THROUGHPUT OPTIMIZATION IN ROBOTIC CELLS
|
|
|
- Amie Young
- 10 years ago
- Views:
Transcription
1 Contents Preface xv 1. ROBOTIC CELLS IN PRACTICE Cellular Manufacturing Robotic Cell Flowshops Throughput Optimization Historical Overview Applications A CLASSIFICATION SCHEME FOR ROBOTIC CELLS AND NOTATION Machine Environment Number of Machines Number of Robots Types of Robots Cell Layout Processing Characteristics Pickup Criterion Travel-Time Metric Number of Part-Types Objective Function An α β γ Classification for Robotic Cells Cell Data Processing Times Loading and Unloading Times Notations for Cell States and Robot Actions CYCLIC PRODUCTION Operating Policies and Dominance of Cyclic Solutions 29 ix
2 x THROUGHPUT OPTIMIZATION IN ROBOTIC CELLS 3.2 Cycle Times Waiting Times Computation of Cycle Times Lower Bounds on Cycle Times Optimal 1-Unit Cycles Special Cases General Cases: Constant Travel-Time Cells Optimization over Basic Cycles General Cases: Time Cells Additive and Euclidean Travel Calculation of Makespan of a Lot A Graphical Approach Algebraic Approaches Quality of 1-Unit Cycles and Approximation Results Additive Travel-Time Cells Pyramidal Cycles A 1.5-Approximation Algorithm A 10/7-Approximation for Additive Cells Constant Travel-Time Cells A 1.5-Approximation Algorithm Euclidean Travel-Time Cells DUAL-GRIPPER ROBOTS Additional Notation Cells with Two Machines A Cyclic Sequence for m-machine Dual-Gripper Cells Dual-Gripper Cells with Small Gripper Switch Times Comparing Dual-Gripper and Single-Gripper Cells Comparison of Productivity: Computational Results Efficiently Solvable Cases Single-Gripper Cells with Output Buffers at Machines Dual-Gripper Robotic Cells: Constant Travel Time Lower Bounds and Optimal Cycles: m-machine Simple Robotic Cells One-Unit Cycles Multi-Unit Cycles PARALLEL MACHINES Single-Gripper Robots Definitions k-unit Cycles and Blocked Cycles 156
3 Contents xi Structural Results for k-unit Cycles Blocked Cycles LCM Cycles Practical Implications Optimal Cycle for a Common Case Fewest Machines Required to Meet Timelines Dual-Gripper Robots Lower Bound on Per Unit Cycle Time An Optimal Cycle Improvement from Using a Dual-Gripper Robot or Parallel Machines Installing a Dual-Gripper Robot in a Simple Robotic Cell Installing Parallel Machines in a Single-Gripper Robot Cell Installing a Dual-Gripper Robot in a Single-Gripper Robotic Cell with Parallel Machines An Illustration on Data from Implemented Cells MULTIPLE-PART-TYPE PRODUCTION: SINGLE-GRIPPER ROBOTS MPS Cycles and CRM Sequences Scheduling Multiple Part-Types in Two-Machine Cells Scheduling Multiple Part-Types in Three-Machine Cells Cycle Time Derivations Efficiently Solvable Special Cases Steady-State Analyses Reaching Steady State for the Sequence CRM(π 2 ) Reaching Steady State for the Sequence CRM(π 6 ) A Practical Guide to Initializing Robotic Cells Intractable Cycles for Three-Machine Cells MPS Cycles with the Sequence CRM(π 2 ) MPS Cycles with the Sequence CRM(π 6 ) Complexity of Three-Machine Robotic Cells Scheduling Multiple Part-Types in Large Cells Class U: Schedule Independent Problems Class V 1: Special Cases of the TSP Class V 2: NP-Hard TSP Problems Class W : NP-Hard Non-TSP Problems Overview Heuristics for Three-Machine Problems A Heuristic Under the Sequence CRM(π 2 ) 270
