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    • 4. 发明申请
    • System and method for production planning utilizing on-line state-space planning
    • 使用在线状态空间规划的生产计划的系统和方法
    • US20050278303A1
    • 2005-12-15
    • US10855936
    • 2004-05-27
    • Wheeler RumlMarkus Fromherz
    • Wheeler RumlMarkus Fromherz
    • G06F7/00
    • G06Q10/06Y10S707/99933Y10S707/99934Y10S707/99935
    • A combinatorial search method implemented in a computer control system utilizes on-line state-space planning of operations for multi-step production processes. The planner considers various possible combinations of actions, searching for one that correctly transforms the initial state of the object into the specified desired final state. Each combination of actions the planner considers is called a search node, with each node containing a plan representing a series of actions of various machines on a single object and also containing the predicted state of the object with those actions applied either forward or backward. The method includes determining which of the search nodes to extend further at each search iteration and if the object state in the chosen search node conforms to the desired state of the object, or whether actions should be added to the node's plan. Actions that are applicable to the chosen node's object state are selected, transformations are applied to the attributes, and the resulting plan is returned to the system.
    • 在计算机控制系统中实现的组合搜索方法利用多步骤生产过程的在线状态空间规划操作。 计划员考虑各种可能的动作组合,搜索正确地将对象的初始状态转换为指定的期望最终状态的组合。 计划者认为的每个动作组合称为搜索节点,每个节点包含一个计划,该计划表示单个对象上各种机器的一系列动作,并且还包含具有向前或向后应用的动作的预测状态。 该方法包括确定哪些搜索节点在每个搜索迭代中进一步扩展,以及如果所选择的搜索节点中的对象状态符合对象的期望状态,或者是否应该将动作添加到节点的计划中。 选择适用于所选节点的对象状态的动作,将变换应用于属性,并将生成的计划返回给系统。
    • 6. 发明授权
    • System and method for manufacturing system design and shop scheduling using network flow modeling
    • 使用网络流建模制造系统设计和车间调度的系统和方法
    • US08407077B2
    • 2013-03-26
    • US11364685
    • 2006-02-28
    • Haitham Ali HindiWheeler Ruml
    • Haitham Ali HindiWheeler Ruml
    • G06Q10/00
    • G05B19/41885G06Q10/04G06Q50/04Y02P90/30
    • A method and tool is provided to obtain an optimistic estimate or exact optimal value of an operational parameter for a realistic system model under investigation. The model includes components and paths arranged to process continuous or discrete commodities. The system could be a model of a manufacturing system with different machines processing multiple job types, with different sequences of operations at different stages. Constraints are applied to the abstracted network flow model, and a plurality of steady state network flows are performed. and combined to captures the transformation of the commodities from a first state to a final output state. The optimistic estimate of the realistic system model under investigation is then returned through use of a general purpose or custom solver. The method can be used to perform tradeoff studies between machine allocations, job mixes, operating costs, reliability and throughput, or to speed up scheduling and machine control.
    • 提供了一种方法和工具,以获得正在调查的现实系统模型的操作参数的乐观估计或精确最优值。 该模型包括用于处理连续或离散商品的组件和路径。 该系统可以是具有处理多种作业类型的不同机器的制造系统的模型,在不同阶段具有不同的操作序列。 约束应用于抽象网络流模型,并执行多个稳态网络流。 并结合起来,将商品从第一个国家转变为最终产出状态。 然后通过使用通用或定制求解器返回对现实系统模型的乐观估计。 该方法可用于在机器分配,作业混合,运行成本,可靠性和吞吐量之间进行权衡研究,或加快调度和机器控制。
    • 7. 发明申请
    • AI PLANNING BASED QUASI-MONTECARLO SIMULATION METHOD FOR PROBABILISTIC PLANNING
    • 用于概率规划的基于AI规划的QUASI-MONTECARLO模拟方法
    • US20110238614A1
    • 2011-09-29
    • US12748686
    • 2010-03-29
    • Sungwook YoonWheeler RumlMinh Binh Do
    • Sungwook YoonWheeler RumlMinh Binh Do
    • G06N5/02
    • G06N99/005
    • A computer-based method and system for AI planning based quasi-Monte Carlo simulation for probabilistic planning are provided. The method includes generating a set of possible actions for an initial state, generating a set of sample future outcomes, generating solutions for each of the sample future outcomes, using an AI planner, generating a set of future outcome solutions that are low probability and high-impact, combining the solutions generated from each of the sample future outcomes with the future outcome solutions generated by the AI Planner into an aggregated set of future outcome solutions, analyzing the aggregated set of future outcome solutions, selecting a best action based at least partially on the analysis of the aggregated set of future outcome solutions, and outputting the selected best action to computer memory.
