DPP: An agent-based approach for distributed process planning

A changing shop floor environment characterized by larger variety of products in smaller batch sizes requires creating an intelligent and dynamic process planning system that is responsive and adaptive to the rapid adjustment of production capacity and functionality. In response to the requirement, this research proposes a new methodology of distributed process planning (DPP). The primary focus of this paper is on the architecture of the new process planning approach, using multi-agent negotiation and cooperation. The secondary focus is on the other supporting technologies such as machining feature-based planning and function block-based control. Different from traditional methods, the proposed approach uses two-level decision-making—supervisory planning and operation planning. The former focuses on product data analysis, machine selection, and machining sequence planning, while the latter considers the detailed working steps of the machining operations inside of each process plan and is accomplished by intelligent NC controllers. By the nature of decentralization, the DPP shows promise of improving system performance within the continually changing shop floor environment.     

A review of function blocks for process planning and control of manufacturing equipment

Manufacturing in a job-shop environment is often characterized by a large variety of products in small batch sizes, requiring real-time monitoring for dynamic distributed decision making, and adaptive control capabilities that are able to handle, in a responsive way, different kinds of uncertainty, such as changes in demand and variations in production capability and functionality. In many manufacturing systems, traditional methods, based on offline processing performed in advance, are used. These methods are not up to the standard of handling uncertainty, in the dynamically changing environment of these manufacturing systems. Using real-time manufacturing intelligence and information to perform at a maximum level, with a minimum of unscheduled downtime, would be a more effective approach to handling the negative performance impacts of uncertainty. The objective of our research is to develop methodologies for distributed, adaptive and dynamic process planning as well as machine monitoring and control for machining and assembly operations, using event-driven function blocks. The implementation of this technology is expected to increase productivity, as well as flexibility and responsiveness in a job-shop environment. This paper, in particular, presents the current status in this field and a comprehensive overview of our research work on function block-enabled process planning and execution control of manufacturing equipment.     

An overview of function block enabled adaptive process planning for machining

Small- and medium-sized enterprises (SMEs) in job-shop machining are experiencing more shop-floor uncertainties today than ever before, due to multi-tier outsourcing, customised product demands and shortened product lifecycle. In a fluctuating shop floor environment, a process plan generated in advance is often found unsuitable or unusable to the targeted resources, resulting both in wasted effort spent in early process planning and in productivity drop when idle machines have to wait for operations to be re-planned. Consequently, an adaptive process planning approach is in demand. Targeting shop-floor uncertainty, the objective of this research is to develop a novel adaptive process planning method that can generate process plans at runtime to unplanned changes. This paper, in particular, presents an overview of adaptive process planning research and a new methodology, including two-layer system architecture, generic supervisory planning, machine-specific operation planning, and adaptive setup planning. Particularly, function blocks are introduced as a core enabling technology to bridge the gap between computer systems and CNC systems for adaptive machining.     

A hybrid approach for dynamic routing planning in an automated assembly shop

Highly turbulent environment of dynamic job-shop operations affects shop floor layout as well as manufacturing operations. Due to the dynamic nature of layout changes, essential requirements such as adaptability and responsiveness to the changes need to be considered in addition to the cost issues of material handling and machine relocation when reconfiguring a shop floor’s layout. Here, based on the source of uncertainty, the shop floor layout problem is split into two sub-problems and dealt with by two modules: re-layout and find-route. GA is used where changes cause the entire shop re-layout, while function blocks are utilised to find the best sequence of robots for the new conditions within the existing layout. This paper reports the latest development to the authors’ previous work.     

Simulation-based planning and control: From shop floor to top floor

This paper illustrates how simulation-based shop-floor planning and control can be extended to enterprise-level activities (top floor). First, the general planning and control concept are discussed, followed by an overview of simulation-based shop-floor planning and control. Analogies between the shop floor and top floor are discussed in terms of the components required to construct simulation-based planning and control systems. Analogies are developed for resource models, coordination models, physical entities, and simulation models. Differences between the shop floor and top floor are also discussed in order to identify new challenges faced for top-floor planning and control. A major difference between the top floor and the shop floor is the way a simulation model is constructed for use in planning, depending on whether time synchronization among member simulations becomes an issue or not. Another difference is in the distributed communication/computing platform. This work uses a distributed computing platform using Web services technology to integrate heterogeneous simulations and systems in a distributed top-floor control environment. The research results reveal that simulation-based planning and control is extensible to the top-floor environment’s evolving new research challenges.     