4 xii THROUGHPUT OPTIMIZATION IN ROBOTIC CELLS A Heuristic Under the Sequence CRM(π 6 ) Computational Testing Heuristics for General Three-Machine Problems Heuristics for Large Cells The Cell Design Problem Forming Cells Buffer Design An Example Computational Testing MULTIPLE-PART-TYPE PRODUCTION: DUAL-GRIPPER ROBOTS Two-Machine Cells: Undominated CRM Sequences Two-Machine Cells: Complexity Cycle Time Calculation Strong NP-Completeness Results Polynomially Solvable Problems Analyzing Two-Machine Cells with Small Gripper Switch Times A Heuristic for Specific CRM Sequences A Performance Bound for Heuristic Hard-CRM A Heuristic for Two-Machine Cells Comparison of Productivity: Single-Gripper Vs. Dual- Gripper Cells An Extension to m-machine Robotic Cells MULTIPLE-ROBOT CELLS Physical Description of a Multiple-Robot Cell Cycles in Multiple-Robot Cells Cycle Times Scheduling by a Heuristic Dispatching Rule Computational Results Applying an LCM Cycle to Implemented Cells NO-WAIT AND INTERVAL ROBOTIC CELLS No-Wait Robotic Cells Interval Pick-up Robotic Cells OPEN PROBLEMS Simple Robotic Cells Simple Robotic Cells with Multiple Part Types 376
5 Contents xiii 10.3 Robotic Cells with Parallel Machines Stochastic Data Dual-Gripper Robots Flexible Robotic Cells Implementation Issues Using Local Material Handling Devices Revisiting Machines 379 Appendices Appendix A 383 A.1 1-Unit Cycles 383 A Unit Cycles in Classical Notation 384 A Unit Cycles in Activity Notation 385 Appendix B 387 B.1 The Gilmore-Gomory Algorithm for the TSP 387 B.1.1 The Two-Machine No-Wait Flowshop Problem 387 B.1.2 Formulating a TSP 388 B.1.3 The Gilmore-Gomory Algorithm 389 B.2 The Three-Machine No-Wait Flowshop Problem as a TSP 394 Copyright Permissions 409 Index 413
6
vii TABLE OF CONTENTS CHAPTER TITLE PAGE DECLARATION DEDICATION ACKNOWLEDGEMENT ABSTRACT ABSTRAK
vii TABLE OF CONTENTS CHAPTER TITLE PAGE DECLARATION DEDICATION ACKNOWLEDGEMENT ABSTRACT ABSTRAK TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS LIST OF SYMBOLS LIST OF APPENDICES
Approximability of Two-Machine No-Wait Flowshop Scheduling with Availability Constraints
Approximability of Two-Machine No-Wait Flowshop Scheduling with Availability Constraints T.C. Edwin Cheng 1, and Zhaohui Liu 1,2 1 Department of Management, The Hong Kong Polytechnic University Kowloon,
Classification - Examples
Lecture 2 Scheduling 1 Classification - Examples 1 r j C max given: n jobs with processing times p 1,...,p n and release dates r 1,...,r n jobs have to be scheduled without preemption on one machine taking
! Solve problem to optimality. ! Solve problem in poly-time. ! Solve arbitrary instances of the problem. #-approximation algorithm.
Approximation Algorithms 11 Approximation Algorithms Q Suppose I need to solve an NP-hard problem What should I do? A Theory says you're unlikely to find a poly-time algorithm Must sacrifice one of three
Planning and Scheduling in Manufacturing and Services
Michael L. Pinedo Planning and Scheduling in Manufacturing and Services Second edition 4y Springer Preface Contents of CD-ROM vii xvii Part I Preliminaries 1 Introduction 3 1.1 Planning and Scheduling:
! Solve problem to optimality. ! Solve problem in poly-time. ! Solve arbitrary instances of the problem. !-approximation algorithm.
