Distributed Production Control System Research Articles

GERT-network optimization model for technologies of hazardous industry management

The paper introduces GERT-network optimization model for the technologies of hazardous manufacturing process management supported by the software of distributed production control systems. The development and implementation of the formal means for optimizing processes for the formation of managing hazardous industries using GERT-networks technology is presented. It allows to include into the model random deviations and uncertainty arising directly during the execution of each separate task of hazardous industries management technology.

Simultaneous Workload Allocation and Capacity Dimensioning for Distributed Production Control

Capacity dimensioning in production systems is an important task within strategic and tactical production planning which impacts system cost and performance. Traditionally capacity demand at each worksystem is determined from standard operating processes and estimated production flow rates, accounting for a desired level of utilization or required throughput times. However, for distributed production control systems, the flows across multiple possible production paths are not known a priori. In this contribution, we use methods from algorithmic game-theory and traffic-modeling to predict the flows, and hence capacity demand across worksystems, based on the available production paths and desired output rates, assuming non-cooperative agents with global information. We propose an iterative algorithm that converges simultaneously to a feasible capacity distribution and a flow distribution over multiple paths that satisfies Wardrop's first principle. We demonstrate our method on models of real-world production networks.

Controlling Myopic Behavior in Distributed Production Systems — A Classification of Design Choices

The future of manufacturing and logistics is currently envisioned under many names: Industry 4.0, Manufacturing 2.0, Physical Internet, etc. They share the vision of distributing control tasks to “smart” machines and products to attain higher flexibility, adaptability, and, in the light of increasingly complex and dynamic environmental conditions, higher logistic performance. The flip-side of such systems under distributed control is the rise of “myopic” (short-sighted) decision making, leading to system nervousness and loss of performance.Designing manufacturing (control) systems for distributed control hence is a significant challenge: With the system performance becoming an emergent property of the interplay of various decision making entities, system designers become conductors of societies of cyber-physical systems, seeking to balance the desirable traits of distributing control while limiting the negative effects of myopic decision making.In this contribution, we set out to help manufacturing system designers to better understand myopic behavior and the design decisions that are known to affect it. Our contribution can serve as a design aid for planners of distributed control systems by structuring the solution space of design decisions to control myopic behavior. By pointing to examples from various research streams, we provide guidance for system designers, seeking to maximize the performance of distributed production control systems.

Using the blender game engine for real-time emulation of production devices

This paper describes the principles, architecture and design details for using the Blender Game Engine in real-time production device emulation. An emulation system for a real material transport and handling production installation was implemented based on this. A prototype of a distributed production control system was run on top of the device emulation system to evaluate feasibility. A cardinal architectural principle is the clear distinction between production controllers and production devices. This principle, applicable through flexible communication middleware, enables the implementation of portable production controllers that execute transparently on either the emulated or the real production system. Production system engineers may take advantage of this approach to develop and gain confidence in complex production control solutions.

On-line production control of a flexible multi-cell manufacturing system operating in a highly dynamic environment

This paper examines the problem of real-time production control of a flexible multi-cell manufacturing system operating in a highly changing produce-to-order environment. A two-level distributed production control system (DPCS) is suggested and examined through a simulation study. The proposed production control system allows autonomous and simultaneous operation of each cell using heuristic rules. The heuristic rules take into account processing efficiency and time-based considerations. Simulation experiments show that the proposed DPCS robustly withstands variations in the product-mix, machine and handling device failures, and lead-time duration. The simulation results also show that the developed DPCS substantially decreases orders' lateness in comparison with that of a previous version of this model.

On-line scheduling in a multi-cell flexible manufacturing system

This paper deals with on-line scheduling in a multi-cell flexible manufacturing system, operating in a produce-to-order environment. A two level distributed production control system (DPCS) is developed and tested through a simulation study. The DPCS allows autonomous and simultaneous operation of each cell-controller, utilizing only local and short term information as well as simple heuristic rules. Simulation experiments show that the proposed DPCS achieves good results in throughput, tardiness of orders and WIP inventory level and that it is robust to machine and handling device failures.

Distributed dynamic production control in a multi-cell flexible manufacturing system

This paper deals with real-time control of a multi-cell flexible manufacturing system, operating in a produce-to-order environment. A two-level Distributed Production Control System (DPCS) is developed, the DPCS allowing autonomous operation of each cell-controller, utilizing only local and short term information as well as simple heuristic rules.