Application of the multi-agent approach in just-in-time production control system

2003 ◽  
Vol 17 (2) ◽  
pp. 90 ◽  
Author(s):  
Ruey-Shun Chen ◽  
Kun-Yung Lu ◽  
C.C. Chang
Keyword(s):  
Control System ◽  
Production Control ◽  
Just In Time ◽  
Time Production ◽  
Production Control System ◽  
Multi Agent

Production events graphical deduction model enabled real-time production control system for smart job shop

2017 ◽  
Vol 232 (16) ◽  
pp. 2803-2820 ◽  
Author(s):  
Chuang Wang ◽  
Pingyu Jiang ◽  
Tiantian Lu
Keyword(s):  
Control System ◽  
Real Time ◽  
Production Control ◽  
Job Shop ◽  
Material Flows ◽  
Time Production ◽  
Production Control System ◽  
Work In Process ◽  
Control Procedures ◽  
Manufacturing Resources

Under industry 4.0, the production control system of smart job shop should be able to real-time respond to various production events and effectively coordinate different kinds of manufacturing resources in good order according to their material flows. Production events enabled real-time production control system is good at responsibility and flexibility. Internet of things (IoT) can provide enormous real-time production events, which represent the change of material flows. However, some of production events seriously interfere with production control procedures. They sharply restrict the real-time capability of production control system. Thus, it is imperative that an efficient realization method of production control system, which is enabled by useful production events. Additionally, the control system should satisfy production control procedures visibility. For solving the problems, a production events graphical deduction model enabled real-time production control system for smart job shop is proposed in this article. Firstly, the manufacturing resources are divided into work in process related, operator-related, cutting tool-related, fixture tool-related, and measuring tool-related. And the material flows of different manufacturing resources in IoT-enabled smart job shop are described in detail. Secondly, the graphical deduction model of production event is put forward. Based on the model, the material flows of manufacturing resources in a process are segmented into several stages according to different production events. And then, the cooperation model of manufacturing resources is established by using the time and logical relationships between production events. Thirdly, the control model in a process is drawn from the cooperation model. Next, the entire control procedure of work in process production in IoT-enabled smart job shop is proposed. Finally, a small-scale IoT-enabled manufacturing system is used to verify the feasibility of the proposed model and methods.

Pompano Subsea Development: Production Control System and Umbilicals

1996 ◽  
Author(s):  
R.M. Pricharct ◽  
K.P. DeJohn ◽  
P. Farrell ◽  
C. Baggs ◽  
D. Harris
Keyword(s):  
Control System ◽  
Production Control ◽  
Production Control System

Systematic Approach to the Assessment of Production Control Functioning at Hazardous Production Facilities of Oil and Gas Industry

2021 ◽  
pp. 70-75
Author(s):  
R.I. Fatkhutdinov ◽  
◽  
Keyword(s):  
Control System ◽  
Systematic Approach ◽  
Oil And Gas ◽  
Production Control ◽  
Point Of View ◽  
Industrial Safety ◽  
Production Control System ◽  
System Functioning ◽  
Hazardous Production ◽  
Integral Indicator

One of the main causes of accidents at hazardous production facilities of oil and gas production is the inefficient work of production control over compliance with industrial safety requirements. At present there are no criteria for its assessment in the Russian legislation. It is established in the study that that production control in the industrial safety management system performs the role of «control» in accordance with the Shewhart-Deming cycle PDCA, and its main function is to work with nonconformities. In connection with the above, it is proposed to approach production control not only from the point of view of the process, but also from the system approach. To assess the system functioning, the criteria of «effectiveness», «efficiency», «integral indicator» are considered. It is established that from the point of view of proactivity in achieving the goals of production control, the most preferable is the assessment of the integral indicator of the production control system functioning. The considered existing and possible approaches to the assessment of the production control system and the statistical processing of the results of the expert assessment of nineteen parameters confirmed the need for a systematic approach. Based on this, the hypothesis of the production control system functioning is proposed and statistically substantiated, and four main parameters for calculating the integral indicator of the production control system functioning are considered. The built mathematical model based on the fuzzy logic clearly demonstrates the dependence of the integral indicator of the production control system functioning on the considered input parameters. The proposed proactive approach to the assessment of the production control system through nonconformity management is universal and applicable to the «control» function of any control system. It can also be used in the work of Rostechnadzor and be an incentive for enterprises to improve the quality, efficiency, and effectiveness of the production control system.

scholarly journals Dynamic Analysis and Optimization of a Production Control System under Supply and Demand Uncertainties

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Qiankai Qing ◽  
Wen Shi ◽  
Hai Li ◽  
Yuan Shao
Keyword(s):  
Control System ◽  
Performance Indicators ◽  
Demand Uncertainty ◽  
Production Control ◽  
Optimal Parameter ◽  
Dynamic Performance ◽  
Random Demand ◽  
Start Time ◽  
Supply Disruption ◽  
Production Control System

This study investigates the dynamic performance and optimization of a typical discrete production control system under supply disruption and demand uncertainty. Two different types of uncertain demands, disrupted demand with a step change in demand and random demand, are considered. We find that, under demand disruption, the system’s dynamic performance indicators (the peak values of the order rate, production completion rate, and inventory) increase with the duration of supply disruption; however, they increase and decrease sequentially with the supply disruption start time. This change tendency differs from the finding that each kind of peak is independent of the supply disruption start time under no demand disruption. We also find that, under random demand, the dynamic performance indicators (Bullwhip and variance amplification of inventory relative to demand) increase with the disruption duration, but they have a decreasing tendency as demand variance increases. In order to design an adaptive system, we propose a genetic algorithm that minimizes the respective objective function on the system’s dynamic performance indicators via choosing appropriate system parameters. It is shown that the optimal parameter choices relate closely to the supply disruption start time and duration under disrupted demand and to the supply disruption duration under random demand.

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