摘要
市场竞争的日益激烈和市场需求动态多变，迫使企业车间的制造系统必须具备快速可重构的能力，从而以合理的成本快速投入新产品的生产，适应动态多变的市场和客户的个性化需求。同时，随着物联网技术的发展和制造执行系统在生产制造领域应用的不断深入，也促使生产车间不断向透明化、数字化、可视化方向转变。本文分析和总结了目前典型的制造模式特点以及车间制造系统的发展趋势，在虚拟制造单元、物联网和制造执行系统等技术的基础上，发展了一种新的车间制造模式——虚拟单元车间制造模式。本文以虚拟单元制造车间的支持技术、虚拟单元重构方法以及车间的生产控制与调度为研究重点，对这些关键问题和技术进行了系统研究。主要研究内容如下：
1、为了使制造系统能够适应动态多变的市场，并具备透明化、数字化、可视化等特点，本文在虚拟制造单元、物联网和制造执行系统等技术的基础上，发展了一种新的车间制造模式——虚拟单元车间制造模式。为了提高车间管理控制的自动化水平，提出了物联网与车间制造系统全面融合的理念和结构，并在可行性方面进行了探索和研究，就一些关键问题提出了相应的技术解决方案。首先，探讨了物联网技术对车间生产的重要意义，提出了一种基于网格平台的物联网构建方法，针对物联网环境的特点提出了一种基于网格服务的MES系统设计开发方法，构建了基于网格服务的MES系统层次体系结构和功能模型。然后，提出了一种物联网环境下生产实时监测和订单实时跟踪问题的解决方法。该方法首先采用WSRF技术将生产设备封装成标准的网格资源，通过设备网格资源动态信息的实时更新与查询和网格资源通知机制实现对生产设备加工过程的实时监测；然后采用OGSA—DAI网格中间件和WSRF技术将异构数据库系统封装成标准的网格服务，从而屏蔽系统的差异性，实现订单进度信息的集成访问。从而可以实现管理人员和客户对生产进度的实时监测，不但为管理者的管理决策提供了实时数据支持，而且便于客户准确预测订单完工时间。
2、针对单件、小批量、个性化面向订单生产企业，研究了多张不同交货期生产订单并存情况下的虚拟单元重构问题。采用两阶段的策略进行求解。在产品聚类阶段提出了一种最小树和遗传算法相结合的自适应聚类算法，在算法中引入了一种新的基于最小树的编码方法，有效的缩短了编码长度，缩小了问题的搜索空间。该算法只需操作人员给定聚类的最大数量，就可以自适应地确定最佳聚类数目和产品聚类。在单元重构阶段建立了问题的多目标整数规划模型，针对该问题的特点，将粒子群算法与协同进化策略相结合，提出了一种平行多目标协同粒子群优化算法(PCMOPSO)。采用生产实例对算法的有效性和可行性进行了验证。
3、对虚拟单元制造模式下的车间调度特点进行了分析，然后分别针对目标数量较多的高维多目标调度问题和资源共享情况下的生产调度问题进行了研究。针对具有高维搜索空间的多目标生产调度问题，提出了一种基于偏好的多目标粒子群优化算法（PMOPSO）。算法引入了决策者的偏好信息，用以指导算法的搜索过程，使算法在决策者感兴趣的区域进行搜索，不但缩小了算法的搜索空间，提高了算法的效率，而且一次运算只求得偏好区域内若干个折衷解，避免了决策者要在众多非劣解中做出困难的选择。在算法中，采用了新的偏好信息给定方法，即采用目标间重要关系、目标数值或目标权重大致取值范围来表示偏好信息。采用该方法，不但便于决策者给定偏好信息，而且还可以根据决策者的需求，对搜索区域的范围进行适当的调整。针对偏好信息的特点，提出了一种模拟人类社会组织“投票选举”的偏好信息处理方法，该方法直观简便并易于实现。针对资源共享情况下的生产调度问题的特点，将粒子群优化算法与协同进化算法相结合提出了一种分布式协同多目标粒子群调度算法，针对共享资源调度冲突问题，在算法中引入了一种模拟市场机制的解码规则加以解决。并通过仿真试验，对上述两种算法性能进行比较分析和评价，结果表明了算法的有效性和可行性。
4、研究了多张不同交货期订单并存情况下的多目标分批动态调度问题。首先，针对传统的多目标动态调度问题，提出了一种基于滚动窗口的多目标粒子群动态调度算法，该算法采用基于周期和事件驱动的混合再调度机制将调度过程分成连续静态调度区间，在每个区间内用基于Pareto概念的多目标粒子群算法对窗口工件进行调度优化，一次求解可得到多个Pareto最优解，为决策者提供了多样性的选择。并针对调度问题的特点，并设计了一种适合多目标调度的间隙插入式解码方法，有效的减小了问题搜索空间。然后，针对面向订单制造的多目标分批动态调度问题，提出了一种基于粒子群算法的多目标柔性分批动态调度算法，算法采用局部更新和完全重调度相结合的策略，既可以保持生产秩序的稳定，同时又保证了算法对突发事件的快速反应能力。在算法中，提出了一种新的基于“游标”的柔性批次分割方法，并采用一种批次分割与加工工序相融合的粒子编码方法，使得该算法不但可以根据机床负荷与订单交货期将工件族划分为具有柔性批量的多个批次，而且可使批次工艺路线和加工排序同时得到优化。而且，算法一次求解可得到多个Pareto最优解，为决策者提供了多样性的选择。并且，通过实例验证了上述算法有效性和可行性。
5、最后，设计并开发了虚拟单元制造车间MES原型系统，对原型系统的主要功能进行了介绍，并将该原型系统在工业泵阀工厂进行了实验性应用。
