调交叉与并行,自始至终存在着双向信息的传递,属于典型的项目式运作模式。模具的生产管理具有多企业、多项目共享资源,协调管理的典型需求。模具生产过程中存在许多不确定因素(如任务工期不确定、可再生资源发生故障、随机插入新订单等),这些不确定因素的出现往往导致实际生产无法遵循开始制订的基准调度计划,只有采取行之有效的预防方法和科学的动态调度与控制策略,才能优化资源配置,尽量消除不确定因素的影响,确保项目顺利进行。
本文在国家自然科学基金(50675039、50875051)和国家863计划资助项目(2006AA04Z132)的联合资助下研究不确定环境下的模具制造多项目动态调度问题。从模具制造的实际情况出发,分析了不确定因素的描述方法,并详细探讨了模具制造多项目动态调度的决策机制,以及相关调度模型的构建、仿真与求解方法。论文研究成果的应用,将有利于提高模具企业的生产管理水平,从而提升我国模具行业的整体竞争力。
论文的主要工作包括如下几个方面：
1、分析了模具的制造方式与特点,阐述了不确定因素和突发事件对模具制造多项目调度计划的影响,在此基础上构建了一种不确定环境下的模具制造多项目预测—反应式调度模型；
2、针对模具制造项目执行过程中任务工期不确定的情况,提出了一种模具制造多项目预测—反应式调度模型求解算法。首先采用启发式微粒群算法,并结合基于关键链技术的时间缓冲设置方法求取模具制造多项目调度的预测计划(基准调度计划)。在调度计划执行过程中,以任务拖期为作为反应调度的触发事件,应用基于复合最优模型的改进微粒群算法,并结合修改后的串行调度生成方案和并行调度生产方案求解反应调度模型,以缓解局部任务拖期对整体调度计划的影响；
3、针对模具制造项目执行过程中任务工期不确定和项目调度计划具有固定资源流约束的情况,提出了一种度量资源流网络稳定性的方法,并设计了一种基于优先规则的微粒群算法来构建稳定的资源流网络,在此基础上提出了一种具有固定资源流约束的模具制造多项目预测—反应式调度模型求解算法；
4、针对可再生资源发生故障与任务工期不确定这两种不确定因素同时出现对多项目调度计划的干扰问题,提出了一种模具制造多项目预测—反应式调度模型求解算法。采用生灭过程理论对可再生资源的不确定性进行了分析与建模,提出了一种基于两阶段优化的启发式算法来求解稳定的基准调度计划。根据实际情况,对常用的串行调度生成方案进行了修改,在此基础上提出了一种基于混沌微粒群算法的反应调度算法；
5、从多目标优化的角度出发,系统地研究了可随机插单、可再生资源发生故障以及任务工期不确定等多种不确定因素共同影响下的模具制造多项目动态调度问题。提出了一种基于最优解评估选取方法,并将孤立点搜索策略与精英归档策略相结合的改进多目标微粒群算法进行反应调度模型求解；
6、开发了一套《不确定环境下的模具制造多项目动态调度仿真系统》,便于设置不同的环境,采用各种不同的算法和数据,以及配置不同的算法参数进行模具制造多项目调度仿真,从而对上述调度模型的合理性与有效性进行分析与验证,同时也有利于对相关调度算法的性能进行系统地分析与比较。
Moulds and Dies (M&D), mainly used for efficient mass production of industry product parts and components, are indispensably basic craft equipment for industrial production; they serve as an essential constituent for total equipment manufacturing industry. Simply put, the level of M&D manufacturing has become an important indicator for measuring a country's product manufacturing level. Accompanied by the trend that global manufacturing industry is transferring to China, Guangdong province has been gradually becoming one of most important manufacturing bases in the world with the ever-growing demand for M&D. As opposed to the average level of M&D industry of developed industrial countries, however, the level of M&D industry of our nation, especially that in the enterprise production management area, is still lagging behind.