Hierarchy aware distributed plan execution monitoring

Collaborative plan execution is becoming increasingly important given its potential for operational agility and cost reduction. In this paper we propose a distributed and hierarchy aware monitoring procedure for operational plan execution taking place in a dynamic environment characterized by unreliable communication and exogenous events. The contribution of this paper consists in employing a hierarchical clustering approach supporting a multi-party and hierarchy aware information sharing mechanism that is resilient to disruptions in the execution environment. The proposed distributed monitoring procedure uses asymmetric clustering to reflect hierarchical relationships along with gossip based communication across the clusters. Of significance is the information sharing mechanism formalization which utilizes a fresh information window in conjunction with communicating Markov Decision Processes. We show the usefulness of assessing shared information awareness via probabilistic model checking for various combinations of clustering topology and disruption conditions. In this context, we assess formal specifications expressed in probabilistic temporal logic and show how the model checking results can be used to derive the best fresh window value to maximize an information awareness utility function. An illustrative case study is also presented.     

Dynamic planning model for determining cutting parameters using neural networks in feature-based process planning

Although feature-based computer-aided process planning plays a vital role in automating and integrating design and manufacturing for efficient production, its off-line properties prohibit the shop floor controllers from rapidly coping with unexpected production errors. The objective of the paper is to suggest a neural network-based dynamic planning model, by which the shop floor controllers determine cutting parameters in real-time based on shop floor status. At off-line is the dynamic planning model constructed as a neural network form, and then embedded into each removal feature. The dynamic planning model will be executed by the shop floor controllers to determine the cutting parameters. A prototype system is constructed to validate whether the dynamic planning model is capable of determining dynamically and efficiently the cutting parameters for a particular set of shop operating factors. Owing to the dynamic planning model, the shop floor controller will increase flexibility and robustness by rapidly and adaptively determining the cutting parameters in unexpected errors occurring.     

Real-time scheduling for hybrid flowshop in ubiquitous manufacturing environment

This paper discusses the implementation of RFID technologies, which enable the shop floor visibility and reduce uncertainties in the real-time scheduling for hybrid flowshop (HFS) production. In the real-time HFS environment, the arriving of new jobs is dynamic, while the processes in work stages are not continuous. The decision makers in shop floor level and stage level have different objectives. Therefore, classical off-line HFS scheduling approaches cannot be used under these situations. In this research, two major measures are taken to deal with these specific real-time features. Firstly, a ubiquitous manufacturing (UM) environment is created by deploying advanced wireless devices into value-adding points for the collection and synchronization of real-time shop floor data. Secondly, a multi-period hierarchical scheduling (MPHS) mechanism is developed to divide the planning time horizon into multiple shorter periods. The shop floor manager and stage managers can hierarchically make decisions for their own objectives. Finally, the proposed MPHS mechanism is illustrated by a numerical case study.     

基于多Agent的Web服务动态合成的研究

现有基于传统工作流的Web服务合成的研究,不能很好满足Web服务合成动态、分布 式的要求。将Web服务动态合成技术和多Agent技术结合,着重于合成服务流程执行阶段的监控和 异常处理,给出了基于多Agent技术的Web服务动态合成系统的框架MAS WS,描述了其中的关键技 术,包括Agent联邦动态组建、主动协作、服务流程分层管理、流程规划与执行交叉进行等,并分析了 MAS WS的优势。     

实时专家系统中反应—规划—动作的集成

以AMPE模型为基础,探讨了在动态环境下AI系统如何集成响应执行和规划生成的问题。AMPE模型是从实际应用中演化出的AI模型,采用分离结构,在规划中引入一类被称为观察点的特殊行为,支持异步重规划。描述了AMPE的各类规划行为的形式和消息的基本结构。给出一个规划表示的实例。     

ASP: An Adaptive Setup Planning Approach for Dynamic Machine Assignments

This paper presents a decision-making approach towards adaptive setup planning that considers both the availability and capability of machines on a shop floor. It loosely integrates scheduling functions at the setup planning stage, and utilizes a two-step decision-making strategy for generating machine-neutral and machine-specific setup plans at each stage. The objective of the research is to enable adaptive setup planning for dynamic job shop machining operations. Particularly, this paper covers basic concepts and algorithms for one-time generic setup planning, and run-time final setup merging for dynamic machine assignments. The decision-making algorithms validation is further demonstrated through a case study. Note to Practitioners-With increased product diversification, companies must be able to profitably produce in small quantities and make frequent product changeovers. This leads to dynamic job shop operations that require a growing number of setups in a machine shop. Moreover, today's customer-driven market and just-in-time production demand for rapid and adaptive decision making capability to deal with dynamic changes in the job shop environment. Within the context, how to come up with effective and efficient setup plans where machine availability and capability change over time is crucial for engineers. The adaptive setup planning approach presented in this paper is expected to largely enhance the dynamism of fluctuating job shop operations through adaptive yet rapid decision makings.     

