Production control of manufacturing systems: a multiple time scaleapproach

In this note, a stochastic production model containing processes with different time scales is developed. It is shown that if the time scales of the processes are very different, some hierarchical algorithms that are much more efficient than the standard policy iteration method can be developed to find the optimal production control. Moreover, if the time scales fall far apart, the optimal control of a deterministic limiting problem depending only on the mean characteristics of the processes can be used to approximate the optimal control of the original problem. The limiting problem has much lower dimension than its original counterpart and thus is much easier to solve. A numerical example is used to illustrate the potential of the proposed approach     

Time-optimal production control in a manufacturing system

This study demonstrates the use of control engineering techniques to regulate the product inventory levels of a workshop in a manufacturing system by manipulating the number of machines and the labour time employed during production. The production policy that minimizes the production time is obtained by time-optimal control techniques.     

An object-oriented manufacturing control system

This paper presents the development of an event-driven control architecture and its implementation in a physical simulator of a computerized manufacturing system using object-oriented techniques. The architecture was developed to improve the efficiency of handling concurrent control events in the DOS environment. In the implementation, the control system of the physical simulator consists of four distinct layers of control devices: a PC/386 computer, a microcontroller, I/O modules and the system's control devices such as motors, solenoids and sensors. A control program residing in the PC/386 coordinates system-level tasks such as event scheduling, while a BASIC program running on the microcontroller handles all low-level control tasks such as sensor monitoring and motion control. The concepts and developments presented in this paper should help in implementing an efficient control system for both CIM systems and their physical simulators.     

Production and maintenance control for manufacturing systems

This paper addresses the production and maintenance control problem of a failure prone manufacturing system consisting of one machine and producing one part type. The machine is assumed to have three working states: good, average and bad, and a failure state. In the three working states, the machine can produce parts and some of these parts are rejected with a rate depending on the machine state. In the failure state, no part is produced. The state transition of the machine is governed by a continuous-time Markov process. The jump rates from average and bad states to the good state are the preventive maintenance rates and the one from failure state to good state is the corrective maintenance rate. The production rate and the maintenance rates are optimized     

Production control of a pull system with production and demanduncertainty

We consider a continuous material-flow manufacturing system with an unreliable production system and a variable demand source which switches randomly between zero and a maximum level. The failure and repair times of the production system and the switching times of the demand source are assumed to be exponentially distributed random variables. The optimal production flow control policy that minimizes the expected average inventory carrying and backlog costs is characterized as a double-hedging policy. The optimal hedging levels are determined analytically by minimizing the closed-form expression of the cost function. We investigate two approximate single hedging policies. It is empirically shown that an approximate policy that uses a single hedging level which is the sum of a production uncertainty term and a demand uncertainty term gives accurate results for the expected average cost     

Optimal hedging point control for a failure-prone manufacturing system

This paper considers a manufacturing system with multiple operational modes producing one part type. The part processing time at each operational mode is exponentially distributed and its rate is controllable. The demand arrival is random and described by a Poisson process. By minimizing an infinite-horizon discounted expected cost function, the optimal service rate control is derived. It is proved that the optimal policy is of a hedging point structure by examining the properties of the optimal cost function such as convexity, monotonicity and asymptotic behaviours. The hedging points at different operational modes can be ordered according to their production capacities. The relationships of the hedging points with some system parameters are presented. These structural properties of the optimal control policy are helpful in finding simple and realistic suboptimal policies for practical manufacturing systems. A numerical example is given to demonstrate our results.     

Pheromone-based coordination for manufacturing system control

A pheromone-based coordination approach, which comes from the collective behavior of ant colonies for food foraging, is applied to control manufacturing system in this paper, aiming at handling dynamic changes and disturbances. The pheromone quantum of manufacturing cell is calculated inversely proportional to the cost, which can guarantee a minimal cost to process the orders. This approach has the capacity for optimization model to automatically find efficient routing paths for processing orders and to reduce communication overhead which exists in contract net protocol in shop floor control system. A prototype system is developed, and experiments confirm that pheromone-based coordination approach has excellent control performance and adaptability to disturbances in shop floor.     

Simultaneous control of maintenance and production rates of a manufacturing system with defective products

In the past three decades, studies of simultaneous maintenance and production planning have been focusing on age-dependent machine failure and inventory. This paper presents the interaction between defective products and optimal control of production rate, lead time and inventory. Our aim is to minimize the expected discounted overall cost due to maintenance activities, inventory holding and backlogs. Through Condition-Based Maintenance, we monitor in a real time the manufacturing system’s health by describing N operational states. We consider two maintenance states of a machine controlled by two decision variables: production and maintenance rates. The optimal policy is characterized by the dynamic programming solution to a piecewise deterministic optimal control problem. A numerical illustration and a sensitive analysis are developed with a set of parameters calibrated on an existing manufacturing system.     

Manufacturing system control with Petri nets in cellular manufacturing systems

This paper discusses the use of Petri nets in system control. A Petri net-model is developed for a Cellular Manufacturing System (CMS). The system control heuristics can be generated based on this Petri-net model. Some of the controls of the cell operation is demonstrated through the application of Petri nets. These controls include the control of work-in-process (WIP) inventory within cells and stock-on-hand (SOH) in the cells, the control of part-changes, and the control of disturbance handling.     