M&D manufacturing is generally in the single-project order form with resource-oriented feature. Regarded as a typical project-based operation model, the M&D production process places a very special emphasis on cross and parallelism; inside the process always exists the two-way information transmission between a project and another. M&D production management features typical requirements for resource pooling and management coordination by multiple enterprises and projects. In practical M&D production exist many uncertainties, such as task delaying, renewable resources breaking-down and new projects arriving at random time, making it always impossible for production managers to follow the original baseline schedule (or initial schedule). Only by taking effective preventive approaches and scientific dynamic scheduling and controlling strategies can make it possible to optimize the resource allocation, to eliminate the influence of uncertainties and to ensure the project execution.
Supported by the National Natural Science Foundation of China (Grant No.50675039, 50875051) and the National High-Tech. R&D Program of China (Grant No.2006AA04Z132), our research was focused on uncertainty-based dynamic scheduling problem for multiple M&D manufacturing projects. From the practical M&D manufacturing situation, the description methods for uncertainty factors were herein analyzed. The following two issues have been discussed in detail as well, the decision-making mechanism of dynamic scheduling for multiple M&D manufacturing projects, and the construction, simulation and solution methods for the related scheduling models. Applications of the research achievements will be advantageous to increase production management levels of M&D corporations, and to improve M&D industry whole competitiveness of our country.
The research mainly includes the following aspects.
1. The manufacturing model of M&D and its characteristics were analyzed, and uncertainties and incidents impacting on multiple M&D manufacturing projects scheduling were elaborated. A predictive-reactive scheduling model for multiple M&D manufacturing projects under uncertainties was constructed.
2. Corresponding to the fact that task delaying frequently happens in multiple M&D manufacturing projects process, an algorithm was proposed to solve the predictive-reactive scheduling model. A stable baseline schedule was constructed by setting time buffers based on critical chain technology, therefore, to eliminate the effect of task delaying as far as possible. According to the actual situation, the commonly used serial schedule generation scheme and parallel schedule generation scheme were modified. On this basis, an improved particle swarm optimization was proposed to solve the reactive scheduling model.
3. The concepts of resource flow and resource flow network were introduced in this thesis. A method of measuring the stability of resource flow network was proposed. On this basis, a particle swarm optimization based on the priority rules was put forward to construct a stable resource flow network and to simplify the solving process of the resource flow network. Considering the frequent occurrence of tasks delaying during multiple M&D manufacturing projects execution, an algorithm was proposed to solve the predictive-reactive scheduling model with fixed resource flow constraint.
4. Considering the influence of renewable resources breaking-down and task delaying on scheduling for multiple M&D manufacturing projects, an algorithm was presented to solve the predictive-reactive scheduling model. Uncertain availabilities of renewable resources were analyzed with the theory of birth-death process, and a heuristic algorithm based on two-staged optimization was proposed to solve the stable baseline schedule. According to the actual situation, the commonly used serial schedule generation scheme was further modified. On this basis, a reactive scheduling algorithm based on chaotic particle swarm optimization was proposed.
5. From multi-objective optimization perspective, a multi-objective dynamic scheduling problem for multiple M&D manufacturing projects was systematically studied with considering the co-influence of such multiple uncertainties as random arrival of new projects, breaking-down of renewable resources and tasks delaying. An improved multi-objective particle swarm optimization algorithm was proposed to solve the reactive scheduling model; based on the assessment and selection method for optimal solution, the algorithm combines both the isolated points searching strategy and the elite archiving strategy.
6. A system-simulation software of dynamic scheduling for multiple M&D manufacturing projects under uncertainties was developed, with which it is convenient to simulate the dynamic scheduling process by setting up different environments. Using different algorithms as well as a large variety of data, and configuring various parameters for related algorithms, the system simulation software can analyze and verify the rationality and the effectiveness of the scheduling models mentioned above; in the meantime, such software can be also of great value for systematically analyzing and comparing the performance of the related scheduling algorithms.
引文
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