Approximation Algorithms Chapter Approximation Algorithms Q Suppose I need to solve an NP-hard problem What should I do? A Theory says you're unlikely to find a poly-time algorithm Must sacrifice one of
Factors to Describe Job Shop Scheduling Problem
Job Shop Scheduling Job Shop A work location in which a number of general purpose work stations exist and are used to perform a variety of jobs Example: Car repair each operator (mechanic) evaluates plus
Manufacturing Planning and Control for Supp Chain Management
Manufacturing Planning and Control for Supp Chain Management Sixth Edition F. Robert Jacobs Indiana University William L. Berry The Ohio State University (Emeritus) D. Clay Whybark University of North
JUST-IN-TIME SCHEDULING WITH PERIODIC TIME SLOTS. Received December May 12, 2003; revised February 5, 2004
Scientiae Mathematicae Japonicae Online, Vol. 10, (2004), 431 437 431 JUST-IN-TIME SCHEDULING WITH PERIODIC TIME SLOTS Ondřej Čepeka and Shao Chin Sung b Received December May 12, 2003; revised February
Manufacturing Planning and Control for Supply Chain Management
Manufacturing Planning and Control for Supply Chain Management APICS/CPIM Certification Edition F. Robert Jacobs Indiana University William L. Berry The Ohio State University (Emeritus) D.ClayWhybark University
A review of lot streaming in a flow shop environment with makespan criteria
6th International Conference on Industrial Engineering and Industrial Management. XVI Congreso de Ingeniería de Organización. Vigo, July 18-20, 2012 A review of lot streaming in a flow shop environment
Integer Programming Approach to Printed Circuit Board Assembly Time Optimization
Integer Programming Approach to Printed Circuit Board Assembly Time Optimization Ratnesh Kumar Haomin Li Department of Electrical Engineering University of Kentucky Lexington, KY 40506-0046 Abstract A
Chapter 11. 11.1 Load Balancing. Approximation Algorithms. Load Balancing. Load Balancing on 2 Machines. Load Balancing: Greedy Scheduling
Approximation Algorithms Chapter Approximation Algorithms Q. Suppose I need to solve an NP-hard problem. What should I do? A. Theory says you're unlikely to find a poly-time algorithm. Must sacrifice one
Objective Criteria of Job Scheduling Problems. Uwe Schwiegelshohn, Robotics Research Lab, TU Dortmund University
Objective Criteria of Job Scheduling Problems Uwe Schwiegelshohn, Robotics Research Lab, TU Dortmund University 1 Jobs and Users in Job Scheduling Problems Independent users No or unknown precedence constraints
Software Performance and Scalability
Software Performance and Scalability A Quantitative Approach Henry H. Liu ^ IEEE )computer society WILEY A JOHN WILEY & SONS, INC., PUBLICATION Contents PREFACE ACKNOWLEDGMENTS xv xxi Introduction 1 Performance
TABLE OF CONTENTS CHAPTER NO. TITLE PAGE NO. ABSTRACT iii LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS
ix TABLE OF CONTENTS CHAPTER NO. TITLE PAGE NO. ABSTRACT iii LIST OF TABLES x LIST OF FIGURES xii LIST OF ABBREVIATIONS xiv 1 INTRODUCTION 1 1.1 ENTERPRISE RESOURCE PLANNING (ERP) AN OVERVIEW 1 1.2 AIM
Cloud Computing. and Scheduling. Data-Intensive Computing. Frederic Magoules, Jie Pan, and Fei Teng SILKQH. CRC Press. Taylor & Francis Group
Cloud Computing Data-Intensive Computing and Scheduling Frederic Magoules, Jie Pan, and Fei Teng SILKQH CRC Press Taylor & Francis Group Boca Raton London New York CRC Press is an imprint of the Taylor
R u t c o r Research R e p o r t. A Method to Schedule Both Transportation and Production at the Same Time in a Special FMS.
