+设计-巷道式自动化立体车库升降部分毕业设计
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温州大学瓯江学院WENZHOU UNIVERSITY OUJIANG COLLEGE本科毕业设计（论文）外 文 翻 译专 业机械工程及自动化班级07机自本三学生姓名周建鹏学号07202063316指导教师马光职 称教授温州大学瓯江学院教务部制一种来控制自动化仓储系统的方法Francesco Amato，Francesco Basile, Ciro Carbone, Pasquale Chiacchio摘要：本文的目的是研究一个控制算法的自动化仓库存储系统。一般来说，一个控制算法的实现需要三个初步的步骤：开发一个可靠的模型；根据一些优化准则设计控制程序；对这些控制程序的验证。至于造型,是一种新水平的控制体系结构,即一个优化系统,介绍进行实时优化从而简化低级的控制和改善的整体性能。在此背景下，我们对整个仓库模型的作用进行了讨论，并决定建立这样一个模型使用彩色时间定时网的框架，在控制方面，我们提出了两种控制算法，分别为在机架的位置得到简化的连续性假设，优化的起重机（在仓库的过道移动）和穿梭在一条直线放在线移动（作业的运作之间的走道和采摘/加气站区）。为了评估所提出的控制算法的性能，我们定义了三个不同的费用指数。在广泛的仿真模型上进行验证，以证明所提出的控制算法的有效性。为进一步验证该算法，在实验台上还考虑了考虑通信延迟和计算时间来进行模拟。最后,该建筑和控制算法应用于工厂里。关键字：有色时间Petri网；仓库自动化；工厂自动化；优化在过去十五年来，仓库系统的规划和控制的最优策略已经被我们找到了。用一个完全自动化的方式实现复杂的业务挑战对高速成长的现代计算机技术和推广是一种挑战。规划涉及高层次的决策，如分配到商品的存放地点（随机，分类存储，相关产品聚类政策）（范登堡空军基地，1999年）或者是仓库系统本身的设计。所谓的调度控制包括最优排序和调度的存储和检索。我们指的是普通仓库架构的通道数目，由一台起重机，梭，采摘/加气位置和输入/输出缓冲器组成（见图1）。图1在每一过道两旁有一排货架的NR和NC列组成，而且，正如所说，每个通道是由起重机服务，移动在同一时间，它执行以下操作能够在垂直和水平：（一）联合单元（SU）采摘到储存在过道缓冲器的输入，因为输入/输出简称（I / O的）的过道点；（二）贮存于SU到指定地点的机架秒；（三）运动的位置R，其中检索已要求；（四）项苏检索存入R;（五）回来的I / O点。这种操作被称为在仓储系统中，双指令（DC）的机器周期。至于梭，它的动作就一直放在过道轴正交单声道的立体采摘路径和执行行动（从主输入缓冲区，过道输出位置和采摘/加气站输出位置）和存款的行动（进主输出缓冲区，过道输入地点，采摘/加气站输入地点）。采摘，加气领域代表与工厂的车间接口。一个采摘-加气站湾（记为BayPR图1）组成，这样一苏（托盘上进行），可部分掏空了采摘的位置，矿床定位和车间通过传送带连接的界面位置然后在车间的位置，然后进行人工操作在同一托盘上，靠过道的机架位置回来。I/O缓冲器代表与工厂的其他仓库的接口。仓库系统控制方法介绍。本文的第一个贡献是控制结构的发展上升到一个新的层次水平（Basile2, Carbone& Chiacchio5, 2003）。在以前的应用程序中，只有长期进行优化：它通常要一天或一周的时间范围，这是仓库系统的简化模型和系统的性能统计特性的基础。相反，我们引入了的这个实时优化工具，其核心是调度单元与执行处理序列的短期优化的目标，通常只要有目标，能根据系统的当前状态用最少的时间，完成采摘或小数目存储任务。还确定了一个详细的模型的设计和执行作用。其主要的优点是：l 短期优化带来更好的性能。l 设计，实施和低级别的处理大大简化测试序列，因为所有的优化问题采取的负责调度。l 因为优化器上的系统（它在实时工程）当前国家依靠它也可以管理缺陷和系统退化l 粗略地讲，调度输出是为了开始处理其控制的代码序列在低层次的执行，因此，它可以很容易地适应不同的植物本文的第二个贡献是在离散事件系统框架正式确立详细可靠的自动化立体仓库模型，如图1所示；模型的建立是为控制的设计和性能评价作基础。得到的模型是基于有色时间Petri网的方法（Jensen13, 1995； Hsieh, HWang, &Chou11, 1998)。在这种情况下该文献可以是主要贡献归纳如下：l 一个详细的整个仓库的正式模型建立起来，而以往的文献中主要考虑了（Hsieh, HWang, &Chou12, 1998），或仅实施了模拟目的（Lee等14， 1996； ChincholkarKrishnaiah，Chetty5，1996）l 该模型是高度模块化，每个模块参数：模型来模拟不同组件的重用仓库系统是很容易l 用一个更高的层次调度器（调度）接口是体现在所获得的模型，从而使脱机状态相关的调度控制算法的性能评价l 之间有一个清晰的模型实体和布局通信，从而使该模型用于在线监测使用（见柔性制造系统的监控（Feldmann & Colombo7，1999）l 详细的模型可以用于复杂的控制算法，这是基于上线前瞻（或者- IF）的技术（(Hsieh & Chen11, 1999）。第三个贡献是开发的控制算法在优化系统（OS）的实施。更确切地说，我们专注于直流周期的起重机和采摘/存储活动由堆垛机执行的操作同步优化。前文（Bozer & White4, 1984）和（Han, McGinnis9, Hsieh & White12，1987）认为在连续机架假设直流优化问题，即是一个无限的逐点的位置由机架解决每一个观看对实数。按照( Bozer& White4,1984），(Han, McGinnis9,Hsieh& White 12,1987; and Lee & Schaefer14,1996），指引我们发展的算法，用最佳的顺序检索订单。然而,我们从一开始就更为严格的假设(是基于现实世界经验),苏被存储的位置已经分配的地址在I / O点。这意味着它是不可能在这个水平下如以往文献假设的选择目的地并放在在一个空位置号。此外我们还提出了一种程序,基于参数识别技术,准确计算输送量的起重机/靠过道的子系统来运用我们的算法。不断逼近使人们能够表达吞吐量的功能完善的一小部分有意义的参数优化的穿梭在仓库中的图1类型系统的问题还没有被以前文献中讨论。我们提出了一个算法优化的序列进行采摘和穿梭，以减少每个周期所需的时间。假设再次沿道路上穿梭的位置连续结构，我们评估穿梭子系统由该算法应用中取得的理论吞吐量提高就在其中被认为是没有优化的情况下。一个在我们优化发展的关键点是，我们将证明，起重机优化是独立的（在一定意义上被指定）跟穿梭的优化，它们是可以分开进行的。参考文献：1Amato， F., & Basile, F. (2002). Crane and shuttle optimization in warehousing systems. 2002 IEEE International Conference on Robotics and Automation (ICRA02).2Basile, F., Carbone, C., & Chiacchio, P. (2001). Modeling of as/rs via coloured petri nets. IEEE International Conference on Advanced Intelligent Mechatronics (AIM 01).3Basile, F., Carbone, C., & Chiacchio, P. (2003). An approach to control automated warehouse systems. IEEE international conference on computational engineering in systems applications (CESA03).4Bozer, Y. A., & White, J. A. (1984). Travel-time models for automated storage/retrieval systems. IEE Transactions, 16(4), 