DOCSLIB.ORG

Multi-Agent-Based Production Planning and Control

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Multi-Agent-Based Production Planning and Control Jie Zhang Shanghai Jiao Tong University China © 2017 National Defense Industry Press Library of Congress Cataloging‐in‐Publication Data Names: Zhang, Jie, 1963 September 21– author. Title: Multi-agent-based production planning and control / Jie Zhang, Shanghai Jiao Tong University, China. Description: First edition. | Hoboken, NJ, USA : John Wiley & Sons, Inc., [2017] | Includes bibliographical references and index. Identifiers: LCCN 2016044467 (print) | LCCN 2017002169 (ebook) | ISBN 9781118890066 (cloth) | ISBN 9781118890080 (pdf) | ISBN 9781118890097 (epub) Subjects: LCSH: Production planning. | Production control. Classification: LCC TS155 .Z4349 2017 (print) | LCC TS155 (ebook) | DDC 658.5–dc23 LC record available at https://lccn.loc.gov/2016044467 Set in 10/12pt Warnock by SPi Global, Pondicherry, India Contents Preface xiii About this book xv 1 Agent Technology in Modern Manufacturing 1 1.1 Introduction 1 1.2 Agent and Multi‐Agent System 1 1.2.1 Agent 2 1.2.2 Multi‐Agent System 4 1.3 Agent Technologies in Manufacturing Systems 7 1.3.1 Contemporary Manufacturing Systems 7 1.3.2 Agents in Production Planning and Control Systems 8 1.3.3 The Existing Requirements 10 1.4 Book Organization 11 1.4.1 Purpose of the Book 11 1.4.2 Scope of the Book 12 1.4.3 Content of the Book 12 References 14 2 The Technical Foundation of a Multi‐Agent System 21 2.1 Introduction 21 2.2 The Structure of an Agent 21 2.2.1 Thinking Agent 23 2.2.2 Reactive Agent 26 2.2.3 Hybrid Agent 28 2.3 The Structure of a Multi‐Agent System 29 2.3.1 The Environment of a Multi‐Agent System 29 2.3.2 The Structure of a Multi‐Agent System 30 2.4 Modeling Methods of a Multi‐Agent System 34 2.4.1 The Behavior Model of a Multi‐Agent System 34 2.4.2 The Running Model of a Multi‐Agent System 35 2.5 The Communication and Interaction Model of a Multi‐Agent System 37 2.6 The Communication Protocol for a Multi‐Agent System 39 2.6.1 Communication Languages for an Agent 40 2.6.2 The Communication Ontology for an Agent 42 2.7 The Interaction Protocol for a Multi‐Agent System 43 2.7.1 Classification of Interaction Protocols 43 2.7.2 Description of Interaction Protocols 45 2.7.3 The Collaboration‐Based Interaction Protocol 47 2.7.4 The Negotiation‐Based Interaction Protocol 48 2.8 Conclusion 50 References 50 3 Multi‐Agent‐Based Production Planning and Control 55 3.1 Introduction 55 3.2 Manufacturing Systems 56 3.2.1 Concept 56 3.2.2 Classification 57 3.3 Production Planning and Control 61 3.3.1 Production Planning and Control Activities 61 3.3.2 Production Planning and Control Mode 64 3.3.3 Production Planning and Control Systems 66 3.3.4 Hybrid Push‐Pull Production Planning and Control System 68 3.4 Multi‐Agent‐Based Push‐Pull Production Planning and Control System (MAP4CS) 71 3.4.1 Mapping Methods 72 3.4.2 Functions of a Hybrid Push‐Pull Production Planning and Control System 73 3.4.3 Structures of a MAP4CS 77 3.4.4 The Running Model of a MAP4CS 80 3.4.5 Behavior Models of a MAP4CS 82 3.4.6 The Interactive Model of a MAP4CS 85 3.5 Conclusion 90 References 91 4 Multi‐Agent‐Based Production Planning for Distributed Manufacturing Systems 95 4.1 Introduction 95 4.2 Production Planning for Distributed Manufacturing Systems 96 4.2.1 Distributed Manufacturing Systems 96 4.2.2 Features of Distributed Manufacturing Systems 99 4.2.3 Production Planning Methods for Distributed Manufacturing Systems 102 4.3 Multi‐Agent‐Based Production Planning in Distributed Manufacturing Systems 106 4.3.1 A Production Planning Model for Distributed Manufacturing Systems 107 4.3.2 Production Planning in MASs 112 4.3.3 The Running Model of a Multi‐Agent‐Based Production Planning System 116 4.4 Agents in Multi‐Agent Production Planning Systems 118 4.4.1 Order Demand Management Agent 118 4.4.2 Cooperative Planning Agent 120 4.4.3 Critical Resource Capacity Management Agent 121 4.5 Contract Net Protocol‐Based Production Planning Optimization Method 123 4.5.1 Contract Net Protocol 123 4.5.2 Contract Net Protocol‐Based Collaborative Production Planning Algorithm 126 4.5.3 Case Study 130 4.6 Bid Auction Protocol‐Based Production Planning Optimization Method 133 4.6.1 Bid Auction Protocol 134 4.6.2 The Bid Auction Protocol‐Based Negotiating Production Planning Algorithm 135 4.6.3 Case Study 138 4.7 Conclusion 139 References 140 5 Multi‐Agent‐Based Production Scheduling for Job Shop Manufacturing Systems 143 5.1 Introduction 143 5.2 Production Scheduling in Job Shop Manufacturing Systems 144 5.2.1 Job Shop Manufacturing Systems 144 5.2.2 Production Scheduling in Job Shop Manufacturing Systems 146 5.2.3 The Related Literature Review 148 5.3 Multi‐Agent Double Feedback–Based Production Scheduling in Job Shop Manufacturing Systems 153 