单件小批MES作业计划与调度优化方法的研究
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摘要
制造执行系统(Manufacturing Execution System, MES)以作业计划与调度优化为核心,能够实现企业经营管理信息和生产控制信息的集成,解决企业管理系统与生产控制系统之间的信息断层问题。由于单件小批企业的生产环境和信息环境复杂,因此单件小批MES的体系结构应能够解决单件小批生产管理的适应性问题,其作业计划与调度优化方法应该既满足各种复杂的调度目标,又动态处理车间生产中的不确定随机事件。
本文以单件小批生产类型为研究对象,提出新的单件小批MES体系结构及其作业计划与调度优化方法,解决单件小批产品整个生产过程的优化问题,达到优化运行、控制与管理企业的经营活动和生产活动的目的。本文主要研究工作包括以下几个方面：
针对单件小批产品生产的特点,为解决单件小批产品生产对MES体系结构的适应性需求问题,以及传统的集成造成的功能重叠、数据异构和系统复杂等问题,提出单件小批MES体系结构,建立它的功能模型,确定单件小批MES与企业资源计划、车间现场控制的集成关系,以满足单件小批制造企业实际应用的需求。
为适应单件小批MES车间作业计划管理模式,解决MES的工序级作业计划制定、作业监控以及作业计划变更等问题,设计单件小批MES作业计划与调度优化策略,确保找到满足多种约束条件的、手工编制无法找到的、优化的作业计划方案,保证作业调度结果能够符合车间的实际生产活动,达到作业计划与生产实际统一的目标；并给出作业计划与调度问题的形式化描述,以及作业计划与调度优化目标。
为解决MES中作业计划与调度优化的智能性问题,以遗传算法作为基础,提出两种单件小批MES作业计划与调度静态优化算法,构成算法库,首先以最长完工时间最短为调度目标,提出作业计划与调度的改进病毒进化遗传算法,然后以平均流程时间最短为调度目标,提出作业计划与调度的单亲遗传算法,两种算法均具有良好的收敛速度和解的较优性,以满足作业计划与调度问题对多目标性的要求。
为解决MES作业计划与调度优化的敏捷性问题,提出考虑不确定因素的单件小批作业计划与调度动态算法,根据单件小批企业生产过程中大量存在不确定性等随机事件的情况,实现生产进度监控与作业计划变更,以满足作业计划与调度问题对动态随机性要求。并且,为了使得动态作业计划与调度既能够适应生产过程中的环境变化,又能够增加MES的稳定性,设计动态调度的驱动机制。
为了验证单件小批MES及其作业计划与调度优化方法的有效性,基于所提出的单件小批MES体系结构及其作业计划与调度优化方法,设计实现了单件小批MES及作业计划与调度算法,结合作业计划与调度的实际问题,进行应用验证。
Manufacturing execution system (MES) which core is job shop scheduling optimization can be used to integrate between enterprise management information and production control information to solve the problem of the information fault between them. Because production environment and information environment of the single and small batch enterprise are very complex, the architecture of MES should be able to solve the adaptivity problem of single and small batch production management, and the job shop scheduling optimization method.should meet many kinds of complex scheduling objectives and dynamically process the uncertain random events during job shop production.
With the single and small batch production as research object, a new architecture and job shop scheduling optimization method of single and small batch MES are proposed in this thesis, in order to solve the optimization problem during the whole production process and to archieve optimizing running, controlling and managing the business activities and production activities of the enterprise. The main research work includes the following aspects:
According to the characteristics of single and small batch production, a new architecture of single and small batch MES has been proposed; the functional model and the integrated relationship between MES, enterprise resource planning (ERP), and shop floor control (SFC) have been built. Furthermore, the adaptation needs of the single and small batch manufacture to the architecture of MES have been satisfied; the problems of functions overlapping, data heterogeneity, and system complexity caused by the traditional integration have been solved to meet the needs of the enterprises practical application.
For solving the problem of job shop planning, work monitoring and plan changing, the job shop scheduling optimization strategy has been proposed in order to meet the demand of job shop management flow. The job shop scheduling optimization strategy ensures that scheduling results can meet the actual production activities of job shop, and achieves a degree of reunification for work plan and actual production. Then the formal description of job shop scheduling problem and the optimization object for job shop scheduling optimization are given.
Based on the traditional genetic algorithm, two efficient and feasible job shop scheduling statical algorithms have been provided to solve the intelligence problem of job shop scheduling optimization. First, an improved virus evolutionary genetic algorithm has been provided for minimizing the maximal completion time; second, an integer coded partheno genetic algorithm has been provided for minimizing the average flow time. Both algorithms are good at convergence rate and optimum solution, and meet the multi-objective requirements on job shop scheduling problems.
Based on uncertain random events in the production of the single and small batch enterprise, a job shop scheduling dynamic algorithm by considering uncertain factors has been provided to solve the agility problem of job shop scheduling optimization. It can control product progress and adjust the plan to meet the dynamic randomness need of job shop scheduling. Then the driving mechanism combining event-driven with cycle-driven has been proposed to adapt the environment change during job shop production and to increase stability of MES.