R u t c o r Research R e p o r t A Method to Schedule Both Transportation and Production at the Same Time in a Special FMS Navid Hashemian a Béla Vizvári b RRR 3-2011, February 21, 2011 RUTCOR Rutgers
11. APPROXIMATION ALGORITHMS
11. APPROXIMATION ALGORITHMS load balancing center selection pricing method: vertex cover LP rounding: vertex cover generalized load balancing knapsack problem Lecture slides by Kevin Wayne Copyright 2005
MINIMIZING THE TOTAL COMPLETION TIME IN A TWO STAGE FLOW SHOP WITH A SINGLE SETUP SERVER
MINIMIZING THE TOTAL COMPLETION TIME IN A TWO STAGE FLOW SHOP WITH A SINGLE SETUP SERVER A THESİS SUBMITTED TO THE DEPARTMENT OF INDUSTRIAL ENGINEERING AND THE GRADUATE SCHOOL OF ENGINEERING AND SCIENCE
Assembly line balancing to minimize balancing loss and system loss. D. Roy 1 ; D. Khan 2
J. Ind. Eng. Int., 6 (11), 1-, Spring 2010 ISSN: 173-702 IAU, South Tehran Branch Assembly line balancing to minimize balancing loss and system loss D. Roy 1 ; D. han 2 1 Professor, Dep. of Business Administration,
HYBRID GENETIC ALGORITHMS FOR SCHEDULING ADVERTISEMENTS ON A WEB PAGE
HYBRID GENETIC ALGORITHMS FOR SCHEDULING ADVERTISEMENTS ON A WEB PAGE Subodha Kumar University of Washington [email protected] Varghese S. Jacob University of Texas at Dallas [email protected]
NP-complete? NP-hard? Some Foundations of Complexity. Prof. Sven Hartmann Clausthal University of Technology Department of Informatics
NP-complete? NP-hard? Some Foundations of Complexity Prof. Sven Hartmann Clausthal University of Technology Department of Informatics Tractability of Problems Some problems are undecidable: no computer
TABLE OF CONTENT CHAPTER TITLE PAGE TITLE DECLARATION DEDICATION ACKNOWLEDGEMENTS ABSTRACT ABSTRAK
TABLE OF CONTENT CHAPTER TITLE PAGE TITLE DECLARATION DEDICATION ACKNOWLEDGEMENTS ABSTRACT ABSTRAK TABLE OF CONTENT LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS LIST OF APPENDICES i ii iii iv v
Dong-Ping Song. Optimal Control and Optimization. of Stochastic. Supply Chain Systems. 4^ Springer
Dong-Ping Song Optimal Control and Optimization Supply Chain Systems of Stochastic 4^ Springer Contents 1 Stochastic Supply Chain Systems 1 1.1 Introduction 1 1.2 Uncertainties'in Supply Chain Systems
Complexity Theory. IE 661: Scheduling Theory Fall 2003 Satyaki Ghosh Dastidar
Complexity Theory IE 661: Scheduling Theory Fall 2003 Satyaki Ghosh Dastidar Outline Goals Computation of Problems Concepts and Definitions Complexity Classes and Problems Polynomial Time Reductions Examples
MIP-Based Approaches for Solving Scheduling Problems with Batch Processing Machines
The Eighth International Symposium on Operations Research and Its Applications (ISORA 09) Zhangjiajie, China, September 20 22, 2009 Copyright 2009 ORSC & APORC, pp. 132 139 MIP-Based Approaches for Solving
Network Security A Decision and Game-Theoretic Approach
Network Security A Decision and Game-Theoretic Approach Tansu Alpcan Deutsche Telekom Laboratories, Technical University of Berlin, Germany and Tamer Ba ar University of Illinois at Urbana-Champaign, USA
CMPSCI611: Approximating MAX-CUT Lecture 20
CMPSCI611: Approximating MAX-CUT Lecture 20 For the next two lectures we ll be seeing examples of approximation algorithms for interesting NP-hard problems. Today we consider MAX-CUT, which we proved to
Applied Algorithm Design Lecture 5
Applied Algorithm Design Lecture 5 Pietro Michiardi Eurecom Pietro Michiardi (Eurecom) Applied Algorithm Design Lecture 5 1 / 86 Approximation Algorithms Pietro Michiardi (Eurecom) Applied Algorithm Design
Contents. 1 Introduction. 2 Feature List. 3 Feature Interaction Matrix. 4 Feature Interactions
1 Introduction 1.1 Purpose and Scope................................. 1 1 1.2 Organization..................................... 1 2 1.3 Requirements Notation............................... 1 2 1.4 Requirements
Offline sorting buffers on Line
Offline sorting buffers on Line Rohit Khandekar 1 and Vinayaka Pandit 2 1 University of Waterloo, ON, Canada. email: [email protected] 2 IBM India Research Lab, New Delhi. email: [email protected]
1 st year / 2014-2015/ Principles of Industrial Eng. Chapter -3 -/ Dr. May G. Kassir. Chapter Three
Chapter Three Scheduling, Sequencing and Dispatching 3-1- SCHEDULING Scheduling can be defined as prescribing of when and where each operation necessary to manufacture the product is to be performed. It
LIST OF FIGURES. Figure No. Caption Page No.