5.3.1 Principles of Double Feedback Scheduling Strategy 153 5.3.2 The Architecture of the Multi‐Agent Double Feedback–Based Production Scheduling System 154 5.3.3 The Running Model for the Multi‐Agent Double Feedback–Based Production Scheduling 155 5.4 Agents in the Multi‐Agent Double Feedback–Based Scheduling System 158 5.4.1 Task Management Agent 159 5.4.2 Collaborative Scheduling Agent 160 5.4.3 Resource Capacity Management Agent 161 5.5 Positive Feedback–Based Production Scheduling in Job Shop Manufacturing Systems 162 5.5.1 Problem Description 163 5.5.2 Multi‐Agent Positive Feedback Scheduling System Based on Contract Net Protocol 167 5.5.3 Positive Feedback Production Scheduling Algorithm Based on the Hierarchical Genetic Algorithm 168 5.5.4 Case Study 174 5.6 Negative Feedback–Based Production Rescheduling in Job Shop Manufacturing Systems 177 5.6.1 Problem Description 177 5.6.2 Multi‐Agent Negative Feedback Rescheduling System Based on Ant Colony Auction Protocol 179 5.6.3 Ant Colony Algorithm–Based Negative Feedback Rescheduling Approach 181 5.6.4 Case Study 188 5.7 Conclusion 188 References 190 6 Multi‐Agent‐Based Production Scheduling in Re‐Entrant Manufacturing Systems 197 6.1 Introduction 197 6.2 Production Scheduling in Re‐Entrant Manufacturing Systems 198 6.2.1 Re‐Entrant Manufacturing Systems 198 6.2.2 Production Scheduling in Re‐Entrant Manufacturing Systems 201 6.2.3 The Related Literature Review 204 6.3 Multi‐Agent‐Based Hierarchical Adaptive Production Scheduling in Re‐Entrant Manufacturing Systems 208 6.3.1 Hierarchical Adaptive Production Scheduling Strategy 208 6.3.2 The Architecture of the Multi‐Agent Hierarchical Adaptive Production Scheduling System 210 6.3.3 The Running Model for a Multi‐Agent Hierarchical Adaptive Production Scheduling System 212 6.4 Agents in a Multi‐Agent Hierarchical Adaptive Production Scheduling System 212 6.4.1 Task Management Agent 214 6.4.2 Collaborative Scheduling Agent 215 6.4.3 Resource Capacity Management Agent 217 6.5 Hierarchical Production Scheduling in Re‐Entrant Manufacturing Systems 218 6.5.1 Problem Description 218 6.5.2 Contact Net Protocol based Production Scheduling in the System Layer 222 6.5.3 GPGP‐CN Protocol Based Production Scheduling in the Machine Layer 226 6.5.4 Case Study 238 6.6 Adaptive Rescheduling in Re‐Entrant Manufacturing Systems 244 6.6.1 Problem Description 244 6.6.2 Rescheduling Strategy 247 6.6.3 FNN‐Based Rescheduling 248 6.6.4 Case Study 253 6.7 Conclusion 253 References 258 7 Multi‐Agent‐Based Production Control 263 7.1 Introduction 263 7.2 Multi‐Agent Production Control System 264 7.2.1 Requirements of Production Control Process 264 7.2.2 The Architecture of a Multi‐Agent Production Control System 265 7.2.3 The Running Model for Multi‐Agent Production Control Systems 268 7.3 Agents in Multi‐Agent Production Control Systems 271 7.3.1 Collaborative Task Management Agent 271 7.3.2 Machine Management Agent 273 7.3.3 Material Management Agent 274 7.3.4 Production Monitoring Agent 275 7.3.5 Warning Management Agent 276 7.3.6 Performance Analysis Agent 277 7.3.7 Quality Management Agent 278 7.3.8 Production Process Tracking and Tracing Agent 280 7.4 Technologies and Methods for Multi‐Agent Production Control Systems 283 7.4.1 XML‐Based Production Monitoring 283 7.4.2 Differential Manchester Encoding Rule‐Based Warning Management 284 7.4.3 Material Identification Technology for Production Process Tracking and Tracing 287 7.5 Conclusion 294 References 295 8 Multi‐Agent‐Based Material Data Acquisition 297 8.1 Introduction 297 8.2 RFID Technology 297 8.2.1 Development of RFID Technologies 297 8.2.2 RFID Technology Standard 301 8.3 Agent‐Based Material Data Acquisition System 306 8.3.1 Requirement Analysis of Material Data Acquisition 306 8.3.2 Multi‐Agent RFID‐Based Material Data Acquisition Structure 307 8.3.3 The Running Model of a Multi‐Agent Material Data Acquisition System 309 8.4 Agents in Multi‐Agent RFID‐Based Material Data Acquisition Systems 312 8.4.1 RFID Middleware Agent 312 8.4.2 RFID Reader Agent 322 8.4.3 RFID Tag Agent 322 8.5 Multi‐Agent RFID‐Based Material Data Acquisition Systems 326 8.5.1 Hardware and Configuration 326 8.5.2 Material Data Process and Publish 327 8.6 Conclusion 329 References 332 9 Multi‐Agent‐Based Equipment Data Acquisition 333 9.1 Introduction 333 9.2 Basics of OPC Technology 334 9.2.1 Development of OPC Technology 334 9.2.2 OPC Technology Overview 335 9.3 Agent‐Based Equipment Data Acquisition System 340 9.3.1 Requirement Analysis of Equipment Data Acquisition 340 9.3.2 The MAS Structure of the OPC‐Based Equipment Data Acquisition 341 9.3.3 The Running Model of the Equipment Data Acquisition MAS 345 9.4 Agents in the Multi‐Agent OPC‐Based Equipment Data Acquisition System 347 9.4.1 OPC Agent 347 9.4.2 OPC Server
Recommended publications
  • Lean Manufacturing in a High Containment Environment