329338.5Chincholkar, A. K., & Krishnaiah Chetty, O. V. (1996). Simultaneous optimization of control factors in automated and retrieval systems and FMS using stochastic coloured Petri nets and the Taguchi method. The International Journal of Advanced Manufacturing Technology, 12, 137144.6DiCesare, F., Harhalakis, G., Proth, J. M., Silva, M., & Vernadat, F. B. (1993). Practice of petri nets in manufacturing. London: Chapman and Hall.7Feldmann, K., & Colombo, A. W. (1999). Monitoring of flexible production systems using high-level Petri net specifications. Control Engineering Practice, 7, 14491466.8Graves, S. C., Hausman, W. H., & Schwarz, L. B. (1977). Storageretrieval interleaving in automatic warehousing systems. Management Science, 23(9), 935945.9Han, M. H., McGinnis, L. F., Shieh, J. S., & White, J. A. (1987). On sequencing retrievals in an automated storage/retrieval system. IEE Transactions, 19(3), 5666.10Hausman, W. H., Schwarz, L. B., & Graves, S. C. (1976). Optimal storage assignment in automatic warehousing systems. Management Science, 22(6), 629638.11Hsieh, S., & Chen, Y. F. (1999). Agvsimnet: A Petri-net-based agvs simulation system. The International Journal of Advanced Manufacturing Technology, 15, 851861.12Hsieh, S., Hwang, J. S., & Chou, H. C. (1998). A Petri net based structure for AS/RS operation modeling. International Journal of Production Research, 36(12), 33233346.13Jensen, K. (1995). Colored Petri nets. Basic concepts, analysis methods and practical use. Volume 1. Monographs on theoretical computer science. New York: Springer.14Lee, H. F., & Schaefer, S. K. (1996). Retrieval sequencing for unit-load automated storage and retrieval systems with multiple openings. International Journal of Production Research, 34(10), 29432962.15Lee, S. G., de Souza, R., & Ong, E. K. (1996). Simulation modelling of a narrow aisle automated storage and retrieval system (AS/RS) serviced by rail-guided vehicles. Computers in Industry, 30, 241253.16Linn, R. J., & Xie, X. (1993). A simulation analysis of sequencing rules in a pull-based assembly facility. International Journal of ProductionResearch, 31(10), 23552367.17Murata, T. (1989). Petri nets: Properties, analysis and applications. Proceedings of IEEE, 77(4), 541580.18Ramaswamy, S., & Valavanis, K. P. (1994). Modeling, analysis and simulation of failures in a material handling systems with extended Petri net. IEEE Transactions on System, Man and Cybernetics, 24(9), 13581373.19Seidmann, A. (1988). Intelligent control schemes for automated storage and retrieval systems. International Journal of Production Research, 26(5), 931952.20Silva, M., & Teruel, E. (1997). Petri nets for the design and operation of manufacturing systems. European Journal of Control, 3(3), 182199.21Van den Berg, J. P. (1999). A literature survey on planning and control of warehousing systems. IIE Transactions, 31, 113.An approach to control automated warehouse systemsFrancesco Amato，Francesco Basile, Ciro Carbone, Pasquale ChiacchioAbstractThe goal of this paper is the development of control algorithms for the management of an automated warehouse system.As usual, the implementation of a control algorithm requires three preliminary steps: development of a reliable model; design of control procedures according to some optimality criteria; validation of these control procedures. As for modelling, a new level in the control architecture, namely an optimizer system, is introduced which performs real-time optimization thus simplifying