A single and small batch MES has been designed and developed based on the architecture and the optimization method proposed in this thesis, and the job shop scheduling algorithms have been implemented and embedded into MES. According to the job shop scheduling practical application problems, the architecture of single and small batch MES and its job shop scheduling optimization method have been validated.
本文以单件小批生产类型为研究对象,提出新的单件小批MES体系结构及其作业计划与调度优化方法,解决单件小批产品整个生产过程的优化问题,达到优化运行、控制与管理企业的经营活动和生产活动的目的。本文主要研究工作包括以下几个方面：
针对单件小批产品生产的特点,为解决单件小批产品生产对MES体系结构的适应性需求问题,以及传统的集成造成的功能重叠、数据异构和系统复杂等问题,提出单件小批MES体系结构,建立它的功能模型,确定单件小批MES与企业资源计划、车间现场控制的集成关系,以满足单件小批制造企业实际应用的需求。
为适应单件小批MES车间作业计划管理模式,解决MES的工序级作业计划制定、作业监控以及作业计划变更等问题,设计单件小批MES作业计划与调度优化策略,确保找到满足多种约束条件的、手工编制无法找到的、优化的作业计划方案,保证作业调度结果能够符合车间的实际生产活动,达到作业计划与生产实际统一的目标；并给出作业计划与调度问题的形式化描述,以及作业计划与调度优化目标。
为解决MES中作业计划与调度优化的智能性问题,以遗传算法作为基础,提出两种单件小批MES作业计划与调度静态优化算法,构成算法库,首先以最长完工时间最短为调度目标,提出作业计划与调度的改进病毒进化遗传算法,然后以平均流程时间最短为调度目标,提出作业计划与调度的单亲遗传算法,两种算法均具有良好的收敛速度和解的较优性,以满足作业计划与调度问题对多目标性的要求。
为解决MES作业计划与调度优化的敏捷性问题,提出考虑不确定因素的单件小批作业计划与调度动态算法,根据单件小批企业生产过程中大量存在不确定性等随机事件的情况,实现生产进度监控与作业计划变更,以满足作业计划与调度问题对动态随机性要求。并且,为了使得动态作业计划与调度既能够适应生产过程中的环境变化,又能够增加MES的稳定性,设计动态调度的驱动机制。
为了验证单件小批MES及其作业计划与调度优化方法的有效性,基于所提出的单件小批MES体系结构及其作业计划与调度优化方法,设计实现了单件小批MES及作业计划与调度算法,结合作业计划与调度的实际问题,进行应用验证。
Manufacturing execution system (MES) which core is job shop scheduling optimization can be used to integrate between enterprise management information and production control information to solve the problem of the information fault between them. Because production environment and information environment of the single and small batch enterprise are very complex, the architecture of MES should be able to solve the adaptivity problem of single and small batch production management, and the job shop scheduling optimization method.should meet many kinds of complex scheduling objectives and dynamically process the uncertain random events during job shop production.
With the single and small batch production as research object, a new architecture and job shop scheduling optimization method of single and small batch MES are proposed in this thesis, in order to solve the optimization problem during the whole production process and to archieve optimizing running, controlling and managing the business activities and production activities of the enterprise. The main research work includes the following aspects:
According to the characteristics of single and small batch production, a new architecture of single and small batch MES has been proposed; the functional model and the integrated relationship between MES, enterprise resource planning (ERP), and shop floor control (SFC) have been built. Furthermore, the adaptation needs of the single and small batch manufacture to the architecture of MES have been satisfied; the problems of functions overlapping, data heterogeneity, and system complexity caused by the traditional integration have been solved to meet the needs of the enterprises practical application.
For solving the problem of job shop planning, work monitoring and plan changing, the job shop scheduling optimization strategy has been proposed in order to meet the demand of job shop management flow. The job shop scheduling optimization strategy ensures that scheduling results can meet the actual production activities of job shop, and achieves a degree of reunification for work plan and actual production. Then the formal description of job shop scheduling problem and the optimization object for job shop scheduling optimization are given.
Based on the traditional genetic algorithm, two efficient and feasible job shop scheduling statical algorithms have been provided to solve the intelligence problem of job shop scheduling optimization. First, an improved virus evolutionary genetic algorithm has been provided for minimizing the maximal completion time; second, an integer coded partheno genetic algorithm has been provided for minimizing the average flow time. Both algorithms are good at convergence rate and optimum solution, and meet the multi-objective requirements on job shop scheduling problems.
Based on uncertain random events in the production of the single and small batch enterprise, a job shop scheduling dynamic algorithm by considering uncertain factors has been provided to solve the agility problem of job shop scheduling optimization. It can control product progress and adjust the plan to meet the dynamic randomness need of job shop scheduling. Then the driving mechanism combining event-driven with cycle-driven has been proposed to adapt the environment change during job shop production and to increase stability of MES.
A single and small batch MES has been designed and developed based on the architecture and the optimization method proposed in this thesis, and the job shop scheduling algorithms have been implemented and embedded into MES. According to the job shop scheduling practical application problems, the architecture of single and small batch MES and its job shop scheduling optimization method have been validated.
引文
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