多属性虚拟企业部分并行协商项目规划

在虚拟企业环境下,制定基于盟主与合作伙伴协商的项目规划不仅要考虑任务时间与工期的可行性,而且要关注工期费用。在分析虚拟企业项目协商规划的工期与费用的不确定性的基础上,提出了多属性工期一费用随机项目模型,以及工期一费用部分并行协商规划算法,以期望工期费用为优化目标,结合示例阐述其计算过程。最后针对模具生产的特点,对模具虚拟企业的项目进行仿真规划,仿真结果表明了这一方法的有效性和可靠性。     

多属性的虚拟企业串行协商项目规划

通过分析虚拟企业项目协商规划的工期与费用的不确定性，提出了工期一费用随机项目模型和工期～费用串行协商规划算法，以期望工期费用为优化目标，结合协商实例进行仿真规划，并解释其计算过程，说明了这一方法的有效性和可靠性。     

基于协商的模具虚拟企业生产项目规划

项目规划是虚拟企业建立和运行的关键环节。虚拟企业在不同行业有不同的组织和运行模式，模具虚拟企业是一种基于项目的动态联盟，项目工期规划是盟主与合作伙伴充分协商、协调一致的过程。在分析模具虚拟企业工期协商过程的不确定性因素基础上，提出串行序贯协商的协商机制，定义盟主和潜在合作伙伴的工期方案效用模型及其修正方法，提出盟主协商策略的产生方法，通过示例解释方案效用值的计算，并通过模具生产实例验证了规划方法的可行性。     

虚拟企业生产项目粗规划

针对项目驱动的虚拟企业生产信息部分共享、成员企业间约束力弱的特点 ,本文提出一种基于协商的虚拟企业生产计划与控制四层模型 :项目粗规划、项目协商规划、生产详细规划和生产作业控制。并就项目粗规划进行深入的讨论 ,分析了粗规划的工作流程和缩短项目总工期的两种工期调整方法 ,从调整范围和调整次序两个方面给出了工期调整方法的处理原则。在此基础上 ,提出了一种控制子项目工期紧迫度和项目协商难度的启发式优化算法。     

虚拟企业协同项目进度控制

研究了虚拟企业环境下协同制造项目进度控制的问题。通过分析在虚拟企业环境下项目进度控制的禾确定因素，建立了两类不确定性工期-费用离散概率数学模型，提出了赶工估测的计算方法，在此基础上，确定了以赶工方案的工期-费用可协商性为综合性能指标的再协商机制，并结合示例解释其计算迹程。菠后针对模具生产的特点，对协同制造的模具项目进度进行仿真监控，说明了这一方法的有效性和可靠性。     

面向协商的项目进度粗规则

虚拟企业的项目进度粗规划是一种面向协商的项目规划,分析了虚拟企业生产项目进度粗规划工作流及工期调整的一些处理原则,在项目协商规划有解的前提下,提出一种由盟主控制工期协商难度,缩短交货期的生产项目进度粗规划方法。     

CPC环境下的模具虚拟企业项目粗规划

研究了产品协同商务(CPC)环境下的模具虚拟企业生产项目粗规划问题。在CPC环境下,模具项目规划的制定是建立在盟主与合作伙伴协商的基础上,粗规划是模具项目规划的重要组成部分,它对后续规划过程起引导性作用,编制过程需要综合考虑安全性和有效性指标。分析了模具虚拟企业项目规划的不确定因素,提出了粗规划的优化目标,结合示例解释粗规划计算过程,最后针对模具生产的特点,提出了一种近似搜索算法,并通过生产实例验证了其可行性。     

协同制造项目进度管理

在分析虚拟企业协商调整项目规划方案的不确定性因素的基础上,建立了工期-费用离散概率模型,提出了可快速判断是否有可行解的判据准则,并结合示例解释其过程.最后,针对模具生产的特点,提出了一种基于约束满足的近似搜索算法,并通过生产实例说明了这一方法的有效性和可靠性.     

不确定环境下模具制造项目群随机调度

为确保模具按时交货,通过分析模具制造项目工期、费用与报酬的不确定性,以及项目返修频繁的特点,建立了基于离散时间马尔可夫链的模具制造项目群随机演化模型,并提出了求解该随机动态规划模型的算法框架.通过启发式策略仿真和Q学习,有效解决了"维数灾难"问题.最后结合示例阐述了该算法的执行过程,及其可行性与可靠性.     

随机生产环境下的模具制造单元资源配置

通过分析模具的生产需求情况,提出了一种模具聚类指标,应用聚类算法构造了几类典型的模具范例.通过资源静态与动态配置手段,提出了一种制造单元资源配置优化策略.针对模具生产的特点,以最小化期望工期费用为优化目标,提出了一种基于资源选择决策模型的改进型蚁群能力平衡算法,并通过生产实例,说明了该方法的可靠性和有效性.     

