Modelling and simulation of dynamically integrated manufacturing systems

Fluctuations in demand patterns and products?? mixes, driven by continuous changes in customer requirements, are inducing significant changes on the operations of manufacturing organisations. How to respond to such changes rapidly and at minimum cost constitutes a major challenge for manufacturers. The DIMS project (Dynamically Integrated Manufacturing Systems) has developed an agent-based approach that enables manufacturing systems to be modelled using multi-agent systems such that optimal and timely responses to changes are generated from the interactions taking place within the multi-agents systems. This approach also incorporates a distributed discrete event simulation mechanism that enables ??what-if?? system configurations that have been generated through agent interactions to be evaluated dynamically for system restructure. This paper presents the approach with particular focus on the distributed simulation mechanism.     

Cooperative, hybrid agent architecture for real-time traffic signal control

This paper presents a new hybrid, synergistic approach in applying computational intelligence concepts to implement a cooperative, hierarchical, multiagent system for real-time traffic signal control of a complex traffic network. The large-scale traffic signal control problem is divided into various subproblems, and each subproblem is handled by an intelligent agent with a fuzzy neural decision-making module. The decisions made by lower-level agents are mediated by their respective higher-level agents. Through adopting a cooperative distributed problem solving approach, coordinated control by the agents is achieved. In order for the multiagent architecture to adapt itself continuously to the dynamically changing problem domain, a multistage online learning process for each agent is implemented involving reinforcement learning, learning rate and weight adjustment as well as dynamic update of fuzzy relations using an evolutionary algorithm. The test bed used for this research is a section of the Central Business District of Singapore. The performance of the proposed multiagent architecture is evaluated against the set of signal plans used by the current real-time adaptive traffic control system. The multiagent architecture produces significant improvements in the conditions of the traffic network, reducing the total mean delay by 40% and total vehicle stoppage time by 50%.     

An agent-based approach for e-manufacturing and supply chain integration

A major problem facing manufacturing organisations is how to provide efficient and cost-effective responses to the unpredictable changes taking place in a global market. This problem is made difficult by the complexity of supply chain networks coupled with the complexity of individual manufacturing systems within supply chains. Current systems such as manufacturing execution systems (MES), supply chain management (SCM) systems and enterprise resource planning (ERP) systems do not provide adequate facilities for addressing this problem. This paper presents an approach that would enable manufacturing organisations to dynamically and cost-effectively integrate, optimise, configure, simulate, restructure and control not only their own manufacturing systems but also their supply networks, in a co-ordinated manner to cope with the dynamic changes occurring in a global market. This is realised by a synergy of two emerging manufacturing concepts: Agent-based agile manufacturing systems and e-manufacturing. The concept is to represent a complex manufacturing system and its supply network with an agent-based modelling and simulation architecture and to dynamically generate alternative scenarios with respect to planning, scheduling, configuration and restructure of both the manufacturing system and its supply network based on the coordinated interactions amongst agents.     

制造系统控制结构现状和发展——从集中结构到动态结构

系统的决策控制系统依赖于系统内部的控制结构,制造系统的控制结构是先进制造与自动化领域的重要研究课题。制造系统的控制结构与计算机技术、生产设备自动化水平及制造模式等因素有关。早期的制造系统采用集中控制结构,而柔性制造系统和计算机集成制造系统多采用递阶控制结构。目前制造业已进入一个新时代,传统生产模式和企业结构无法适应不可预测的激烈的环境变化。因此,未来制造系统需要具备快速变化能力的动态控制结构。     

火电厂多代理控制系统

针对火电厂非线性、多变量和多控制目标的特点,设计了一个火电厂多代理控制系统(PPMACS).在PP MACS中,前馈控制代理(FFCAs)采用神经模糊系统进行决策,反馈控制代理(FBCAs)采用基于遗传算法的模糊系统进行决策.优化任务分解代理(OTDAs)通过一个优化代理和一个分解代理来进行多目标优化分解PPMACS的任务.协调代理根据运行条件协调PPMACS的各个代理.仿真结果显示了火电厂多代理控制系统能够实现火电单元机组的多目标运行和大范围负荷跟踪.神经网络、模糊逻辑和遗传算法是PPMACS中的智能代理进行决策的有效工具.     