the low-level contro and improving the overall performance. In this context, the role of a detailed model of the whole warehouse is discussed and such a model is built up by using the colored timed Petri nets framework. As for control, we propose two control algorithms, derived under the simplifying continuity assumption of the rack locations, to optimize the operations of the cranes (moving within the aisles of the warehouse) and the operations of the shuttle (moving on a straight line placed between the aisles and the picking/refilling area),respectively. To evaluate the performance of the proposed control algorithms we define three different cost indices. As for the validation, extensive simulations are performed on the model in order to prove the effectiveness of the proposed control algorithms. A further validation of the algorithms has been performed on a test bed in order to take into account communication delays and computation times. Finally, the proposed architecture and control algorithms have been applied to a real plant.Keywords：Discrete event systems modelling; Colored timed Petri nets; Warehouse automation; Factory automation; OptimizationIn the last fifteen years, a big effort has been made to find optimal strategies for planning and control of warehouse systems. These issues have become more and more challenging with the growth and diffusion of modern computer technology which allows the implementation of complex operations in a completely automatic way. Planning involves high level decisions, like assignment of goods to the storage locations (random, class-based, correlated product clustering policies) (VandenBerg, 1999) or the designing of the. warehouse system itself. Control problems involve optimal sequencing and scheduling of storage and retrieval requests resulting in the so-called dispatching control. We refer to a general warehouse architecture consisting of a number of aisles, each one served by a crane, shuttles, picking/refilling positions and in/out buffers (see Fig. 1).Fig. 1.On both sides of each aisle there is a storage rack composed of nr rows and nc columns; moreover, as said, each aisle is served by a crane, capable of moving both vertically and horizontally at the same time, which performs the following operations：(i) picking of theStock Unit (SU) to be stored at the buffer input of the aisle, referred as the input/output (I/O) point of theaisle；(ii) storage of the SU into the assigned location S of the rack; (iii) movement to location R where a retrieval has been requested; (iv) retrieval of the SU stored in R; (v) coming back to the I/O point. This set of operations is called, in the warehousing system context, a dual command (DC) machine cycle.As to the shuttle, it moves along a mono-dimensional path placed orthogonally with respect to the aisle axis and performs picking actions (from the main input buffer, the aisles output locations and the picking/ refilling output locations) and deposit actions (into the main output buffer, the aisles input locations, the picking/refilling input locations.The pickingrefilling areas represent an interface with the shop floor of the factory. A pickingrefilling bay (denoted as BayPR in Fig. 1) consists of a picking location, a deposit location and a shop floor interface location connected via conveyors so that an SU (carried on a pallet) can be partially emptied by a human operator at the shop floor location and then carried back on the same pallet to an aisle rack location.The