LIST OF FIGURES Figure No. Caption Page No. Figure 1.1 A Cellular Network.. 2 Figure 1.2 A Mobile Ad hoc Network... 2 Figure 1.3 Classifications of Threats. 10 Figure 1.4 Classification of Different QoS
A Comparison of Oracle Performance on Physical and VMware Servers
A Comparison of Oracle Performance on Physical and VMware Servers By Confio Software Confio Software 4772 Walnut Street, Suite 100 Boulder, CO 80301 303-938-8282 www.confio.com Comparison of Physical and
THE CERTIFIED SIX SIGMA BLACK BELT HANDBOOK
THE CERTIFIED SIX SIGMA BLACK BELT HANDBOOK SECOND EDITION T. M. Kubiak Donald W. Benbow ASQ Quality Press Milwaukee, Wisconsin Table of Contents list of Figures and Tables Preface to the Second Edition
GPU for Scientific Computing. -Ali Saleh
1 GPU for Scientific Computing -Ali Saleh Contents Introduction What is GPU GPU for Scientific Computing K-Means Clustering K-nearest Neighbours When to use GPU and when not Commercial Programming GPU
SIMS 255 Foundations of Software Design. Complexity and NP-completeness
SIMS 255 Foundations of Software Design Complexity and NP-completeness Matt Welsh November 29, 2001 [email protected] 1 Outline Complexity of algorithms Space and time complexity ``Big O'' notation Complexity
Programming Using Python
Introduction to Computation and Programming Using Python Revised and Expanded Edition John V. Guttag The MIT Press Cambridge, Massachusetts London, England CONTENTS PREFACE xiii ACKNOWLEDGMENTS xv 1 GETTING
The Classes P and NP. [email protected]
Intractable Problems The Classes P and NP Mohamed M. El Wakil [email protected] 1 Agenda 1. What is a problem? 2. Decidable or not? 3. The P class 4. The NP Class 5. TheNP Complete class 2 What is a
NP-Completeness and Cook s Theorem
NP-Completeness and Cook s Theorem Lecture notes for COM3412 Logic and Computation 15th January 2002 1 NP decision problems The decision problem D L for a formal language L Σ is the computational task:
QUANTITATIVE METHODS. for Decision Makers. Mik Wisniewski. Fifth Edition. FT Prentice Hall
Fifth Edition QUANTITATIVE METHODS for Decision Makers Mik Wisniewski Senior Research Fellow, Department of Management Science, University of Strathclyde Business School FT Prentice Hall FINANCIAL TIMES
Computer Algorithms. NP-Complete Problems. CISC 4080 Yanjun Li
Computer Algorithms NP-Complete Problems NP-completeness The quest for efficient algorithms is about finding clever ways to bypass the process of exhaustive search, using clues from the input in order
Curriculum Vitae. B.M.T. Lin, NCTU, TW
Curriculum Vitae Bertrand Miao-Tsong Lin ( 林 妙 聰 ) Gender: Male Marital status: Married (1 son and 1 daughter) Date of Birth: May 4, 1964 Nationality: Taiwan, ROC Affiliation: Institute of Information
SERVICE MANAGEMENT AN INTEGRATED APPROACH TO SUPPLY CHAIN MANAGEMENT AND OPERATIONS. Cengiz Haksever Barry Render
SERVICE MANAGEMENT AN INTEGRATED APPROACH TO SUPPLY CHAIN MANAGEMENT AND OPERATIONS Cengiz Haksever Barry Render Preface CONTENTS xxi Part I: Understanding Services 1 THE IMPORTANT ROLE SERVICES PLAY IN
Tutorial 8. NP-Complete Problems
Tutorial 8 NP-Complete Problems Decision Problem Statement of a decision problem Part 1: instance description defining the input Part 2: question stating the actual yesor-no question A decision problem
Scheduling Shop Scheduling. Tim Nieberg
Scheduling Shop Scheduling Tim Nieberg Shop models: General Introduction Remark: Consider non preemptive problems with regular objectives Notation Shop Problems: m machines, n jobs 1,..., n operations