Modelling adaptive multi-agent manufacturing control with discrete event system formalism

This paper introduces an approach for modelling and designing multi-agent control architectures for agile manufacturing using a generic formalism based on a system-theoretic discrete event approach. To describe the details of the modelling strategy, we apply the proposed approach to a multi-agent network for Job flow control in a manufacturing plant. Two interacting types of autonomous controllers, Part Agents and Machine Agents, are in charge of controlling the part flow and the machine processing sequences. Both type of agents are first modelled as atomic discrete event systems and subsequently integrated in the model of the entire network of autonomous controllers. To improve the performance of the network of agents, we introduce a mechanism based on evolutionary algorithms adapting the agents’ decision laws that are encapsulated in agents’ states. Through network simulation, the algorithm continuously searches for effective decision laws, consequently adapting agent's behaviour to the current operational conditions of the manufacturing floor. Simulation results show the potentialities of the approach.     

Optimal control of production rate in a failure prone manufacturing system

We address the problem of controlling the production rate of a failure prone manufacturing system so as to minimize the discounted inventory, cost, where certain cost rates are specified for both positive and negative inventories, and there is a constant demand rate for the commodity produced. The underlying theoretical problem is the optimal control of a continuous-time system with jump Markov disturbances, with an infinite horizon discounted cost criterion. We use two complementary approaches. First, proceeding informally, and using a combination of stochastic coupling, linear system arguments, stable and unstable eigenspaces, renewal theory, parametric optimization, etc., we arrive at a conjecture for the optimal policy. Then we address the previously ignored mathematical difficulties associated with differential equations with discontinuous right-hand sides, singularity of the optimal control problem, smoothness, and validity of the dynamic programming equation, etc., to give a rigorous proof of optimality of the conjectured policy. It is hoped that both approaches will find uses in other such problems also. We obtain the complete solution and show that the optimal solution is simply characterized by a certain critical number, which we call the optimal inventory level. If the current inventory level exceeds the optimal, one should not produce at all; if less, one should produce at the maximum rate; while if exactly equal, one should produce exactly enough to meet demand. We also give a simple explicit formula for the optimal inventory level.     

Design and control of a single-stage dual-actuator system for high-precision manufacturing

This paper proposes a design for the linear state feedback control of the dual-actuator system, which is a dual-input single-output system for the high-precision manufacturing stage. The proposed control prevents saturation or reduces the unnecessary movement of the piezoelectric (PZT) actuator at the transient response by tracking the error between the estimated and actual positions of the coarse-actuator system at each control sample. Also, a new mechanism of the single-stage dual actuator is introduced. The axes of the stepper motor and the PZT actuator are co-axial. The coupling effects between the stepper motor and the PZT actuator are considered. Both the simulation and experiment results show that the proposed algorithm successfully prevents unwanted motions of the PZT actuator at the transient response. The experiment results show that the settling time and overshoot were enhanced by 45.7 and 95.9 %, respectively, for the proposed algorithm when the reference distance was 10 μm, which exceeds the stroke of the PZT actuator.     

An expert system for manufacturing cell control

An expert control system was designed to control an unmanned manufacturing cell in order to meet the operational requirements of a cellular Manufacturing System (CMS). In this paper, a knowledge-based three-layer control concept was used to build the cell control system. This cell control system is built to include workers' experience and problem handling ability. The cell control algorithms and heuristics are based on the pull system control principle. A Petri net is used to generate the cell control algorithm. The structure of the control system and the application of the Petri net method will be demonstrated.     

Production scheduling and inventory control for multi-stage manufacturing systems

In this paper, singular perturbation methods are used to synthesize high-gain error-actuated production control policies for a class of multi-stage manufacturing systems in which each manufacturing sub-system comprises either one or several production facilities. It is shown that when the manufacturing sub-systems are connected in cascade, each sub-system can be controlled in isolation, but that production control policies based on this approach lead to undesirable transient behaviour if the value of the feedback gain parameter is small. An alternative approach whereby production control policies are synthesized for the composite system leads to improved transient behaviour. However, if the value of the feedback gain parameter is large, both approaches are shown to yield good transient behaviour. In addition, it is demonstrated that trade-off between rapid adjustments in production rates and large inventory-level deviation can be achieved by selecting an appropriate value for the feedback gain parameter.     

Production decision support system for computerized manufacturing systems

The introduction of Computerized Manufacturing Systems (CMS) into the mid-volume manufacturing industry has been an important new step in the development of automated manufacturing. CMS's are computer-integrated systems which incorporate Direct Numerical Control (DNC) capabilities with automatic material handling to manufacture parts. Due to its complex nature, the task of production control in an CMS cannot rely on human effort alone. It is essential to develop a software package in the form of a Production Decision Support System (PDSS) to assist the production decision maker in operating this complex manufacturing facility.     

A structured adaptive supervisory control methodology for modelingthe control of a discrete event manufacturing system

Two basic measures, model complexity and model construction efficiency, are usually used to evaluate the implementability of a methodology for modeling the control of a discrete event manufacturing system (DEMS) on the shop floor. Many well-recognized methods are used to represent and analyze the dynamics of DEMs, but not many relevant applications have been found in developing control software for the shop floor due to their shortcomings in satisfying these two measures. The paper explores a methodology for modeling the control of a DEMS, which leads to ease of control software development, rather than a new representational/analytical tool, by significantly reducing the model complexity (in terms of the number of required control states) and improving the model construction efficiency. First, an extended finite machine, called a deterministic finite capacity machine (DFCM) with parallel computing capability is developed. Based on DFCMs, the complexity growth function of a DEMS control model is linear in the number of synthesized control components. Then, an automaton structure of a DFCM control model, called structured adaptive supervisory control (SASC), is developed. By referring to supervisory control theory, an SASC model is created with three function layers: acceptance, adaptive supervision, and execution. The well-defined structure ensures that the control model can be constructed systematically