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1

Zhuchenko, O. A. "CONTROL SYSTEM OF CARBON PRODUCTION." Scientific notes of Taurida National V.I. Vernadsky University. Series: Technical Sciences 1, no. 1 (2020): 72–78. http://dx.doi.org/10.32838/2663-5941/2020.1-1/13.

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2

Bezek John, D., and M. Kogge Peter. "Inferencing production control computer system." Computer Integrated Manufacturing Systems 10, no. 2 (May 1997): 178–79. http://dx.doi.org/10.1016/s0951-5240(97)84352-2.

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3

John, Bezek, and Kogge Peter. "Inferencing production control computer system." Computer Integrated Manufacturing Systems 10, no. 1 (February 1997): 85. http://dx.doi.org/10.1016/s0951-5240(97)88076-7.

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4

Reader, J. R. "Planning a production control system." Production Engineer 65, no. 9 (1986): 31. http://dx.doi.org/10.1049/tpe.1986.0214.

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5

Yu, Jia, and Hong Lin Zhao. "All-Electric Subsea Production Control System." Applied Mechanics and Materials 251 (December 2012): 196–200. http://dx.doi.org/10.4028/www.scientific.net/amm.251.196.

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All-Electric Subsea Production Control System, which has many advantages over conventional Multiple Electric-hydraulic control system, is the development tendency of subsea production control system. Being based on principle of all-electric control system, its advantages, working principle and key technologies were analyzed in this paper, which could be a reference for further study.
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6

John, Bezek, and Kogge Peter. "5517642 Inferencing production control computer system." Expert Systems with Applications 11, no. 4 (January 1996): V. http://dx.doi.org/10.1016/s0957-4174(97)86758-9.

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7

Morimoto, T., K. Hatou, and Y. Hashimoto. "Intelligent Control for Plant Production System." IFAC Proceedings Volumes 28, no. 4 (May 1995): 139–44. http://dx.doi.org/10.1016/s1474-6670(17)45554-6.

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8

Rutka, Romualdas. "Automatic System for Energy Production Control." IFAC Proceedings Volumes 31, no. 24 (September 1998): 25–26. http://dx.doi.org/10.1016/s1474-6670(17)38498-7.

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9

Moreno, L., M. A. Salichs, R. Aracil, and P. Campoy. "A Production System for AGVS Control." IFAC Proceedings Volumes 23, no. 3 (September 1990): 659–63. http://dx.doi.org/10.1016/s1474-6670(17)52635-x.

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10

Pons, Dirk. "System model of production inventory control." International Journal of Manufacturing Technology and Management 20, no. 1/2/3/4 (2010): 120. http://dx.doi.org/10.1504/ijmtm.2010.032895.

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11

Trammell, Carmen J., Leon H. Binder, and Cathrine E. Snyder. "The automated production control documentation system." ACM Transactions on Software Engineering and Methodology (TOSEM) 1, no. 1 (January 2, 1992): 81–94. http://dx.doi.org/10.1145/125489.122826.

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12

Manevich, V. E. "Control system evolution in glass production." Glass and Ceramics 46, no. 6 (June 1989): 250–52. http://dx.doi.org/10.1007/bf00680286.

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13

Kasakow, Georg, and Jan Christian Aurich. "Reactive Production Control - Information to Control a Production." Applied Mechanics and Materials 869 (August 2017): 151–58. http://dx.doi.org/10.4028/www.scientific.net/amm.869.151.

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Sustainable protection and expansion of the competitiveness of the industrial location Germany is an important topic of current research. How can manufacturing companies in high wage countries counter the challenges of market turbulences? Crucial for the competitiveness of these companies is the compensation of wage and salary costs through highly efficient processes. A promising approach is to increase the responsiveness of a company. Responsiveness means, to react as quickly as possible to events or changes of the market, which are not yet evident and can not be foreseen at the time of production system planning. An increase of responsiveness enables companies to react quickly and flexible to market turbulences. The question arises, which market information are relevant, to which a company has to react as quickly as possible in order to handle market turbulence. This paper addresses this issue and identifies relevant information, that are already known during the phase of product development, and the unknown information of the market, to which an existing production system has to react in order to compensate market turbulences. The interplay of known and unknown information enables a responsive production control. The origin and usage of this information, which enables reactive production control, are a part of this article and are explained therein.
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14

Sartika, Erwani Merry, T. Rudi Sarjono, and Kevin Christian. "Modular Production System Control Using Supervisory Control Theory Method." Journal of Physics: Conference Series 1858, no. 1 (April 1, 2021): 012095. http://dx.doi.org/10.1088/1742-6596/1858/1/012095.