Customer order management in service oriented holonic manufacturing

One of the most important problems when considering the design of manufacturing systems based on SOA paradigms is the integration of shop floor devices in the business processes at the enterprise level. This paper presents the design and implementation of the Customer Order Management (COM) module based on SOA architecture in the context of holonic manufacturing systems. The COM module is integrating with SOA enabled shop floor devices using industry standards. The implementation leverages a multi agent system suited for industrial applications integrated in a SOA environment capable of dynamic BPEL workflow generation and execution. The prototype consists in a SCA application for core COM module functionality and an extension for NetLogo MAS platform for SOA integration. The COM module interacts with the MES layer using real time events handled by the BPEL process implementation in the execution stage. A web based portal frontend for the COM module has been developed to allow real time tracking of customer orders, providing data about product batch execution and individual progress of each product on the production line.     

Modeling integrated CAPP/PPS systems

In order to solve the isolation and the unilateral sequence integration between CAPP and PPS, this paper proposes the integrated system of CAPP and PPS based on distributed and dynamic process planning (ICAPPS), aiming for single-piece, small-lot and made-to-order production (SPSLMOP), and then systematically builds the integrated ICAPPS model. On the basis of the integrated model, this paper constructs the function model of ICAPPS, which includes the designing layer, the part planning layer, the shop planning layer and the scheduling layer in detail, according to the requirement for the rationalization of resource utilization, process design and plan scheduling in SPSLMOP.     

Autonomous Driving with Concurrent Goals and Multiple Vehicles: Mission Planning and Architecture

We introduce a new distributed planning paradigm, which permits optimal execution and dynamic replanning of complex multi-goal missions. In particular, the approach permits dynamic allocation of goals to vehicles based on the current environment model while maintaining information-optimal route planning for each individual vehicle to individual goals. Complex missions can be specified by using a grammar in which ordering of goals, priorities, and multiple alternatives can be described. We show that the system is able to plan local paths in obstacle fields based on sensor data, to plan and update global paths to goals based on frequent obstacle map updates, and to modify mission execution, e.g., the assignment and ordering of the goals, based on the updated paths to the goals.The multi-vehicle planning system is based on the GRAMMPS planner; the on-board dynamic route planner is based on the D* planner. Experiments were conducted with stereo and high-speed ladar as the to sensors used for obstacle detection. This paper focuses on the multi-vehicle planner and the systems architecture. A companion paper (Brumitt et al., 2001) analyzes experiments with the multi-vehicle system and describes in details the other components of the system.     

Shop-floor resource virtualization layer with private cloud support

Large scale emergence of mature cloud solutions, ranging from software-as-a-service based solutions for business management, to very sophisticate private cloud solutions; offer the building blocks for constructing extremely flexible enterprise systems that can respond to environmental changes with great agility. Manufacturing enterprises need to adopt these new technologies to advance in a new era of mass customization where flexibility, scalability and agility are the differentiating factors. In this context, this paper introduces the virtualized MES and shop floor architecture as an intermediate layer in the manufacturing stack and discusses the advantages offered by this approach for manufacturing enterprises. A classification of MES and shop floor devices is presented focusing on the virtualization techniques suitable for each device type, considering the level of distributed intelligence and the virtualization overhead. Shop floor virtualization through shop floor profiles is presented and discussed underlying the flexibility of the solution. A pilot multi-agent implementation for virtual shop floor configuration based on the CoBASA reference architecture is presented and discussed. The shop floor profiles which define the virtual layout and mappings of the robotized manufacturing system are also provided in this context. The pilot implementation using six Adapt robots and a IBM CloudBurst 2.1 private cloud, is described and virtualization overhead in terms of event propagation delays is measured and presented in several scenarios of resource workload collocation on physical cloud blades     

Process plan and part routing optimization in a dynamic flexible job shop scheduling environment: an optimization via simulation approach

This paper presents an optimization via simulation approach to solve dynamic flexible job shop scheduling problems. In most real-life problems, certain operation of a part can be processed on more than one machine, which makes the considered system (i.e., job shops) flexible. On one hand, flexibility provides alternative part routings which most of the time relaxes shop floor operations. On the other hand, increased flexibility makes operation machine pairing decisions (i.e., the most suitable part routing) much more complex. This study deals with both determining the best process plan for each part and then finding the best machine for each operation in a dynamic flexible job shop scheduling environment. In this respect, a genetic algorithm approach is adapted to determine best part processing plan for each part and then select appropriate machines for each operation of each part according to the determined part processing plan. Genetic algorithm solves the optimization phase of solution methodology. Then, these machine-operation pairings are utilized by discrete-event system simulation model to estimate their performances. These two phases of the study follow each other iteratively. The goal of methodology is to find the solution that minimizes total of average flowtimes for all parts. The results reveal that optimization via simulation approach is a good way to cope with dynamic flexible job shop scheduling problems, which usually takes NP-Hard form.     