Adaptive consensus control for stochastic nonlinear multiagent systems with full state constraints

In this paper, the consensus tracking problem is investigated for stochastic nonlinear multiagent systems with full state constraints and time delays. The barrier Lyapunov functions proposed for single‐agent constrained systems are constructively extended to solve the consensus problem for multiagent systems with the full state constraints. Some Lyapunov‐Krasovskii functionals are introduced to compensate for state time delays, which are inherent in the complicated nonlinear systems. Based on the variable separation technique, the difficulty arising from the nonstrict‐feedback structure is overcome. Under a directed communication topology, the distributed neuroadaptive control protocols are proposed to guarantee that all the follower agents follow the trajectory of the leader agent and the full state constraints are not violated. The effectiveness of the proposed distributed adaptive control approach is verified via simulation examples.     

An iterative agent bidding mechanism for responsive manufacturing

In today's market, the global competition has put manufacturing businesses in great pressures to respond rapidly to dynamic variations in demand patterns across products and changing product mixes. To achieve substantial responsiveness, the manufacturing activities associated with production planning and control must be integrated dynamically, efficiently and cost-effectively. This paper presents an iterative agent bidding mechanism, which performs dynamic integration of process planning and production scheduling to generate optimised process plans and schedules in response to dynamic changes in the market and production environment. The iterative bidding procedure is carried out based on currency-like metrics in which all operations (e.g. machining processes) to be performed are assigned with virtual currency values, and resource agents bid for the operations if the costs incurred for performing them are lower than the currency values. The currency values are adjusted iteratively and resource agents re-bid for the operations based on the new set of currency values until the total production cost is minimised. A simulated annealing optimisation technique is employed to optimise the currency values iteratively. The feasibility of the proposed methodology has been validated using a test case and results obtained have proven the method outperforming non-agent-based methods.     

Optimization-based path planning framework for industrial manufacturing processes with complex continuous paths

The complexity of robotic path planning problems in industrial manufacturing increases significantly with the current trends of product individualization and flexible production systems. In many industrial processes, a robotic tool has to follow a desired manufacturing path most accurately, while certain deviations, also called process tolerances and process windows, are allowed. In this work, a path planning framework is proposed, which systematically incorporates all process degrees of freedom (DoF), tolerances and redundant DoF of the considered manufacturing process as well as collision avoidance. Based on the specified process DoF and tolerances, the objective function and the hard and soft constraints of the underlying optimization problem can be easily parametrized to find the optimal joint-space path. By providing the analytical gradients of the objective function and the constraints and utilizing modern multi-core CPUs, the computation performance can be significantly improved. The proposed path planning framework is demonstrated for an experimental drawing process and a simulated spraying process. The planner is able to solve complex planning tasks of continuous manufacturing paths by systematically exploiting the process DoF and tolerances while allowing to move through singular configurations, which leads to solutions that cannot be found by state-of-the-art concepts.     

The performance of workload rules for order acceptance in batch chemical manufacturing

We investigate the performance of workload rules used to support customer order acceptance decisions in the hierarchical production control structure of a batch chemical plant. Customer order acceptance decisions need to be made at a point in time when no detailed information is available about the actual shop floor status during execution of the order. These decisions need therefore be based on aggregate models of the shop floor, which predict the feasibility of completing the customer order in time. In practice, workload rules are commonly used to estimate the availability of sufficient capacity to complete a set of orders in a given planning period. Actual observations in a batch chemical manufacturing plant show that the set of orders accepted needs to be reconsidered later, because the schedule turns out to be infeasible. Analysis of the planning processes used at the plant shows that workload rules can yields reliable results, however at the expense of a rather low capacity utilization. In practice this is often unacceptable. Since, solving a detailed scheduling problem is not feasible at this stage, this creates a dilemma that only can be solved if we can find more detailed aggregate models than workload rules can provide.     

Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment

In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and Many Robots (JSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by several transport robots. Hence, the JSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic multiagent model for the JSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using two sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.     

Multi-level decision making in reconfigurable machining systems using fuzzy logic

Reconfigurable machine tools (RMTs) are synthesised using the principles of modular design in order to achieve the required structural design for a particular part to be manufactured. In this paper, an effective method that uses multi-level fuzzy decisions to create dynamic optimal configurations of machine structures with respect to a given part geometry is presented. A system of modular machine configuration is utilised to arrive at machine configurations considering the fuzzy constraints that are pertinent in this process. With the utilisation of fuzzy decisions for the configuration system model, selection of optimal modular tool configurations is done. Decisions are made at a particular threshold level so as to verify the appropriateness of such decisions.     