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15

Nichols, M. A. "IRRIGATION SYSTEM AND CULTURAL PRACTICES FOR CROP PRODUCTION UNDER CONTROL ENVIRONMENT PRODUCTION SYSTEM." Acta Horticulturae, no. 710 (June 2006): 71–78. http://dx.doi.org/10.17660/actahortic.2006.710.4.

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16

Šarga, Patrik, and Tomáš Záboly. "MODERNIZATION OF THE TRANSPORT SYSTEM CONTROL OF THE PRODUCTION SYSTEM." TECHNICAL SCIENCES AND TECHNOLOGIES, no. 4(18) (2019): 141–47. http://dx.doi.org/10.25140/2411-5363-2019-4(18)-141-147.

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Urgency of the research. Nowadays, it is crucial to keep up with modern technologies. Therefore, this work aims to modernize the production system Festo MPS 500. Thanks to this, it will be possible to apply to the system technologies meeting the latest trends in Industry 4.0. The MPS 500 system prepared in this way can be used to research new trends in accordance with Industry 4.0. The modernized MPS 500 system will also find use in the education of students in the field of automation and mechatronics so that they are sufficiently prepared for practice. Target setting. The goal of the research was to modernize the transport system of the modular production system Festo MPS 500 according to Industry 4.0 platform. Actual scientific researches and issues analysis. When upgrading the system MPS 500 and preparing this paper, we took into account both current sources – publications and papers dealing with the current state of Industry 4.0 and modular production systems as well as existing modular production systems based on Industry 4.0 platform. Uninvestigated parts of general matters defining. At this stage of the research, data acquisition from the system MPS 500 and interconnection with the cloud was not realized. The research objective. The purpose of this article is to modernize the MPS 500, which will allow focusing on Industry 4.0 research specifically for the deployment of Cyber-physical systems, Internet of Things, Big Data, Cloud Computing. The statement of basic materials. Effective research of the new technologies in the industry requires to use modern systems which meet the criteria of Industry 4.0 platform. So the original system Festo MTS 500 was upgraded by systems from Siemens. Conclusions. The main aim of this work was to modernize the transport system of the production system MPS 500. Elements of the system management were changed, and a new control program was created in the TIA Portal environment. The functionality of the MPS 500 was subsequently verified, where the full functionality of the system was confirmed. It makes the MPS 500 ready for further expansion in accordance with Industry 4.0.
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17

IRAVANI, SEYED M. R., and IZAK DUENYAS. "Integrated maintenance and production control of a deteriorating production system." IIE Transactions 34, no. 5 (May 2002): 423–35. http://dx.doi.org/10.1080/07408170208928880.

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18

Potamianos, J., and A. J. Orman. "An Interactive Dynamic Inventory-Production Control System." Journal of the Operational Research Society 47, no. 8 (August 1996): 1017. http://dx.doi.org/10.2307/3010409.

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19

YAN, Hongsen. "Production Control of Tree-structured Manufacturing System." Journal of Mechanical Engineering 45, no. 08 (2009): 148. http://dx.doi.org/10.3901/jme.2009.08.148.

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20

Kapovsky, Boris, Alexander Zakharov, and Marina Nikitina. "Intelligent control system for minced meat production." Potravinarstvo Slovak Journal of Food Sciences 14 (September 28, 2020): 750–58. http://dx.doi.org/10.5219/1342.

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This article presents the theoretical aspects of developing a control system for the processing of frozen raw meat by cutters in automatic mode. The method for analytical calculation of the productivity rate of meat cutting by a cutter with a screw tooth provides an accuracy for which relative error does not exceed 6%. The authors show automatic process control in minced meat production using a control system computer (CSC), with the aim of building an automatic control system (ACS) for chopping raw materials frozen in the form of blocks. The task of ACS synthesis was solved: the system structure and its elements were chosen, the topology of their cause-and-effect relationships and an algorithm of control devices were developed, and their parameters were determined. The ACS’s control loop scheme for raw material cutting speed was realized, where an assembly of devices was chosen as the object of management (OM): the squirrel cage induction motor (SCIM) of the cutting mechanism drive; the frequency converter (FC) of the supply voltage, which changes the rotation speed of the SCIM (the rotation speed of the milling cutter); and the milling cutter of the chopper. The shaping filter method was used, to predict the size of the meat chips produced, to modulate the perturbation acting on the system from the load side. Based on the single-stage chopping of raw meat, an automatic line is created for producing meat products, with a minced meat quality management system based on artificial intelligence on the principle of ‘unmanned technology’.
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21

Tokarev, A. V. "Production control system specified quality sausage products." Proceedings of the Voronezh State University of Engineering Technologies, no. 1 (April 12, 2016): 63–69. http://dx.doi.org/10.20914/2310-1202-2016-1-63-69.

