Heterarchical Manufacturing Systems Research Articles

Distributed feedback control algorithm for dynamic truck loading scheduling problem

Distributed arrival time control (DATC) is a highly distributed feedback control algorithm for scheduling problems in heterarchical operation systems [V.V. Prabhu, Performance of real-time distributed arrival time control in heterarchical manufacturing systems, IIE Transactions 32 (2000) 323–331]. Although DATC has presented outstanding performance for the real-time scheduling problems in dynamic operational environment, little evidence exists for its effectiveness where earliness and tardiness of jobs are penalized at different rates. This study proposes an adapted DATC algorithm, called weighted multi-scale DATC ( wms-DATC), to handle a dynamic truck loading scheduling problem where each truck has different earliness and tardiness penalties during a loading operation in a shipment yard. To validate the performance of wms-DATC in the dynamic truck loading scheduling problem, computational experiments are conducted. The computational results reveal that wms-DATC provides promising results and outperforms existing dispatching rules.

Computing and Communication Quality of Service for Distributedtime-Scaled Simulation in Heterarchical Manufacturing Control

The authors present experimental results and empirical models that characterize the computing and communication workload for distributed time-scaled simulation used for distributed arrival time control (DATC) algorithms. In DATC application, real-time discrete event scheduling is achieved in a highly distributed manner by part entities (work pieces) that continuously control their arrival times at the queues for various resources based on feedback obtained from fast time-scaled distributed simulation. This study focuses on a range of systems of up to 180 parts and 16 machines. It focuses on real-time scheduling in heterarchical manufacturing systems using time-scaled distributed simulation. The quality (nearness to optimality) of schedules of such a system is limited by the speed and accuracy of the distributed simulations. In this paper designed experiments are used to characterize the Quality of Service (QoS). Based on experiments with time-scaled simulation for DATC algorithms, a regression model is built and is used to predict the latency and jitter on the network. The experimental results and regression model will enable systematic assessment of the QoS requirements and design of computing and communication architectures for the next generation of distributed manufacturing control systems.

Computing and Communication Quality of Service for Distributed Time-Scaled Simulation in Heterarchical Manufacturing Control

The authors present experimental results and empirical models that characterize the computing and communication workload for distributed time-scaled simulation used for distributed arrival time control (DATC) algorithms. In DATC application, real-time discrete event scheduling is achieved in a highly distributed manner by part entities (work pieces) that continuously control their arrival times at the queues for various resources based on feedback obtained from fast time-scaled distributed simulation. This study focuses on a range of systems of up to 180 parts and 16 machines. It focuses on real-time scheduling in heterarchical manufacturing systems using time-scaled distributed simulation. The quality (nearness to optimality) of schedules of such a system is limited by the speed and accuracy of the distributed simulations. In this paper designed experiments are used to characterize the Quality of Service (QoS). Based on experiments with time-scaled simulation for DATC algorithms, a regression model is built and is used to predict the latency and jitter on the network. The experimental results and regression model will enable systematic assessment of the QoS requirements and design of computing and communication architectures for the next generation of distributed manufacturing control systems.

Set-up games: a heuristic game-theoretic approach to set-up decisions for heterarchical manufacturing systems

Heterarchical control systems have emerged as promising alternatives to conventional centralized and hierarchical shop floor control systems. However, research to date has largely ignored long-term control issues such as the timing of resource reconfigurations, which are vital for the avoidance of myopic decision-making. As traditional analysis techniques implicitly assume centralized analysis and implementation of policies, they are not directly applicable to heterarchical environments. A non-cooperative game-theoretic planning technique for autonomous analyses of reconfiguration decisions by heterarchical machine controllers is described. Under the proposed system, a machine controller evaluates a set-up game to determine appropriate actions when faced with a sequence-independent reconfiguration decision. The prescribed action is defined by a pure or mixed-strategy equilibrium of the set-up game. By evaluating a game, the machine controller accounts for the potential actions and reactions of other machine controllers in the system. The approach allows the autonomous machine controller to make set-up decisions about future actions while preserving the independence characteristic of controllers in heterarchical environments.

A genetic approach for adaptive multiagent control in heterarchical manufacturing systems

In this paper, we apply genetic algorithms to adapt the decision strategies of autonomous controllers in a part-driven heterarchical manufacturing system. The control agents use pre-assigned decision rules only for a limited amount of time, and obey a rule replacement policy propagating the most successful rules to the subsequent populations of concurrently operating agents. The twofold objective of this approach is to automatically optimize the performance of the control system during the steady-state unperturbed conditions of the manufacturing floor, and to improve the reactions of the agents to unforeseen disturbances (e.g., failures, shortages of materials) by adapting their decision strategies. Results on a detailed discrete event model of a multiagent heterarchical manufacturing system confirm the effectiveness of the approach.