The trends of increasing severe market competition and decreasing product life cycles in theglobal manufacturing era point at the need to develop flexible reconfigurable manufacturing systems,which should adapt themselves to single work piece, small-batch, diversified and make to ordermanufacturing environment. As the application of internet of things and manufacturing executionsystem in manufacturing field, the shop-floor has become more and more transparency, digital andviewable.Thorough summary and analysis of advanced manufacturing system mode, a new shop-floormanufacturing mode based on virtual manufacturing cell, internet of things and manufacturingexecution system, named virtual cellular shop-floor manufacturing mode, was developed. Thisdissertation focuses the production scheduling and control problem of shop-floor in this kind ofmanufacturing mode. An extensive study on the supporting technologies, reconfigurationmethodology of virtual manufacturing cell, production scheduling and control algorithm was carriedout. The main contents and achievements of the dissertations are as follows:
1. In order to make the manufacturing system become transparency, digital, viewable, flexibleand reconfigurable, a new shop-floor manufacturing mode based on virtual manufacturing cell,internet of things and manufacturing execution system (MES), named virtual cellular shop-floormanufacturing mode, was developed. In order to realize the internet of things and automatic control inshop-floor, the feasibility of shop-floor internet of things was discussed and solving methods for a fewrelated problems were put forward. First, a shop-floor internet of things construction method based oncomputing grid was discussed, a MES construction method based on grid service was proposedaccording to characteristics of internet of things, and the architecture of MES system based on gridservice was constructed. And then, a method to realize real time monitoring of shop-floor and productorder with internet of things was proposed. With this method, machine tools was encapsulated as gridresources with WSRF at first, and shop-floor can be real time monitored through inquiring thedynamic information or notifications of grid resources; then the heterogeneous database system wasencapsulated as uniform grid service with OGSA-DAI and WSRF, and the information of orderproduction schedule can be integrated access easily. With the above method, not only the real timemonitoring of the shop-floor can be easily realized for managers of the factory, but also completiontime of orders can be accurately predicted for the customers.