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22

Minemura, Takahisa, Toshiyuki Amemiya, and Kazutoshi Horiuchi. "Production Control System for CIM for Shipbuilding." Journal of the Society of Naval Architects of Japan 1991, no. 170 (1991): 827–41. http://dx.doi.org/10.2534/jjasnaoe1968.1991.170_827.

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23

Huang, Jian Bing, Duo Ning, and Zhong Jun Xiao. "Distributed Control System for Papermaking Production Line." Applied Mechanics and Materials 44-47 (December 2010): 242–46. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.242.

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According to the features of papermaking production line, a new kind domestic distributed control system MACS is developed here for the practice application. The methods of hardware component and software realization for the control system are proposed. Practices prove that the novel distributed control system for the paper-making production line can run automatically, safely and smoothly which can achieve high performance index, and it can sharply reduce the cost of construction and installation as well as the cost of maintenance.
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24

MERTINS, KAI, ROLF ALBRECHT, VIKTOR STEINBERGER, and FRANK-WALTER LUTZE. "Flexible software system for production-adequate control." Production Planning & Control 3, no. 2 (April 1992): 183–98. http://dx.doi.org/10.1080/09537289208919388.

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25

Potamianos, J., and A. J. Orman. "An Interactive Dynamic Inventory-Production Control System." Journal of the Operational Research Society 47, no. 8 (August 1996): 1017–28. http://dx.doi.org/10.1057/jors.1996.128.

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26

Aarts, Robert J., Annamari Suviranta, Pauliina Rauman‐Aalto, and Pekka Linko. "An expert system in enzyme production control." Food Biotechnology 4, no. 1 (January 1990): 301–15. http://dx.doi.org/10.1080/08905439009549742.

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27

Qing Zhang, G. G. Yin, and E. K. Boukas. "Optimal control of a marketing-production system." IEEE Transactions on Automatic Control 46, no. 3 (March 2001): 416–27. http://dx.doi.org/10.1109/9.911418.

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28

Denardo, Eric V., and Christopher S. Tang. "Linear Control of a Markov Production System." Operations Research 40, no. 2 (April 1992): 259–78. http://dx.doi.org/10.1287/opre.40.2.259.

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29

Suzuki, Y., K. Mori, S. Hori, H. Kasashima, T. Itoh, J. Mori, and H. Torikosi. "Autonomous Decentralized Steel Production Process Control System." IFAC Proceedings Volumes 22, no. 15 (September 1989): 63–67. http://dx.doi.org/10.1016/b978-0-08-037870-1.50016-2.

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30

Morimoto, T., K. Hatou, and Y. Hashimoto. "Intelligent control for a plant production system." Control Engineering Practice 4, no. 6 (June 1996): 773–84. http://dx.doi.org/10.1016/0967-0661(96)00068-8.

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31

Li, Hui, and Liming Liu. "Production control in a two-stage system." European Journal of Operational Research 174, no. 2 (October 2006): 887–904. http://dx.doi.org/10.1016/j.ejor.2005.03.036.

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32

White, Anthony S., and Michael Censlive. "Control system analysis of labour supply flows in production systems." Journal of Manufacturing Systems 37 (October 2015): 316–27. http://dx.doi.org/10.1016/j.jmsy.2014.08.001.

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33

Chetouane, Fatah, Zdenek Binder, and Linda Little. "Control and robustness for production system application to electroplating systems." IFAC Proceedings Volumes 32, no. 2 (July 1999): 331–36. http://dx.doi.org/10.1016/s1474-6670(17)56057-7.

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34

Zhang, Haitao, Dunbing Tang, Kun Zheng, and Adriana Giret. "Production control strategy inspired by neuroendocrine regulation." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 232, no. 1 (April 1, 2016): 67–77. http://dx.doi.org/10.1177/0954405416639889.