Temporal Reasoning for a Collaborative Planning Agent in a Dynamic Environment

We present a temporal reasoning mechanism for an individual agent situated in a dynamic environment such as the web and collaborating with other agents while interleaving planning and acting. Building a collaborative agent that can flexibly achieve its goals in changing environments requires a blending of real-time computing and AI technologies. Therefore, our mechanism consists of an Artificial Intelligence (AI) planning subsystem and a Real-Time (RT) scheduling subsystem. The AI planning subsystem is based on a model for collaborative planning. The AI planning subsystem generates a partial order plan dynamically. During the planning it sends the RT scheduling subsystem basic actions and time constraints. The RT scheduling subsystem receives the dynamic basic actions set with associated temporal constraints and inserts these actions into the agent's schedule of activities in such a way that the resulting schedule is feasible and satisfies the temporal constraints. Our mechanism allows the agent to construct its individual schedule independently. The mechanism handles various types of temporal constraints arising from individual activities and its collaborators. In contrast to other works on scheduling in planning systems which are either not appropriate for uncertain and dynamic environments or cannot be expanded for use in multi-agent systems, our mechanism enables the individual agent to determine the time of its activities in uncertain situations and to easily integrate its activities with the activities of other agents. We have proved that under certain conditions temporal reasoning mechanism of the AI planning subsystem is sound and complete. We show the results of several experiments on the system. The results demonstrate that interleave planning and acting in our environment is crucial.     

A case-based system for process planning

Process planning is the phase of manufacturing that is concerned with the selection, and sequencing of manufacturing operations necessary to transform an initial stock material into a finished part. It is a tedious operation that requires highly skilled and experienced personnel of which there is currently great shortage. To disseminate the costly expertise and to reduce process planning times, which tend to add high costs to small batch production, many automated computer systems have been introduced and applied. All these systems, though, require the supervision of an experienced human being and fail to capture the knowledge and reasoning behind process planning decisions. The causes of the lack of an efficient automatic system for process planning are the unique characteristics of the domain. In contrast to other areas where knowledge-based and expert systems have been applied, process planning demands the solution of several different planning problems before a final solution is achieved. Every change introduced to the previous world model creates a completely new world model with its own, new constraints and preconditions that nedd to be satisfied. This, in turn, affects the applicability of the knowledge to the new model.

To provide solutions to the above problems we have designed a prototypical knowledge-based system that uses the high level, dynamic memory structures of MOPs (Memory Organization Packages), meta-MOPs and TOPs (Thematic Organization Packets) to rearrange its knowledge according to its experiences and to predict and avoid errors. To model the dynamic, sharable knowledge and the constantly changing world model a knowledge-representation language named TOLTEC has been designed and used in the system. Furthermore, we propose a novel planning method based on abstract plans and constraints that can solve planning problems in uncertain domains given incomplete information.     

Editorial

The paper describes and explains the basic structures for planning, control and execution in distributed robot systems consisting of several independent units. The structures can be used for a formal specification of architectures or for comparing different architectures and their corresponding interaction protocols. The specification is also the basis for implementing and changing dynamically architectures of already operating control systems in multi-robot applications.     

Agent-Based Systems for Intelligent Manufacturing: A State-of-the-Art Survey

Agent technology has been considered as an important approach for developing distributed intelligent manufacturing systems. A number of researchers have attempted to apply agent technology to manufacturing enterprise integration, supply chain management, manufacturing planning, scheduling and control, materials handling, and holonic manufacturing systems. This paper gives a brief survey of some related projects in this area, and discusses some key issues in developing agent-based manufacturing systems such as agent technology for enterprise integration and supply chain management, agent encapsulation, system architectures, dynamic system reconfiguration, learning, design and manufacturability assessments, distributed dynamic scheduling, integration of planning and scheduling, concurrent scheduling and execution, factory control structures, potential tools and standards for developing agent-based manufacturing systems. An extensive annotated bibliography is provided.     