Introduction to Learning & Decision Trees
Artificial Intelligence: Representation and Problem Solving 5-38 April 0, 2007 Introduction to Learning & Decision Trees Learning and Decision Trees to learning What is learning? - more than just memorizing
Scheduling Single Machine Scheduling. Tim Nieberg
Scheduling Single Machine Scheduling Tim Nieberg Single machine models Observation: for non-preemptive problems and regular objectives, a sequence in which the jobs are processed is sufficient to describe
Business Architecture
Business Architecture A Practical Guide JONATHAN WHELAN and GRAHAM MEADEN GOWER Contents List of Figures List of Tables About the Authors Foreword Preface Acknowledgemen ts Abbreviations IX xi xiii xv
Revenue Management and Survival Analysis in the Automobile Industry
Andre Jerenz Revenue Management and Survival Analysis in the Automobile Industry With a foreword by Prof. Dr. Ulrich Tushaus GABLER EDITION WISSENSCHAFT List of Figures List of Tables Nomenclature xiii
COPYRIGHTED MATERIAL. Contents. List of Figures. Acknowledgments
Contents List of Figures Foreword Preface xxv xxiii xv Acknowledgments xxix Chapter 1 Fraud: Detection, Prevention, and Analytics! 1 Introduction 2 Fraud! 2 Fraud Detection and Prevention 10 Big Data for
Hidden Markov Models
8.47 Introduction to omputational Molecular Biology Lecture 7: November 4, 2004 Scribe: Han-Pang hiu Lecturer: Ross Lippert Editor: Russ ox Hidden Markov Models The G island phenomenon The nucleotide frequencies
Load Balancing and Rebalancing on Web Based Environment. Yu Zhang
Load Balancing and Rebalancing on Web Based Environment Yu Zhang This report is submitted as partial fulfilment of the requirements for the Honours Programme of the School of Computer Science and Software
Efficient and Robust Allocation Algorithms in Clouds under Memory Constraints
Efficient and Robust Allocation Algorithms in Clouds under Memory Constraints Olivier Beaumont,, Paul Renaud-Goud Inria & University of Bordeaux Bordeaux, France 9th Scheduling for Large Scale Systems
W4118 Operating Systems. Instructor: Junfeng Yang
W4118 Operating Systems Instructor: Junfeng Yang Outline Introduction to scheduling Scheduling algorithms 1 Direction within course Until now: interrupts, processes, threads, synchronization Mostly mechanisms
CPSC 211 Data Structures & Implementations (c) Texas A&M University [ 313]
CPSC 211 Data Structures & Implementations (c) Texas A&M University [ 313] File Structures A file is a collection of data stored on mass storage (e.g., disk or tape) Why on mass storage? too big to fit
Industrial Optimization
Industrial Optimization Lessons learned from Optimization in Practice Marco Lübbecke Chair of Operations Research RWTH Aachen University, Germany SICS Stockholm Feb 11, 2013 Discrete Optimization: Some
Optimal Scheduling for Dependent Details Processing Using MS Excel Solver
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 8, No 2 Sofia 2008 Optimal Scheduling for Dependent Details Processing Using MS Excel Solver Daniela Borissova Institute of
How To Understand Multivariate Models
Neil H. Timm Applied Multivariate Analysis With 42 Figures Springer Contents Preface Acknowledgments List of Tables List of Figures vii ix xix xxiii 1 Introduction 1 1.1 Overview 1 1.2 Multivariate Models
Design of Enterprise Systems