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Due to the international business competition of modern manufacturing enterprises, production systems are forced to quickly respond to the emergence of changing conditions. Production control has become more challenging as production systems adapt to frequent demand variation. The neuroendocrine system is a perfect system which plays an important role in controlling and modulating the adaptive behavior of organic cells under stimulus using hormone-regulation principles. Inherited from the hormone-regulation principle, an adaptive control model of production system integrated with a backlog controller and a work-in-progress controller is presented to reduce backlog variation and keep a defined work-in-progress level. The simulation results show that the presented control model is more responsive and robust against demand disturbances such as rush orders in production system.
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35

May, Marvin Carl, Andreas Kuhnle, and Gisela Lanza. "Digitale Produktion und intelligente Steuerung/Digital Production and Intelligent Production Control – Integrating digital and real-world production for adaptive and automated control." wt Werkstattstechnik online 110, no. 04 (2020): 255–60. http://dx.doi.org/10.37544/1436-4980-2020-04-89.

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Im Rahmen der stufenweisen Umsetzung von Industrie 4.0 erreicht die Vernetzung und Digitalisierung die gesamte Produktion. Den physischen Produktionsprozess nicht nur cyber-physisch zu begleiten, sondern durch eine virtuelle, digitale Kopie zu erfassen und zu optimieren, stellt ein enormes Potenzial für die Produktionssystemplanung und -steuerung dar. Zudem erlauben digitale Modelle die Anwendung intelligenter Produktionssteuerungsverfahren und leisten damit einen Beitrag zur Verbreitung optimierender adaptiver Systeme.   In the wake of implementing Industrie 4.0 both integration and digitalization affect the entire production. Physical production systems offer enormous potential for production planning and control through virtual, digital copies and their optimization, well beyond purely cyber-physical production system extensions. Furthermore, digital models enable the application of intelligent production control and hence contribute to the dissemination of adaptively optimizing systems.
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36

Scheifele, Stefan, Jens Friedrich, Armin Lechler, and Alexander Verl. "Flexible, Self-configuring Control System for a Modular Production System." Procedia Technology 15 (2014): 398–405. http://dx.doi.org/10.1016/j.protcy.2014.09.094.

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37

Young, K. W., R. Muehlhaeusser, R. S. H. Piggin, and P. Rachitrangsan. "Agile control systems." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 215, no. 2 (February 1, 2001): 189–95. http://dx.doi.org/10.1243/0954407011525575.

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To cope with unpredictable demand and a wide variety of products, future production systems require agility. To realize manufacturing agility, the control system has to respond and adapt to variations in real-world, dynamic production environments. The control system has to promote requirements such as reduced complexity, increased flexibility, adaptation in real time, extensibility, heterogeneity and autonomous operation. A control system architecture is proposed ensuring manufacturing agility by adapting quickly and cheaply to changes in the production environment.
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38

Cao, Ju Jiang, and Yan Li. "An Intelligent Control System for Digital Workshop Production." Applied Mechanics and Materials 16-19 (October 2009): 75–83. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.75.

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In general, production control of digital workshops is a complicated systematical engineering. Because of lacking the methods and means of acquiring the manufacturing data in real time, the traditional production control methods cannot satisfy the real production control requirement of digital workshops. In this paper, we put forward an intelligent control system for digital workshop production. The main contributions include three aspects: Firstly, we introduce and adopt Radio Frequency Identification (RFID) technology as a means to acquire the manufacturing information in real time produced by the bottom-level manufacturing devices. Secondly, we develop the wcPML (workshop control with Physical Markup Language) and utilize it to act as a media to realize the information visualized description and transmission between the top-level control systems and bottom-level manufacturing sites of workshops. Thirdly, we adopt Java Web technology and wireless LAN to build the infrostructure for the intelligent control system for workshop production. Some key enabling technologies for the intelligent control system also are presented and described following with some concluding remarks.
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39

Tan, B. "Production control of a pull system with production and demand uncertainty." IEEE Transactions on Automatic Control 47, no. 5 (May 2002): 779–83. http://dx.doi.org/10.1109/tac.2002.1000272.

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40

Milkiewicz, F. "Operative Control of Production System with Switched-Over Production Processes Series." IFAC Proceedings Volumes 20, no. 9 (August 1987): 525–32. http://dx.doi.org/10.1016/s1474-6670(17)55761-4.

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41

Wu, Han, Gerald Evans, and Ki-Hwan Bae. "Production control in a complex production system using approximate dynamic programming." International Journal of Production Research 54, no. 8 (September 11, 2015): 2419–32. http://dx.doi.org/10.1080/00207543.2015.1086035.