    Lean Manufacturing in a High Containment Environment

    IPT 32 2009 11/3/10 15:48 Page 74 Manufacturing Lean Manufacturing in a High Containment Environment By Georg Bernhard A case study of how Pfizer’s Right First Time strategy helped overcome at Pfizer Global daunting capacity challenges at its new large-scale containment facility Manufacturing at Illertissen, Germany, while Six Sigma and Lean strategies enabled the site to achieve previously unknown levels of efficiency. In late 2006, Pfizer launched its smoking cessation film-coated tablets. All of the process stages (granulation, product varenicline, known as Chantix® in the US tableting and coating) are controlled and monitored from and Champix® across Europe. According to IMS a separate control room so that employees do not come benchmarks, Champix® was the fastest product launch in into contact with dust that might be generated during the the European pharmaceutical industry, with marketing tablet production run. Transport is handled by automated, in 16 European countries in a five-month period. As well and entirely robotic, laser-guided vehicles. Operators as creating rapid and unprecedented consumer demand, monitor and control all containment room activities from the product launch also brought capacity challenges for a separate control room. Pfizer Inc’s small-scale IllertissenCONtainment (ICON) facility in Illertissen, Germany. The facility’s innovative IT systems integration enabled an extraordinary 85 per cent level of automation (LoA), A strategic site in Pfizer’s global manufacturing network, outstripping even the ICON facility’s extremely high 76 per Illertissen is focused on oral solid dosage forms and is a cent LoA. Thus, in NEWCON, all process sequences centre of excellence in containment production.
  • Stackable Lcc/Lcd Oven Instruction Manual

