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Intelligent Systems in Operations: Methods, Models and Applications in the Supply Chain

Book Description

Intelligent Systems in Operations: Models, Methods, and Applications introduces current and original research in intelligent systems and methodologies. This book intends to provide knowledge and insights on present and future AI applications in OM from current research-oriented thinking on AI-based systems in the benefit of OM tools and decisions in terms of theoretical and empirical models, methods and their comparisons, and actual and proposed applications. A must have for industry professionals seeking examples of AI applications in OM, researchers looking for material that can be extended into further research, and graduate students in the field.

Table of Contents

  1. Copyright
  2. Editorial Advisory Board
  3. List of Reviewers
  4. Preface
  5. Acknowledgment
  6. 1. Employing Intelligent Decision Systems to Aid in Information Technology Project Status Decisions
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
      1. Decision Support Systems
      2. Case-Based Reasoning
        1. Origin of CBR
        2. Case-Based Reasoning Process
      3. Intelligent Agents
      4. Background Summary
    4. RECOGNITION-PRIMED DECISION MAKING ENABLED COLLABORATIVE AGENTS SIMULATING TEAMWORK (R-CAST)
      1. RPD Decision Making Process
      2. RPD Model and Case-Based Reasoning (CBR)
    5. CONVERSION OF R-CAST TO OPERATE IN THE PROJECT MANAGEMENT WORLD
      1. Project Management Simulation Example
      2. Active Knowledge Base (KB) Component
        1. FactType
        2. Fact
        3. Rules
      3. Experience Knowledge Base (EKB) Component
        1. Experience Space Hierarchy
        2. Structure of an Experience
      4. R-CAST Conversion Summary
    6. KNOWLEDGE ACQUISITION ACTIVITIES — FIRST ITERATION
      1. Development of Project Management Scenarios
      2. Structure of Project Management Scenarios
      3. SME Selection Criteria
      4. Knowledge Acquisition Interview Strategy
      5. Preliminary Empirical Results
    7. INCORPORATING FUZZY LOGIC
      1. Fuzzy Logic
        1. Brief Background
      2. Fuzzy Systems and Reasoning
      3. Uncertainty, Fuzziness, and Project Management
      4. Using Fuzzy Logic to Reduce Cognitive Dissonance
      5. Fuzzy Aggregation
        1. Fuzzy Aggregation Techniques
          1. Min/Max Aggregation
          2. Additive Aggregation
    8. KNOWLEDGE ACQUISITION ACTIVITIES — SECOND ITERATION
      1. Fuzzy Logic Exercises
      2. Initial Fuzzy Logic Interview Results (% Over Budget Fuzzy Exercise)
    9. ONGOING WORK
    10. FUTURE RESEARCH
    11. CONCLUSION
    12. REFERENCES
    13. ADDITIONAL READING
  7. 2. SAT and Planning: An Overview
    1. ABSTRACT
    2. INTRODUCTION
    3. MODERN SAT SOLVING
    4. TECHNIQUES FOR SOLVING SAT
      1. Complete Methods
      2. Incomplete Methods
    5. PLANNING AS SAT
    6. CONCLUSION
    7. REFERENCES
  8. 3. Integrated Multi-Agent Coordination
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. EXTENDING HIERARCHICAL TASK NETWORKS (EHTNS) TO REPRESENT COORDINATION PROBLEMS
    4. 3. GPGP MECHANISMS AND THE SCHEDULE COORDINATION PROBLEM
    5. 4. COORDINATION MECHANISM SELECTION
    6. 5. EXTENDED SET OF COORDINATION MECHANISMS
    7. 6. IMPLEMENTATION
    8. 7. DISCUSSIONS
      1. 7.1 Look-Ahead Planning
      2. 7.2 Domain Applications of the EHTN-Based Integrated Multi-Agent Coordination
        1. 7.2.1 Team Performance
        2. 7.2.2 Human Agent Collaboration
    9. 8. CONCLUSION
    10. REFERENCES
    11. ENDNOTES
  9. 4. Complex Event Processing in Sensor-Based Decision Support Systems
    1. ABSTRACT
    2. INTRODUCTION
    3. FOUNDATIONS OF EVENT-DRIVEN ARCHITECTURE
      1. Events
      2. Event Flow
      3. Complex Event Processing
    4. STATE OF THE ART
    5. EDA FOR SENSOR-BASED DECISION SUPPORT SYSTEMS
      1. Event-Driven Architecture for Sensor-Based Systems
      2. Structural Event Models
      3. Complex Event Processing for Decision-Support Systems
    6. CASE STUDY: TRAFFIC MANAGEMENT
      1. Scenario
      2. Event Model
      3. Event Processing Rules
    7. CONCLUSION AND FUTURE RESEARCH
    8. ACKNOWLEDGMENT
    9. REFERENCES
  10. 5. Computational Intelligence for Information Technology Project Management
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
      1. Computational Intelligence