    • 提供了一种基于计算机的方法和系统,用于基于AI规划的准蒙特卡罗模拟进行概率规划。 该方法包括为初始状态生成一组可能的动作,生成一组样本未来结果,使用AI计划器为每个样本未来结果生成解决方案,生成一组低概率和高的未来结果解决方案 将每个样本未来成果产生的解决方案与AI计划员生成的未来成果解决方案结合到未来成果解决方案的集合集中,分析未来成果解决方案的集合集,至少部分地选择最佳行动 对未来成果解决方案的集合进行分析,并将选定的最佳动作输出到计算机内存。
    • 9. 发明申请
    • BOUNDED SUB-OPTIMAL PROBLEM SOLVING
    • 边界子优化问题解决
    • US20090144310A1
    • 2009-06-04
    • US11948265
    • 2007-11-30
    • Wheeler Ruml
    • Wheeler Ruml
    • G06F17/30
    • G06F17/30961
    • A data structure is described that comprises a balanced binary tree and a binary heap, which may be utilized for combinatorial searching algorithms. For instance, solutions for performing a task, such as a print job or the like, are associated with nodes that are utilized to generate the data structure. Each node is associated with a quality indicator that describes a most optimal solution that may be reached through the node when traversing the binary tree. The binary heap is generated from a subset of the nodes in the tree, wherein each node in the subset has a quality indicator value that is within a predefined range of a best known solution quality. The binary heap is sorted according to a search effort indicator value for each node, where nodes that are more easily reached in the tree are placed higher in the heap to facilitate rapid identification.
    • 描述了包括平衡二叉树和二进制堆的数据结构,其可以用于组合搜索算法。 例如,用于执行诸如打印作业等的任务的解决方案与用于生成数据结构的节点相关联。 每个节点都与一个质量指标相关联,该指标描述了遍历二叉树时通过节点可以达到的最佳解决方案。 该二进制堆是从树中的一个节点的子集生成的,其中该子集中的每个节点具有在最佳已知解决方案质量的预定范围内的质量指标值。 根据每个节点的搜索工作指标值对二进制堆进行排序,其中在树中更容易到达的节点放置在堆中较高的位置以便于快速识别。
    • 10. 发明申请
    • Model-based planning using query-based component executable instructions
    • 基于模型的规划使用基于查询的组件可执行指令
    • US20080300708A1
    • 2008-12-04
    • US11807478
    • 2007-05-29
    • Wheeler RumlRobert M. LothusMinh Binh Do
    • Wheeler RumlRobert M. LothusMinh Binh Do
    • G06F19/00
    • G06Q10/06G06F8/30Y02P90/20
    • A method for planning machine control for a system includes determining one or more capabilities and one or more capability constraints for each component used to execute a plan for processing a job by the system. The plan is incrementally constructed based on the components, the one or more capabilities, and the one or more constraints. One or more sets of executable instructions are queried with incremental portions of the plan, wherein each set of executable instructions is associated with a different one of the components and represents the actions that are performed by its corresponding component, each incremental portion includes actions that are to be performed by its corresponding component, and each set of executable instructions executes the incremental portion it received. Upon receiving confirmation from each of the queried sets of executable instructions that indicates each of the components is able to perform the actions in the incremental portions, a final plan for processing the job is generated.
    • 用于规划系统的机器控制的方法包括确定用于由系统执行处理作业的计划的每个组件的一个或多个能力和一个或多个能力约束。 该计划是基于组件,一个或多个功能以及一个或多个约束来逐步构建的。 通过计划的增量部分来查询一组或多组可执行指令,其中每组可执行指令与组件中的不同组件相关联,并表示其对应组件执行的动作,每个增量部分包括 由其对应的组件执行,并且每组可执行指令执行其接收的增量部分。 在从每个查询的可执行指令集中接收到指示每个组件能够执行增量部分中的动作的确认时,生成用于处理作业的最终计划。