    Stackable Lcc/Lcd Oven Instruction Manual

    C-195 P/N 156452 REVISION W 12/2007 STACKABLE LCC/LCD OVEN INSTRUCTION MANUAL Model Atmosphere Volts Amps Hz Heater Phase Watts LCC/D1-16-3 Air 240 14.8 50/60 3,000 1 LCC/D1-16N-3 Nitrogen 240 14.0 50/60 3,000 1 LCC/D1-51-3 Air 240 27.7 50/60 6,000 1 LCC/D1-51N-3 Nitrogen 240 27.7 50/60 6,000 1 Model numbers may include a “V” for silicone free construction. Model numbers may begin with the designation LL *1-*, indicating Models without HEPA filter. Prepared by: Despatch Industries 8860 207 th St. West Lakeville, MN 55044 Customer Service 800-473-7373 NOTICE Users of this equipment must comply with operating procedures and training of operation personnel as required by the Occupational Safety and Health Act (OSHA) of 1970, Section 6 and relevant safety standards, as well as other safety rules and regulations of state and local governments. Refer to the relevant safety standards in OSHA and National Fire Protection Association (NFPA), section 86 of 1990. CAUTION Setup and maintenance of the equipment should be performed by qualified personnel who are experienced in handling all facets of this type of system. Improper setup and operation of this equipment could cause an explosion that may result in equipment damage, personal injury or possible death. Dear Customer, Thank you for choosing Despatch Industries. We appreciate the opportunity to work with you and to meet your heat processing needs. We believe that you have selected the finest equipment available in the heat processing industry.
  • Automationml 2.0 (CAEX 2.15)

    Automationml 2.0 (CAEX 2.15)

    Application Recommendation Provisioning for MES and ERP – Support for IEC 62264 and B2MML Release Date: November 7, 2018 ID: AR-MES-ERP Version: 1.1.0 Compatibility: AutomationML 2.0 (CAEX 2.15) Provisioning for MES and ERP Author: Bernhard Wally [email protected] Business Informatics Group, CDL-MINT, TU Wien AR-MES-ERP 1.1.0 2 Provisioning for MES and ERP Table of Content Table of Content .............................................................................................................................................. 3 List of Figures .................................................................................................................................................. 6 List of Tables .................................................................................................................................................... 7 List of Code Listings ..................................................................................................................................... 11 Provided Libraries .................................................................................................................. 12 I.1 Role Class Libraries ............................................................................................................................ 12 I.2 Interface Class Libraries ..................................................................................................................... 12 Referenced Libraries .............................................................................................................
  • The Lcc 4.X Code-Generation Interface

    The Lcc 4.X Code-Generation Interface

    The lcc 4.x Code-Generation Interface Christopher W. Fraser and David R. Hanson Microsoft Research [email protected] [email protected] July 2001 Technical Report MSR-TR-2001-64 Abstract Lcc is a widely used compiler for Standard C described in A Retargetable C Compiler: Design and Implementation. This report details the lcc 4.x code- generation interface, which defines the interaction between the target- independent front end and the target-dependent back ends. This interface differs from the interface described in Chap. 5 of A Retargetable C Com- piler. Additional infomation about lcc is available at http://www.cs.princ- eton.edu/software/lcc/. Microsoft Research Microsoft Corporation One Microsoft Way Redmond, WA 98052 http://www.research.microsoft.com/ The lcc 4.x Code-Generation Interface 1. Introduction Lcc is a widely used compiler for Standard C described in A Retargetable C Compiler [1]. Version 4.x is the current release of lcc, and it uses a different code-generation interface than the inter- face described in Chap. 5 of Reference 1. This report details the 4.x interface. Lcc distributions are available at http://www.cs.princeton.edu/software/lcc/ along with installation instruc- tions [2]. The code generation interface specifies the interaction between lcc’s target-independent front end and target-dependent back ends. The interface consists of a few shared data structures, a 33-operator language, which encodes the executable code from a source program in directed acyclic graphs, or dags, and 18 functions, that manipulate or modify dags and other shared data structures. On most targets, implementations of many of these functions are very simple.
  • Implementation of Lean Six Sigma Methodology on a Wine Bottling Line