in/out buffers represent the interface of the warehouse with the rest of the plant.In this paper an approach to the control of warehouse systems is presented.The first contribution of this paper is the development of a new hierarchical level in the control architecture (Basile, Carbone, & Chiacchio, 2003). Indeed, in previous applications, only long-term optimization was performed: it usually has a day or week time horizon and it is based on simplified models of warehouse systems and on statistical characterization of the system performance. Conversely, we introduce a further level constituted by a real time optimizer, whose core is the dispatcher unit, with the aim of performing the shortterm optimization of handling sequences, that usually has the objective to minimize the time to complete a little number of picking or storage missions and is based on the current state of the system. The role of a detailed model for the designing and implementation of this level is also discussed. The main advantages obtained are:l The short-term optimization leads to better performance.l Design, implementation and testing of low-level handling sequences is greatly simplified since all the optimization issues are taken in charge by the dispatcher.l Since the optimizer relies on the current state of the system (it works in real time) it can also manage faults and degradation of the systeml Roughly speaking, the dispatcher output is the order to start handling sequences whose control code is executed at low-level; therefore, it can be easily adapted to different plantsThe second contribution of this paper is the development of a detailed formal reliable model of an automated warehouse, as shown in Fig. 1, in the framework of discrete event systems; model building is fundamental for both control design and performance evaluation. The obtained model is based on the colored timed Petri net ARTICLE IN PRESS Fig. 1. Warehouse lay-out. 1224 F. Amato et al. / Control Engineering Practice 13 (2005) 12231241 (CTPN) approach (Jensen, 1995; Hsieh, HWang, & Chou, 1998). In this context the key contributions of the paper can be summarized as follows:l A detailed formal model of the whole warehouse is built up, while the previous literature mainly considered the formal modelling of the individual components (Hsieh, HWang, & Chou, 1998) or only implemented a simplified model for simulation purposes (Lee et al., 1996; Chincholkar & Krishnaiah Chetty, 1996)l The model is highly modular and each module is parameterized: the reuse of model components to model different warehouse systems is very easy.l The interface with a higher level scheduler (dispatcher) is embodied in the obtained model, thus allowing off-line performance evaluation of state-dependent dispatching control algorithmsl There is a clear correspondence between model entities and layout, thus allowing the model to be used for on-line monitoring (see (Feldmann & Colombo, 1999) for monitoring of flexible manufacturing systems).l The detailed model can be used on-line in complex control algorithms which are based on look-ahead (or what-if) techniques (Hsieh & Chen, 1999).The third contribution is the development of the control algorithms implemented in the optimizer system (OS). More precisely we focus on simultaneous optimization of the DC cycles operations of the cranes and the picking/deposit actions performed by the shuttle.Previous papers (Bozer & White, 1984) and (Han, McGinnis, Shieh, & White, 1987) considered the DC