Closed-loop real-time cooperative decision-making dynamics in heterarchical manufacturing systems : Neil A. Duffie, Vittaldas V. Prabhu, Patrick O. Kaltjob, v21, n6, 2002, pp409–418

Closed-loop real-time cooperative decision-making dynamics in heterarchical manufacturing systems : Neil A. Duffie, Vittaldas V. Prabhu, Patrick O. Kaltjob, v21, n6, 2002, pp409–418

Closed-loop real-time cooperative decision-making dynamics in heterarchical manufacturing systems

Abstract Distribution of control in heterarchical manufacturing systems creates challenges in making and modelling cooperative decisions, such as when to produce discrete parts on multiple manufacturing machines. Furthermore, the dynamics of decision-making interactions between highly autonomous entities in these systems are poorly understood and can be undependable because of the absence of a master controller or optimizer. To investigate these phenomena, a system for distributed control of the arrival time of discrete parts in multiple-machine, multiple-processing-step manufacturing systems has been developed and is described in this paper. In the system, continuous control laws replace heuristics, and part arrival times are adjusted locally using feedback of expected completion times. The reported results confirm that the dynamics of the distributed systems are favorable, with arrival times converging exponentially to theoretically predictable values regardless of whether or not a set of arrival times exists that, given the machine resources available, results in parts being completed on their due dates. In the paper, closed-form solutions are obtained for nonlinear, discontinuous differential equations that describe system behavior in different dynamic regions and explain arrival time convergence and intrinsic cooperation between discrete parts in their competition for production machinery. Furthermore, the systems are shown to be responsive to real-time disturbances that can be caused by rush orders, machine failures, and changes in part processing times. These results build confidence in design, implementation, and operation of distributed control systems for manufacturing, regardless of whether they are heuristic or control-law based, even though they appear to behave in a chaotic manner due to the interactions of highly autonomous entities.

Evolutionary adaptation of dispatching agents in heterarchical manufacturing systems

We propose a new approach to job flow adaptive operational control in advanced manufacturing systems. The major feature of the method is the distribution of the control tasks among completely autonomous intelligent agents. Namely, agents are implicitly coordinated by a nature-analogous adaptation mechanism, which continuously tunes the free parameters of the control law of each agent. The proposed approach is effective and reactive to severe disturbances and changes in the manufacturing environment. Simulation experiments illustrate the operational distributed approach and its response to faults.

Performance of real-time distributed arrival time control in heterarchical manufacturing systems

A highly distributed feedback control algorithm for autonomous part entities in heterarchical manufacturing systems is presented in this paper. A difference equation-based model is developed to analyze the discrete time dynamics of the resulting nonlinear control system. Control parameters are found analytically that guarantee that the system is bounded under disturbances. The dynamic response to: (i) changes in due dates; and (ii) the bulk arrival of parts is presented. The ability of the system to exponentially reduce the mean and variance of due-date deviation in the single machine case makes it an attractive option for real-time control of just-in-time production.

Performance of real-time distributed arrival time control in heterarchical manufacturing systems

A highly distributed feedback control algorithm for autonomous part entities in heterarchical manufacturing systems is presented in this paper. A difference equation-based model is developed to analyze the discrete time dynamics of the resulting nonlinear control system. Control parameters are found analytically that guarantee that the system is bounded under disturbances. The dynamic response to: (i) changes in due dates; and (ii) the bulk arrival of parts is presented. The ability of the system to exponentially reduce the mean and variance of due-date deviation in the single machine case makes it an attractive option for real-time control of just-in-time production.

A cascading auction protocol as a framework for integrating process planning and heterarchical shop floor control

A new contract net-style auction protocol is proposed as a framework for integrating process planning and shop floor control in heterarchical manufacturing systems. Process planning is partitioned into on-line and off-line activities; off-line process planning decisions are represented in a graph format and used as input for on-line process planning activities performed by machine controllers. Triggered by the opening round of an auction, the final on-line stages of process planning are dovetailed with the resource allocation process in the shop floor control system. The auction process allows final process planning decisions to be based on timely information, relying on the distribution of static process planning information rather than the distribution of a model of dynamic shop floor status and allowing a controller to identify all the primary and secondary resources and operations that must be provided for the incremental processing of a part.