Design of Enterprise Systems Theory, Architecture, and Methods Ronald E. Giachetti CRC Press Taylor &. Francis Group Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Group, an
Contents. Introduction and System Engineering 1. Introduction 2. Software Process and Methodology 16. System Engineering 53
Preface xvi Part I Introduction and System Engineering 1 Chapter 1 Introduction 2 1.1 What Is Software Engineering? 2 1.2 Why Software Engineering? 3 1.3 Software Life-Cycle Activities 4 1.3.1 Software
Bandwidth management for WDM EPONs
Vol. 5, No. 9 / September 2006 / JOURNAL OF OPTICAL NETWORKING 637 Bandwidth management for WDM EPONs Michael P. McGarry and Martin Reisslein Department of Electrical Engineering, Arizona State University,
Switching and Finite Automata Theory
Switching and Finite Automata Theory Understand the structure, behavior, and limitations of logic machines with this thoroughly updated third edition. New topics include: CMOS gates logic synthesis logic
life science data mining
life science data mining - '.)'-. < } ti» (>.:>,u» c ~'editors Stephen Wong Harvard Medical School, USA Chung-Sheng Li /BM Thomas J Watson Research Center World Scientific NEW JERSEY LONDON SINGAPORE.
Chapter 8. Operations Scheduling
Chapter 8 Operations Scheduling Buffer Soldering Visual Inspection Special Stations Buffer workforce Production Management 161 Scheduling is the process of organizing, choosing and timing resource usage
A Multi-Objective Performance Evaluation in Grid Task Scheduling using Evolutionary Algorithms
A Multi-Objective Performance Evaluation in Grid Task Scheduling using Evolutionary Algorithms MIGUEL CAMELO, YEZID DONOSO, HAROLD CASTRO Systems and Computer Engineering Department Universidad de los
Single machine models: Maximum Lateness -12- Approximation ratio for EDD for problem 1 r j,d j < 0 L max. structure of a schedule Q...
Lecture 4 Scheduling 1 Single machine models: Maximum Lateness -12- Approximation ratio for EDD for problem 1 r j,d j < 0 L max structure of a schedule 0 Q 1100 11 00 11 000 111 0 0 1 1 00 11 00 11 00
Research Paper Business Analytics. Applications for the Vehicle Routing Problem. Jelmer Blok
Research Paper Business Analytics Applications for the Vehicle Routing Problem Jelmer Blok Applications for the Vehicle Routing Problem Jelmer Blok Research Paper Vrije Universiteit Amsterdam Faculteit
High-Mix Low-Volume Flow Shop Manufacturing System Scheduling
Proceedings of the 14th IAC Symposium on Information Control Problems in Manufacturing, May 23-25, 2012 High-Mix Low-Volume low Shop Manufacturing System Scheduling Juraj Svancara, Zdenka Kralova Institute
MINIMUM FLOW TIME SCHEDULE GENETIC ALGORITHM FOR MASS CUSTOMIZATION MANUFACTURING USING MINICELLS
University of Kentucky UKnowledge University of Kentucky Master's Theses Graduate School 2006 MINIMUM FLOW TIME SCHEDULE GENETIC ALGORITHM FOR MASS CUSTOMIZATION MANUFACTURING USING MINICELLS Phanindra
Production and Operations. Management Systems
Production and Operations Management Systems Sushil Gupta and Martin Starr CRC Press Taylor & Francis Croup Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Group, an informa
Quantum and Non-deterministic computers facing NP-completeness
Quantum and Non-deterministic computers facing NP-completeness Thibaut University of Vienna Dept. of Business Administration Austria Vienna January 29th, 2013 Some pictures come from Wikipedia Introduction
Contents. List of Figures. List of Tables. Acknowledgments PART I INTRODUCTION 1