    Implementation of Lean Six Sigma Methodology on a Wine Bottling Line

    MASTER THESIS The implementation of Lean Six Sigma methodology in the wine sector: an analysis of a wine bottling line in Trentino Master Thesis in Industrial Engineering by Sergio De Gracia Directed by Prof. Roberta Raffaelli Academic Year 2013-2014 Table of Contents Abstract ......................................................................................................................................... 9 Acknowledgements ..................................................................................................................... 10 Introduction ................................................................................................................................ 11 Lean Manufacturing .................................................................................................................... 14 1. Lean Manufacturing ................................................................................................................ 14 1.1. TPS in Lean Manufacturing.......................................................................................... 15 1.2. Types of waste and value added ................................................................................. 16 1.2.1. Value Stream Mapping (VSM) ................................................................................... 18 1.3. Continual Improvement process and KAIZEN ............................................................. 19 1.4. Lean Thinking .............................................................................................................
  • Manufacturing Execution Systems (MES)

    Manufacturing Execution Systems (MES)

    Manufacturing Execution Systems (MES) Industry specific Requirements and Solutions ZVEI - German Electrical and Electronic Manufactures‘ Association Automation Division Lyoner Strasse 9 60528 Frankfurt am Main Germany Phone: + 49 (0)69 6302-292 Fax: + 49 (0)69 6302-319 E-mail: [email protected] www.zvei.org ISBN: 978-3-939265-23-8 CONTENTS Introduction and objectives IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII5 1. Market requirements and reasons for using MES IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII6 2. MES and normative standards (VDI 5600 / IEC 62264) IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII8 3. Classification of the process model according to IEC 62264 IIIIIIIIIIIIIIIIIIIIIIIIII 12 3.1 Resource Management IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII13 3.2 Definition Management IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII14 3.3 Detailed Scheduling IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII14 3.4 Dispatching IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII15 3.5 Execution Management IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII15 3.6 Data Collection IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII16 IMPRINT 3.7 Tracking IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII16 3.8 Analysis IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII17 Manufacturing Execution Systems (MES) 4. Typical MES modules and related terms IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII18
  • JANNE HARJU PLANT INFORMATION MODELS for OPC UA: CASE COPPER REFINERY Master of Science Thesis

    JANNE HARJU PLANT INFORMATION MODELS for OPC UA: CASE COPPER REFINERY Master of Science Thesis