An agent-based evolutionary strategic negotiation for project dynamic scheduling

In this paper, an agent-based approach with a mutual influencing, many-issue, one-to-many-party, strategic negotiation model is proposed. The model concentrates on solving the dynamic scheduling problem of a distributed project for non-cooperative and self-interested participants. In this model, the self-interested activity agents possess various negotiation tactics and strategies formed by their respective owner’s subjective preference, aim to find the contract of schedule adjustment mutually acceptable to respective participant’s acquaintance while encountering conflicts over rescheduling settlement. In order to find fitting negotiation tactics and strategies that are optimally adapted for each activity agent, an evolutionary computation approach which encodes the parameters of tactics and strategies of an agent as genes in GAs is also addressed. In the final, a prototype system with a case of a distributed project for dynamic scheduling discussed in researches is simulated to validate the feasibility and applicability of the approach, and some characteristics and future works are also addressed.     

Optimisation of opportunistic maintenance of a multi-component system considering the effect of failures on quality and production schedule: A case study

For a manufacturing equipment, any unplanned breakdown during the production period results into a high production loss. To keep the manufacturing facilities in good condition, preventive maintenance is planned. However, because of limited time and availability of resources, not all the system components can be or need to be repaired/replaced during a planned opportunity. Hence, the unplanned breakdowns can also be considered as an opportunity to do the maintenance activities for other components to take the advantage of economic dependency in multi-component system. However, when the system is under maintenance, it is very conservative to take the decision of maintenance actions on the components because of limited available time and resources. For such situation, this paper consider an opportunistic maintenance model for a multi-component system to take maintenance decision with a constraint on available time and the system availability requirements. The maintenance decisions for each component involves one of the three actions namely, repair, replace or do nothing to achieve the target availability with minimum maintenance cost. The model also considers the effect of component failures on the quality of product being manufactured as well as the production schedule on the machine. The cost of rejections is considered in the total failure cost along with the maintenance and downtime costs. The production schedule delay factor is considered as a constraint for the maintenance decision to account for the effect on production schedule delay. The optimal solution for the model is obtained using three solution methodologies namely simulated annealing, genetic algorithm and sequence heuristics. Using a real-life example of high pressure die casting machine, the opportunistic maintenance approach is demonstrated and results are discussed.     

一种可重构制造系统的生产计划方法

虚拟企业是实现敏捷制造的有效途径，是一种可重构的制造系统。系统在重构过程中，各合作伙伴给出完成各自任务的时间段，制造系统则基于这些信息编排出生产计划。本文建立了这一生产计划问题的数学模型，基于该模型，提出了一个有效算法，并给出了一个简单算例。结果表明，所提出的算法可以决定解的存在性，如果解存在，则求出有效解；如果解不存在或解不可以接受，盟主可与合作伙伴协商解决问题。     

A new multi-objective optimization method for master production scheduling problems based on genetic algorithm

In an environment of global competition, the success of a manufacturing corporation is directly related to the optimization level of its processes in general, but, in particular, to how it plans and executes production. In this context, the master production schedule (MPS) is the key activity for success. In this paper, as in most industries worldwide, the creation of an MPS considers conflicting objectives, such as maximization of service levels, efficient use of resources, and minimization of inventory levels. Unfortunately, the complexity and effort demanded for the creation of a master plan grows rapidly as the production scenario increases, especially when resources are limited, which is the case for most industries. Due to such complexity, industries usually use simple heuristics implemented in spreadsheets that provide a quick plan, but can compromise efficiency and costs. Fortunately, researchers are often proposing new ideas to improve production planning, such as use of artificial intelligence-based heuristics. This work presents the development and use of genetic algorithm (GA) to MPS problems, something that does not seem to have been done so far. It proposes a new genetic algorithm structure, and describes the multi-objective fitness function used, the set of possible individual selection techniques, and the adjustment values for the crossover and mutation operators. The GA developed was applied to two manufacturing scenarios and the most important parameters for the configuration of the GA were identified. This research shows that the use of genetic algorithms is a viable technique for MPS problems; however, its applicability is still heavily dependent on the size of the manufacturing scenario.     

多项目调度问题研究

现有多项目调度研究一般都基于不切实际的假定——资源在多个项目间进行传递时不需要花费额外的时间和成本。在对传递时间进行分析的基础上,建立了考虑资源传递时间的多项目调度问题的数学模型,并针对模型设计了结合并行调度的混合遗传算法,在保证项目任务紧前关系与资源约束的条件下,能够合理优化多项目的资源分配,从而有效地缩短多项目的总工期,通过实例验证了考虑传递时间的必要性,同时实例也证明了算法的有效性。