List of Figures List of Tables Acknowledgments Preface xv xix xxi xxiii PART I INTRODUCTION 1 1 The Evolution of Financial Analysis 3 1.1 Bookkeeping 3 1.2 Modern finance 8 1.3 Departments, silos and analysis
The Trip Scheduling Problem
The Trip Scheduling Problem Claudia Archetti Department of Quantitative Methods, University of Brescia Contrada Santa Chiara 50, 25122 Brescia, Italy Martin Savelsbergh School of Industrial and Systems
Vehicle Routing and Scheduling. Martin Savelsbergh The Logistics Institute Georgia Institute of Technology
Vehicle Routing and Scheduling Martin Savelsbergh The Logistics Institute Georgia Institute of Technology Vehicle Routing and Scheduling Part I: Basic Models and Algorithms Introduction Freight routing
CNC Handbook. Helmut A. Roschiwal. Hans B. Kief. Translated by Jefferson B. Hood. Mc Graw Hill. Singapore Sydney Toronto
Hans B. Kief Helmut A. Roschiwal CNC Handbook Translated by Jefferson B. Hood Mc Graw Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney
2014-2015 The Master s Degree with Thesis Course Descriptions in Industrial Engineering
2014-2015 The Master s Degree with Thesis Course Descriptions in Industrial Engineering Compulsory Courses IENG540 Optimization Models and Algorithms In the course important deterministic optimization
Heuristic Algorithms for Open Shop Scheduling to Minimize Mean Flow Time, Part I: Constructive Algorithms
Heuristic Algorithms for Open Shop Scheduling to Minimize Mean Flow Time, Part I: Constructive Algorithms Heidemarie Bräsel, André Herms, Marc Mörig, Thomas Tautenhahn, Jan Tusch, Frank Werner Otto-von-Guericke-Universität,
ACCUPLACER Arithmetic & Elementary Algebra Study Guide
ACCUPLACER Arithmetic & Elementary Algebra Study Guide Acknowledgments We would like to thank Aims Community College for allowing us to use their ACCUPLACER Study Guides as well as Aims Community College
Data Mining Techniques in CRM
Data Mining Techniques in CRM Inside Customer Segmentation Konstantinos Tsiptsis CRM 6- Customer Intelligence Expert, Athens, Greece Antonios Chorianopoulos Data Mining Expert, Athens, Greece WILEY A John
TABLE OF CONTENTS CHAPTER TITLE PAGE
viii TABLE OF CONTENTS CHAPTER TITLE PAGE TITLE PAGE DECLARATION DEDICATION ACKNOWLEDGEMENT ABSTRACT ABSTRAK TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF APPENDICES I II III IV VI VII VIII
DEVELOPMENT OF SCHEDULING PROGRAM AT STAMPING TOOLS DIVISION P.T. MEKAR ARMADA JAYA MAGELANG
DEVELOPMENT OF SCHEDULING PROGRAM AT STAMPING TOOLS DIVISION P.T. MEKAR ARMADA JAYA MAGELANG THESIS A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Bachelor of Engineering
On lot streaming with multiple products
DISKUSSIONSARBEITEN DER FAKULTÄT FÜR WIRTSCHAFTSWISSENSCHAFTEN DER UNIVERSITÄT BIELEFELD On lot streaming with multiple products Martin Feldmann Dirk Biskup Department of Business Administration and Economics
A SIMULATION STUDY FOR DYNAMIC FLEXIBLE JOB SHOP SCHEDULING WITH SEQUENCE-DEPENDENT SETUP TIMES
A SIMULATION STUDY FOR DYNAMIC FLEXIBLE JOB SHOP SCHEDULING WITH SEQUENCE-DEPENDENT SETUP TIMES by Zakaria Yahia Abdelrasol Abdelgawad A Thesis Submitted to the Faculty of Engineering at Cairo University
Practical Hadoop. Security. Bhushan Lakhe
Practical Hadoop Security Bhushan Lakhe Contents J About the Author About the Technical Reviewer Acknowledgments Introduction xiii xv xvii xix Part I: Introducing Hadoop and Its Security 1 Chapter 1: Understanding