    JANNE HARJU PLANT INFORMATION MODELS FOR OPC UA: CASE COPPER REFINERY Master of Science Thesis Examiner: Hannu Koivisto Examiner and topic approved in the Council meeting of Depart of Auto- mation Science and Engineering on 5.11.2014 i i TIIVISTELMÄ TAMPEREEN TEKNILLINEN YLIOPISTO Automaatiotekniikan koulutusohjelma HARJU, JANNE: PLANT INFORMATION MODELS FOR OPC UA: CASE COPPER REFINERY Diplomityö, 59 sivua Tammikuu 2015 Pääaine: Automaatio- ja informaatioverkot Tarkastaja: Professori Hannu Koivisto Avainsanat: OPC, UA, ISA-95, CAEX, mallinnus, ADI Isojen laitosten informaation rakenne on usein hankala ihmisen hahmottaa silloin, kun kaikki tieto on yhdessä listassa. Jotta laitoksen informaatiosta saadaan parempi koko- naiskuva, tarvitsee se mallintaa jollakin tekniikalla. Kun laitoksen mallintaa hierarkki- sesti, pystyy sen rakenteen hahmottamaan helpommin. Lisäksi, kun malliin saadaan tuotua mittaussignaaleja ja muuta mittauksiin ja muihin arvoihin liittyvää dataa, saadaan se tehokkaaseen käyttöön. Nykyään paljon käytetty klassinen OPC tuo datan käytettäväksi ylemmille lai- toksen tasoille. OPC tuo mittaukset pelkkänä listana, eikä se tuo mittausten välille mi- tään metatietoa. Tässä työssä on tarkoitus tutkia, miten mallintaa esimerkki laitokset käyttäen klassisen OPC:n seuraajaa OPC UA:ta. Työn tavoitteena on tutkia eri OPC UA:n mallinnustyökaluja, menetelmiä ja itse mallinnusta. Työssä tutustutaan OPC UA:n lisäksi ISA-95:n ja sen OPC UA- malliin ja miten tätä mallia pystyy käyttämään oikean laitoksen mallinnuksessa hyödyksi. ISA-95 lisäksi tutustutaan toiseen malliin nimeltä CAEX. CAEX on alun perin suunniteltu suunnitteludatan tallentamiseen stan- dardoidulla tavalla. CAEX:sta on olemassa myös tutkimuksia OPC UA:n kanssa käytet- täväksi ja tässä työssä tutustutaan, onko mallinnettavien tehtaiden kanssa mahdollista käyttää CAEX:ia hyödyksi. Virallista OPC UA CAEX- mallia ei ole vielä tosin julkais- tu.
  • Linux Assembly HOWTO Linux Assembly HOWTO

    Linux Assembly HOWTO Linux Assembly HOWTO

    Linux Assembly HOWTO Linux Assembly HOWTO Table of Contents Linux Assembly HOWTO..................................................................................................................................1 Konstantin Boldyshev and François−René Rideau................................................................................1 1.INTRODUCTION................................................................................................................................1 2.DO YOU NEED ASSEMBLY?...........................................................................................................1 3.ASSEMBLERS.....................................................................................................................................1 4.METAPROGRAMMING/MACROPROCESSING............................................................................2 5.CALLING CONVENTIONS................................................................................................................2 6.QUICK START....................................................................................................................................2 7.RESOURCES.......................................................................................................................................2 1. INTRODUCTION...............................................................................................................................2 1.1 Legal Blurb........................................................................................................................................2
  • Intro to C Programming 1

    Intro to C Programming 1

    Introduction to C via Examples Last update: Cristinel Ababei, January 2012 1. Objective The objective of this material is to introduce you to several programs in C and discuss how to compile, link, and execute on Windows or Linux. The first example is the simplest hello world example. The second example is designed to expose you to as many C concepts as possible within the simplest program. It assumes no prior knowledge of C. However, you must allocate significant time to read suggested materials, but especially work on as many examples as possible. 2. Working on Windows: Example 1 To get started with learning C, I recommend using a simple and general/generic IDE with a freeC/C++ compiler. For example, Dev-C++ is a nice compiler system for windows. Download and install it on your own computer from here: http://www.bloodshed.net/devcpp.html In my case, I installed it in M:\Dev-Cpp, where I created a new directory M:\Dev-Cpp\cristinel to store all projects I will work on. Inside this new directory, I created yet one more M:\Dev- Cpp\cristinel\project1, where I’ll store the files for the first program, which is simply the famous “hello world” program. We’ll create a new project directory for each new program; this way we keep thing nicely organized in individual directories. We create a program by creating a new project in Dev-C++. So, start Dev-C++ and create a new project. In the dialog window, select “Console Application” as the type of project and name it hello.
  • What's New in Omnis Studio 8.1.7