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42

Klosterhalfen, Steffen T., Falk Holzhauer, and Moritz Fleischmann. "Control of a continuous production inventory system with production quantity restrictions." European Journal of Operational Research 268, no. 2 (July 2018): 569–81. http://dx.doi.org/10.1016/j.ejor.2018.02.001.

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43

Andrade, José Henrique de, Francisco Andrea Simões Braga, Luciano Campanini, Josadak Astorino Marçola, and Bruna Carvalho Nunes Rocha. "Production Planning and Control (PPC): production pointing system deployment, use and unfolding." Independent Journal of Management & Production 11, no. 5 (September 1, 2020): 1551. http://dx.doi.org/10.14807/ijmp.v11i5.1299.

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The growing need for efficiency and effectiveness in production systems management has increased the importance of processes and activities related to Production Planning and Control (PPC). Several authors point out that the more dynamic, competitive market, with higher demands from consumers and the increasing insertion of technology to support decision making, make the need for robust management processes imperative. In line with this scenario, the present work aims to present the report of a process of implementation, use and the consequences of a production pointing system. To achieve the proposed objective, a literature review was conducted on the topics of interest and a case study in a company that manufactures hospital medical products in the interior of São Paulo state. As main results, it was observed that the adoption and evolution of a production pointing system generated significant gains for the company's Production Control and increased maturity in terms of Production Management, but required significant efforts and investments to maintain and evolve the production deploying process.
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44

Blanchini, F., S. Miani, and W. Ukovich. "Control of production-distribution systems with unknown inputs and system failures." IEEE Transactions on Automatic Control 45, no. 6 (June 2000): 1072–81. http://dx.doi.org/10.1109/9.863593.

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45

Bijulal, D., Jayendran Venkateswaran, and N. Hemachandra. "Service levels, system cost and stability of production–inventory control systems." International Journal of Production Research 49, no. 23 (December 2011): 7085–105. http://dx.doi.org/10.1080/00207543.2010.538744.

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46

Yu, Wen Wei, and Xue Tao Wang. "Plate Production Line Automation Control System Based on Automatic Gauge Control." Applied Mechanics and Materials 214 (November 2012): 674–78. http://dx.doi.org/10.4028/www.scientific.net/amm.214.674.

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The automation control system to plate production line is developed independently. It has been applied for several plants in China and has a good effect. The whole automaton control system is designed as several levels: basic automation system, process automation system and HMI system. The control functions are realized with these levels. With AGC function in plate mill area basic automation system, the plate thickness accuracy can be guaranteed.
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47

Zhang, Jia Liang, Bei Zhi Li, Jian Guo Yang, and Han Yan Chen. "An Intelligent Control System of Candy Production Process." Advanced Materials Research 282-283 (July 2011): 658–61. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.658.

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Candy production processes involve many process variables. Operators often find it difficult to effectively monitor the process data, analyze current states, detect and diagnose process anomalies, and take appropriate actions to control the processes. The objective of this work is to develop an intelligent control system of candy production process to improve final candy quality and to increase production efficiency with good human-machine interfaces. The study is conducted by using virtual instrument, multi-sensor data fusion and fuzzy control technology to online detect and control typical parameters. Experiment results show that this system can reach requirements of processing accuracy and real-time property and is highly cost-efficient and practical.
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48

Jurovata, Dominika, Pavel Vazan, Lukas Hrcka, and Julia Kurnatova. "Input Control in Production System by Simulation Optimization." Applied Mechanics and Materials 693 (December 2014): 117–22. http://dx.doi.org/10.4028/www.scientific.net/amm.693.117.

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The paper deals with the problem of simulation optimization and presents its usage for input control in a production system. The paper gives an overview of typical concepts of input control. Authors propose different approaches. The basic principles of simulation optimization are explained. A general procedure with each step is defined and this procedure is explained by an example. The solution is focused on the minimization of the production costs, which is included in the objective function. Realization of the simulation optimization is based on the combination of three different algorithms: Adaptive Thermostatistical Simulated Annealing, Random Solution and All Combination algorithm. The proposed procedure definitely leads to exact determination of input interval for given production system and short time period.
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49

ICHIKAWA, Fumio. "In-process control system in semiconductor production line." Journal of the Japan Society for Precision Engineering 54, no. 4 (1988): 655–57. http://dx.doi.org/10.2493/jjspe.54.655.

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50

Kaneko, Yutaka. "3-5 Networked Content Production and Control System." Journal of the Institute of Image Information and Television Engineers 60, no. 5 (2006): 697–701. http://dx.doi.org/10.3169/itej.60.697.

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