optimization problem under the continuous rack assumption, i.e. the rack is viewed as an infinite number of pointwise locations each one addressed by a pair of real numbers.By following the guidelines of Bozer and White (1984), Han, McGinnis, Shieh, and White (1987) and Lee and Schaefer (1996), in this paper we develop an algorithm to optimally sequence the retrieval orders.However, we start from the more restrictive assumption (which is based on real world experience) that the SU to be stored arrives at the I/O point with the address of the destination location already assigned. This means that it is not possible, at this level, to choose the destination among a number of empty locations, as assumed in previous papers. Moreover we propose a procedure, based on parametric identification techniques, to compute precisely the throughput of the crane/aisle subsystem which comes out from the application of our algorithm. The continuous approximation allows us to express the throughput improvement as a function of a small number of meaningful parameters.The problem of shuttle optimization in warehouse system of the type in Fig. 1 has not been previously discussed in the literature. We propose an algorithm to optimize the sequence of picking and deposit operations performed by the shuttle, so as to minimize the time required by each cycle. Assuming again a continuous structure for the locations placed along the shuttle path, we evaluate the theoretical improvement of the shuttle sub-system throughput obtained by the application of the proposed algorithm, with respect to the case in which no optimization is considered.A key point in the development of our optimization is that, as we shall show, the cranes optimization is independent (in a sense to be specified) from the shuttle optimization and therefore they can be performed separately.References：1Amato， F., & Basile, F. (2002). Crane and shuttle optimization in warehousing systems. 2002 IEEE International Conference on Robotics and Automation (ICRA02).2Basile, F., Carbone, C., & Chiacchio, P. (2001). Modeling of as/rs via coloured petri nets. IEEE International Conference on Advanced Intelligent Mechatronics (AIM 01).3Basile, F., Carbone, C., & Chiacchio, P. (2003). An approach to control automated warehouse systems. IEEE international conference on computational engineering in systems applications (CESA03).4Bozer, Y. A., & White, J. A. (1984). Travel-time models for automated storage/retrieval systems. IEE Transactions, 16(4), 329338.5Chincholkar, A. K., & Krishnaiah Chetty, O. V. (1996). Simultaneous optimization of control factors in automated and retrieval systems and FMS using stochastic coloured Petri nets and the Taguchi method. The International Journal of Advanced Manufacturing Technology, 12, 137144.6DiCesare, F., Harhalakis, G., Proth, J. M., Silva, M., & Vernadat, F. B. (1993). Practice of petri nets in manufacturing. London: Chapman and Hall.7Feldmann, K., & Colombo, A. W. (1999). Monitoring of flexible production systems using high-level Petri net specifications. Control Engineering Practice, 7, 14491466.8Graves, S. C., Hausman, W. H., & Schwarz, L. B. (1977). Storageretrieval interleaving in automatic warehousing systems. Management Science, 23(9), 935945.9Han, M. H., McGinnis, L. F., Shieh, J. S., & White, J. A. (1987). On sequencing retrievals in an automated storage/retrieval system. IEE Transactions, 19(3), 5666.10Hausman, W. H., Schwarz, L. B
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