Decentralized tube‐based model predictive control of uncertain nonlinear multiagent systems

This paper addresses the problem of decentralized tube‐based nonlinear model predictive control (NMPC) for a general class of uncertain nonlinear continuous‐time multiagent systems with additive and bounded disturbance. In particular, the problem of robust navigation of a multiagent system to predefined states of the workspace while using only local information is addressed under certain distance and control input constraints. We propose a decentralized feedback control protocol that consists of two terms: a nominal control input, which is computed online and is the outcome of a decentralized finite horizon optimal control problem that each agent solves at every sampling time, for its nominal system dynamics; and an additive state‐feedback law which is computed offline and guarantees that the real trajectories of each agent will belong to a hypertube centered along the nominal trajectory, for all times. The volume of the hypertube depends on the upper bound of the disturbances as well as the bounds of the derivatives of the dynamics. In addition, by introducing certain distance constraints, the proposed scheme guarantees that the initially connected agents remain connected for all times. Under standard assumptions that arise in nominal NMPC schemes, controllability assumptions, communication capabilities between the agents, it is guaranteed that the multiagent system is input‐to‐state stable with respect to the disturbances, for all initial conditions satisfying the state constraints. Simulation results verify the correctness of the proposed framework.     

Constraint satisfaction as a tool for modeling and checking feasibility of multiagent commitments

Commitments are being used to specify interactions among autonomous agents in multiagent systems. Various formalizations of commitments have shown their strength in representing and reasoning on multiagent interactions. These formalizations mostly study commitment lifecycles, emphasizing fulfillment of a single commitment. However, when multiple commitments coexist, fulfillment of one commitment may have an effect on the lifecycle of other commitments. Since agents generally participate in more than one commitment at a time, it is important for an agent to determine whether it can honor its commitments. These commitments may be the existing commitments of the agent as well as any prospective commitments that the agent plans to participate in. To address this, we develop the concept of commitment feasibility, i.e., whether it is possible for an agent to fulfill a set of commitments all together. To achieve this we generalize the fulfillment of a single commitment to the feasibility of a set of commitments. We then develop a solid method to determine commitment feasibility. Our method is based on the transformation of feasibility into a constraint satisfaction problem and use of constraint satisfaction techniques to come up with a conclusion. We show soundness and completeness of our method and illustrate its applicability over realistic cases.     

Reactive scheduling using a multi-agent model: the SCEP framework

Multi-agent systems have been successfully applied to the scheduling problem for some time. However, their use often leads to poorly unsatisfactory disappointing results. A new multi-agent model, called supervisor, customers, environment, producers (SCEP), is suggested in this paper. This model, developed for all types of planning activities, introduces a dialogue between two communities of agents leading to a high level of co-operation. Its two main interests are the following: first it provides a more efficient control of the consequences generated by the local decisions than usual systems to each agent, then the adopted architecture and behaviour permit an easy co-operation between the different SCEP models, which can represent different production functions such as manufacturing, supply management, maintenance or different workshops. As a consequence, the SCEP model can be adapted to a great variety of scheduling/planning problems. This model is applied to the basic scheduling problem of flexible manufacturing systems, and it permits a natural co-habitation between infinite capacity scheduling processes, performed by the manufacturing orders, and finite capacity scheduling processes, performed by the machines. It also provides a framework in order to react to the disturbances occurring at different levels of the workshop.     

A class of intelligent agents for coordinated control of outdoor terrain mapping UGVs

This article develops a systems- and control-oriented intelligent agent framework called the hybrid intelligent control agent (HICA) and describes its composition into multiagent systems. It is essentially developed around a hybrid control system core so that knowledge-based planning and coordination can be integrated with verified hybrid control primitives to achieve the coordinated control of multiple multi-mode dynamical systems. The scheme is applied to the control of a team of unmanned ground vehicles (UGVs) engaged in an outdoor terrain mapping task. Results are demonstrated experimentally.     

Toward a systems- and control-oriented agent framework.

This paper develops a systems- and control-oriented intelligent agent framework called the hybrid intelligent control agent (HICA), as well as its composition into specific kinds of multiagent systems. HICA is essentially developed around a hybrid control system core so that knowledge-based planning and coordination can be integrated with verified hybrid control primitives to achieve the coordinated control of multiple multimode dynamical systems. The scheme is applied to the control of teams of unmanned air and ground vehicles engaged in a pursuit-evasion war game. Results are demonstrated in simulation.