Nonlinear dynamics in distributed arrival time control of heterarchical manufacturing systems

Heterarchical control architectures with fully distributed control have been developed in order to improve responsiveness and effectiveness of manufacturing shop-floor control systems. The dynamics of these highly distributed systems have been difficult to predict particularly when control is based on heuristics. In this paper a dynamical model is developed for a single machine processing an arbitrary number of parts. The structure of the system, which requires queuing of parts when they arrive at a machine, leads to nonlinearities such as dead-zone and discontinuities. A continuous arrival time controller of the integrating type is used that results in a system that can be modeled using nonlinear differential equations that can be solved using a method due to Filippov (1960, 1988). This enables prediction of trajectories of part arrival times and derivation of closed form expressions for steady-state values. The analytical model for the dynamics is validated and the dynamic response of the system is illustrated using numerical simulation.

Dynamics of Real-Time Distributed Scheduling in Multiple-Machine Heterarchical Manufacturing Systems

Heterarchical manufacturing systems with highly-distributed, real-time control of arrival times of parts exhibit high levels of robustness and adaptability to changes in machine availability, part mix, processing times, and due dates. However, distribution of control and queuing in these systems makes analysis and modeling difficult, particularly when parts proceed through multiple processing steps and these processing steps can be performed by more than one machine. In this paper, a general approach is presented for modeling these systems. Examples of multiple-machine and multiple-processing-step systems are presented, illustrating their behavior and closed-form solutions obtained of discontinuous differential equations that represent their dynamics.

Modelling and Analysis of Heterarchical Manufacturing Systems Using Discontinuous Differential Equations

Heterarchical manufacturing systems with distributed arrival time control provide robustness by adapting to disturbances in real time without explicit notification and compensation. Part processing discontinuously couples entities in these systems and makes their behavior highly nonlinear. Discontinuous differential equations that model the dynamics of a multitude of highly distributed part arrival time controllers can be solved using a method due to Filippov, resulting in closed-form equations that characterize system dynamics and convergence. The global performance of the distributed arrival time control is found to compare favorably with dispatching rules currently used in industry.

Modelling and Analysis of Nonlinear Dynamics in Autonomous Heterarchical Manufacturing Systems Control

Heterarchical architectures characterized by fully distributed control and cooperation among loosely-coupled autonomous entities have been proposed for controlling automated manufacturing systems. A non-linear, control-theoretic analytical model is developed in this paper to represent the dynamical behavior of closed-loop distributed controllers in autonomous part-driven heterarchical systems. The model and the control law introduced for adjusting arrival times of parts to be processed allows the dynamics of these highly distributed, highly non-linear control systems to be analyzed. Analytical and numerical results are used to validated the model and illustrate robustness to uncertainties such as new part arrivals and machine failures of the fully distributed, heterarchical system control.

Real-time distributed scheduling of heterarchical manufacturing systems

Lack of master-slave relationships and absence of a centralized supervisor in heterarchical manufacturing systems prohibits the use of centralized scheduling. A fully distributed scheduling method for these systems is described in this paper. Entities in the system continually generate tentative local schedules in real time using a cooperative scheduling heuristic and select the beast schedule for execution. Tentative schedules are evaluated in time-scaled, distributed simulations using software that is a replica of the real system software. The state of the real system is copied into this virtual system at the beginning of each simulation, eliminating cumulative uncertainties and immediately incorporating equipment failures and other unexpected events into schedule evaluation. Experimental results illustrate the real-time distributed scheduling method and its response to faults.

Real-time distributed scheduling of heterarchical manufacturing systems : Neil A. Duffie and Vittaldas V. Prabhu, pp. 94–107

Real-time distributed scheduling of heterarchical manufacturing systems : Neil A. Duffie and Vittaldas V. Prabhu, pp. 94–107

Synthesis of Heterarchical manufacturing systems

The complexity of computer-integrated manufacturing systems with hierarchical architectures grows rapidly with size, resulting in accompanying high costs of development, installation, operation, maintenance, and modification. It is also difficult to introduce fault tolerance into hierarchical structures without significantly increasing system complexity. In the place of a hierarchical structure for manufacturing systems, “cooperative heterarchies” have been suggested in which there are no “higher level” controllers in the system, and each member conforms to certain rules in order to obtain certain privileges. Heterarchical architures offer prospects of: reduced complexity and improved fault tolerance by localizing information and control; reduced software development costs by eliminating supervisory levels; and higher main tainability and modifiability due to improved modularity and self-configurability. Unfortunately, they do not resemble hierarchical management and production control structures, and traditional deterministic design philosophies and methodologies are not necessarily appropriate. This paper is devoted to a discussion of design objectives and philosophies for heterarchical systems. An automated mold production system with a heterarchical architecture is discussed as an example.