    What's New in Omnis Studio 8.1.7

    What’s New in Omnis Studio 8.1.7 Omnis Software April 2019 48-042019-01a The software this document describes is furnished under a license agreement. The software may be used or copied only in accordance with the terms of the agreement. Names of persons, corporations, or products used in the tutorials and examples of this manual are fictitious. No part of this publication may be reproduced, transmitted, stored in a retrieval system or translated into any language in any form by any means without the written permission of Omnis Software. © Omnis Software, and its licensors 2019. All rights reserved. Portions © Copyright Microsoft Corporation. Regular expressions Copyright (c) 1986,1993,1995 University of Toronto. © 1999-2019 The Apache Software Foundation. All rights reserved. This product includes software developed by the Apache Software Foundation (http://www.apache.org/). Specifically, this product uses Json-smart published under Apache License 2.0 (http://www.apache.org/licenses/LICENSE-2.0) The iOS application wrapper uses UICKeyChainStore created by http://kishikawakatsumi.com and governed by the MIT license. Omnis® and Omnis Studio® are registered trademarks of Omnis Software. Microsoft, MS, MS-DOS, Visual Basic, Windows, Windows Vista, Windows Mobile, Win32, Win32s are registered trademarks, and Windows NT, Visual C++ are trademarks of Microsoft Corporation in the US and other countries. Apple, the Apple logo, Mac OS, Macintosh, iPhone, and iPod touch are registered trademarks and iPad is a trademark of Apple, Inc. IBM, DB2, and INFORMIX are registered trademarks of International Business Machines Corporation. ICU is Copyright © 1995-2003 International Business Machines Corporation and others.
  • Total Quality Management Course Code: POM-324 Author: Dr

    Total Quality Management Course Code: POM-324 Author: Dr

    Subject: Total Quality Management Course Code: POM-324 Author: Dr. Vijender Pal Saini Lesson No.: 1 Vetter: Dr. Sanjay Tiwari Concepts of Quality, Total Quality and Total Quality Management Structure 1.0 Objectives 1.1 Introduction 1.2 Concept of Quality 1.3 Dimensions of Quality 1.4 Application / Usage of Quality for General Public / Consumers 1.5 Application of Quality for Producers or Manufacturers 1.6 Factors affecting Quality 1.7 Quality Management 1.8 Total Quality Management 1.9 Characteristics / Nature of TQM 1.10 The TQM Practices Followed by Multinational Companies 1.11 Summary 1.12 Keywords 1.13 Self Assessment Questions 1.14 References / Suggested Readings 1 1.0 Objectives After going through this lesson, you will be able to: Understand the concept of Quality in day-to-day life and business. Differentiate between Quality and Quality Management Elaborate the concept of Total Quality Management 1.1 Introduction Quality is a buzz word in our lives. When the customer is in market, he or she is knowingly or unknowingly very cautious about the quality of product or service. Imagine the last buying of any product or service, e.g., mobile purchased last time. You must have enquired about various features like RAM, Operating System, Processor, Size, Body Colour, Cover, etc. If any of the features is not available, you might have suddenly changed the brand or have decided not to purchase it. Remember, how our mothers buy fruits, vegetables or grocery items. They are buying fresh and look-wise firm fruits, vegetable and groceries. Simultaneously, they are very conscious about the price of the fruits, vegetable and groceries.
  • Kpis As the Interface Between Scheduling and Control

    Kpis As the Interface Between Scheduling and Control

    Preprint, 11th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems June 6-8, 2016. NTNU, Trondheim, Norway KPIs as the interface between scheduling and control Margret Bauer1, Matthieu Lucke2,3, Charlotta Johnsson3, Iiro Harjunkoski2, Jan C. Schlake2 1University of the Witwatersrand, Johannesburg 2050, South Africa (email: [email protected]) 2ABB Corporate Research, Wallstadter Str. 59, 68526 Ladenburg, Germany (email:[email protected]) 3Department of Automatic Control, Lund University, 22100 Lund, Sweden (email [email protected]) Abstract: The integration of scheduling and control has been discussed in the past. While constructing an integrated plant model that may still seem out of reach, scheduling and control systems are increasingly more intertwined. We argue that they are in fact already integrated and give the example of two key performance indicators (KPIs) that are defined in the recent international standard ISO 22400. The focus of this study is on KPIs that consider both planned times and actual times. An amino acid production plant is used in the study, and the production is described from both the scheduling and the control perspective. To illustrate the integration, a schedule is computed containing the planned production times. Resulting measurements from the control system are analyzed for their actual production times using a proposed procedure that detects the start and end time of batches. Using KPIs as the interface between scheduling and control can be used as a strategy for maximizing the plant performance. The study focuses on the process industry. Keywords: Integration between scheduling and control, key performance indicator, distributed control system, planning and scheduling, batch process.