Distributed Reinforcement Learning Control for Batch Sequencing and Sizing in Just-In-Time Manufacturing Systems

This paper presents an approach that is suitable for Just-In-Time (JIT) production for multi-objective scheduling problem in dynamically changing shop floor environment. The proposed distributed learning and control (DLC) approach integrates part-driven distributed arrival time control (DATC) and machine-driven distributed reinforcement learning based control. With DATC, part controllers adjust their associated parts' arrival time to minimize due-date deviation. Within the restricted pattern of arrivals, machine controllers are concurrently searching for optimal dispatching policies. The machine control problem is modeled as Semi Markov Decision Process (SMDP) and solved using Q-learning. The DLC algorithms are evaluated using simulation for two types of manufacturing systems: family scheduling and dynamic batch sizing. Results show that DLC algorithms achieve significant performance improvement over usual dispatching rules in complex real-time shop floor control problems for JIT production.     

A closed-loop control architecture for CAM accounting for shop floor uncertainties

The primary focus of this paper is to demonstrate an enhanced closed-loop computer-aided manufacturing (CAM) architecture using a Petri net based analysis mechanism, which incorporates shop floor uncertainties to generate process plans and alternative processing sequences under dynamic conditions such as machine failures. Modern CAM software provide efficient, error-free, and user-friendly interfaces to develop processing sequences for manufacturing tasks. However, their effective scope is still limited from the post-process planning stage to the pre-machining stage. These types of software fail to incorporate the variability encountered in an actual shop floor to develop effective process plans. This is caused due to CAM software’s limitations in input handling mechanisms, limited simulation functionality, and inability to handle complex process variability from the shop floor. In this paper, CAM functionality is augmented with Petri net models to capture alternative process plans and shop floor variability arising due to equipment failures, repairs, and setups. The output from the analysis of the Petri net model provides a better understanding of the process characteristics and provides information to the CAM system to adapt to shop floor variability. The resulting information from the Petri net model allows the CAM system to compare alternative process plans while taking shop floor information into account. This mechanism can result in better decisions, reduced processing times, and better overall resource utilization, and provide alternative process plans based on current resource information.     

无线传感器网络中的分布式动态路径规划算法

利用无线传感器网络(WSN)根据环境变化为移动主体规划优化路径在实际应用中具有重要意义.针对现有梯度势场算法在动态调整方面的不足,综合考虑路径长度、安全性和通信代价,结合环境因素构造梯度势场函数,提出了一种分布式动态路径规划算法,使网络在环境变化时依据局部信息动态调整梯度势场,为每个节点提供优化路径.仿真结果显示了本文算法可使WSN在环境变化情况下,能够规划出较短路径,有效降低通信代价并灵活处理路径安全性.     

Agent-based Gateway Operating System for RFID-enabled ubiquitous manufacturing enterprise

Capturing and processing of real-time manufacturing shop floor field data is essential in improving the performance of shop floor planning, execution and control. Radio Frequency IDentification (RFID) has enabled real-time information visibility and realized ubiquitous manufacturing enterprises with proper functionalities of Enterprise Information Systems (EISs). This paper presents a flexible, modularized and re-configurable framework for the new generation RFID middleware system, named Gateway Operating System (GOS). It is an overall software solution designed and proposed not only to address basic functions of RFID middleware system, but also to overcome the particular challenges and requirements for real-life manufacturing scenarios. GOS aims to provide an easy-to-deploy, simple-to-use and affordable RFID middleware solution for manufacturing applications. A multi-agent based model, named gateway smart agent manager, is designed to enable the heterogeneous RFID devices in a “Plug and Play” fashion and to cope with the changes from these connected hardware devices. To guarantee the versatility and scalability of GOS, an XML (eXtensible Markup Language) based message exchanging protocol is designed to fulfill the communication and interactions between applications and devices. Based on this protocol, an easy-to-deploy and simple-to-use application manager is built to manage, configure and use the connected devices as well as deployed applications. The proposed GOS will provide a new referenced framework for the development of lightweight RFID middleware system for manufacturing environment.