2. To adapt to characteristics of single work piece, small-batch, diversified and make to ordermanufacturing environment, a methodology which could be used to reconfigure VirtualManufacturing Cell for multiple product orders with different due dates was proposed. The methodology is divided into two phases: product clustering and virtual cell reconfiguration. In thephase of product clustering, an adaptive clustering algorithm based minimal spanning tree and geneticalgorithm was proposed. To compress the length of gene code and searching space of problem, a newcoding method based on minimal spanning tree is employed in the algorithm. The algorithm canautomatically estimate the optimal number of clusters without a-priori information. In the phase ofvirtual cell reconfiguration, a non-linear multi-objective integer programming model was constructedand a new parallel collaborative multi-objective particle swarm optimization (PCMOPSO) algorithmwas proposed. Finally, the algorithms were verified through examples.
3. Aiming at the characteristics of scheduling problem in the virtual cellular shop-floor, themulti-objective scheduling problem with large dimensional searching space and scheduling problemwith resource shared by different cells ware studied respectively. To solve the multi-objectivescheduling problem with large dimensional searching space, a preference based multi-objectiveparticle swarm optimization algorithm (PMOPSO) was proposed. The preference information ofdecisions maker is incorporated into the algorithm to lead the searching direction. So that, not only thesearching space is compressed and the efficiency of the algorithm is improved, but also just a fewtrade-off solutions located in preferred area are obtained in a single run, and the hard work ofchoosing a satisfying solution from numerous non-inferior solutions is eliminated. In the algorithm, anew expression method of preference information based on importance relationship among objectivesand the value range of objectives or objective weights was proposed. With this method, not only thepreference of decisions maker can be easily specified, but also the range of searching area can beadjusted properly according to the requirements of decisions maker. In view of the characteristics ofpreference information, a new preference information handling method, which simulates the “vote” ofhuman society, was proposed. The method is intuitive, simple and easy to use. To solve the schedulingproblem with resource shared by different cells, a distributed collaborative multi-objective particleswarm algorithm was proposed, in the algorithm a new decoding method which simulates marketmechanism is employed to solve the resource conflict. Finally, the performance of the abovealgorithms was evaluated through simulations, and the results demonstrate the feasibility andefficiency of proposed algorithms.4. The multi-objective dynamic scheduling problem with lot-splitting for multiple product orders withdifferent due dates was studied. First, the traditional multi-objective dynamic scheduling problem wasstudied. To solve the multi-objective dynamic scheduling problem, a new multi-objective dynamicscheduling algorithm based on particle swarm optimization algorithm and rolling-horizon was proposed. In this algorithm, periodic and event driven rescheduling strategies were employed and thedynamic scheduling problem was decomposed into a series of continual and static schedulingproblems, then an improved multi-objective particle swarm optimization algorithm were applied tooptimize each of the static scheduling problems, and the decisions maker can choose a satisfyingsolution from many Pareto optimal solutions obtained in a single run. To compress the searchingspace, a new active multi-objective scheduling decoding method was employed in the algorithm. Tosolve the multi-objective dynamic scheduling problem with lot-splitting for multiple product orderswith different due dates, a novel multi-objective flexible size lot-splitting dynamic schedulingalgorithm based on particle swarm optimization algorithm was proposed. Local and global updatingstrategy are both considered in the algorithm, local updating strategy is adopted for those turbulencesthat happen in high frequency but have little effect on the scheduling; otherwise, global updatingstrategy is adopted. In the algorithm, a flexible size lot-splitting approach based on “cursors” was putforward. Combined the lot-splitting and the lot scheduling, a novel particle coding scheme wasproposed. So that the algorithm not only can split lots into flexible size sub-lots according to machineworkloads, but also can optimize the lots routing and sequencing simultaneously. The performance ofthe proposed algorithms was evaluated through simulations, and the results demonstrate the feasibilityand efficiency of the proposed algorithms.5. Finally, A MES prototype system for virtual cellular shop-floor was designed and developed. By itsexperimental application in industry pump and valve factories, the application problem of system isdiscussed.
引文
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