      2. Fuzzy Logic
      3. Neural Networks
      4. Genetic Algorithms
      5. Background Summary and Additional Comments
    4. CURRENT CI APPLICATIONS FOR IT PROJECT MANAGEMENT
      1. Fuzzy Logic Applications
        1. Criticality Prediction
        2. Software Quality Prediction
        3. Software Development Effort Estimation
        4. Project Team Selection
        5. Project Portfolio Management
      2. Neural Network Applications
        1. Software Error Estimation
        2. Software Cost Estimation
        3. Software Risk Analysis
        4. Software Process Improvement
      3. Genetic Algorithm Applications
        1. Project Scheduling
        2. Software Complexity Identification
        3. Software Testing
      4. Hybrid Applications
        1. Software Risk Analysis
        2. Software Quality Prediction
        3. Software Maintenance
        4. Project Scheduling
      5. Potential CI Applications for IT Project Management
        1. Project Status
        2. Earned Value Analysis
        3. Return on Investment Calculation
        4. Customer Satisfaction
        5. Lack of PM Skills
        6. Communication and Situational Awareness
        7. Document Management
    5. CONCLUSION
    6. REFERENCES
    7. ADDITIONAL READING
  11. 6. Piece-Mold-Machine Manufacturing Planning
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED RESEARCH
    4. MANUFACTURING PLANNING
      1. Integer Linear Programming
      2. Computational Complexity of PMM
        1. Decision Version of Problem PMM (Named as dPMM)
        2. Decision Version of Multiple Knapsacks Problem(MKP)
    5. SOLUTION METHODS
      1. Exact Solutions for PMM Using a B&B
      2. Iterated Local Search
      3. Computational Results
    6. CONCLUSION
    7. REFERENCES
  12. 7. Intelligent Simulation System for Supply Chain Event Management (SCEM)
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. APPLICATIONS OF INTELLIGENT SIMULATION SYSTEMS ON SCEM
      1. 2.1. Simulation Framework
      2. 2.2. Implementation Issues
    4. 3. AN ILLUSTRATIVE CASE STUDY
    5. 4. CONCLUSION
    6. REFERENCES
  13. 8. Mathematical Models for Optimizing the Global Mining Supply Chain
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
      1. An Overview of Mining Operations
      2. Basic Problems in Mining Operations
      3. Strategic Mine Planning
      4. Open Pit and Underground Mine Planning
        1. The Ultimate Pit Limit Problem
        2. Mine Production Scheduling
        3. Stochastic Approaches
        4. Considerations on Underground Mining Operations
      5. Mine Load and Haulage Equipment Allocation and Dispatching
      6. Railway Scheduling and Dispatch
      7. Port Planning and Scheduling
      8. Blending
      9. Other Applications of Artificial Intelligence in the Mining Industry
    4. INTEGRATED APPROACHES AND THE GLOBAL MINING SUPPLY CHAIN
      1. Some Related Work
      2. The Global Mining Supply Chain
    5. SOLUTION APPROACHES TO THE INTEGRATED MINING SUPPLY CHAIN
    6. FUTURE RESEARCH DIRECTIONS
    7. CONCLUSION
    8. REFERENCES
      1. KEY TERMS AND DEFINITIONS
    9. ENDNOTE
  14. 9. Negotiation Policies for E-Procurement by Multi Agent Systems
    1. ABSTRACT
    2. INTRODUCTION
    3. LITERATURE REVIEW
    4. WORKFLOW DESIGN
    5. E-COMMERCE OPERATIONAL APPROACHES
      1. Negotiation Process
      2. Qualified Contemporary Multi Negotiation (QCMN)
      3. Auction
      4. Single Round
      5. Customer Behavior
    6. PRODUCTION PLANNING ALGORITHM
    7. THE PRODUCTION PLANNING MODEL
    8. SIMULATION ENVIRONMENT
    9. SIMULATION RESULTS
    10. CONCLUSIONS AND FUTURE DEVELOPMENT
    11. REFERENCES
  15. 10. A 3D Vision-Based Solution for Product Picking In Industrial Applications
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
      1. The Pose Estimation Problem
      2. Camera Model
      3. Feature Extraction
    4. A MONOCULAR 3D VISION SYSTEM FOR PICKING APPLICATIONS
      1. Picking Application
      2. Production Lines with Manual and Automatic Operations
      3. Requirements for the Vision System
      4. The Vision System
    5. CALIBRATION
    6. EXPERIMENTAL SETUP
    7. NUMERICAL RESULTS
    8. FUTURE RESEARCH DIRECTIONS
    9. CONCLUSION
    10. REFERENCES
    11. KEY TERMS AND DEFINITIONS
  16. 11. Developing a Collaborative Supply Chain Management Platform: A Service —Oriented Approach
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. STATE OF THE ART
      1. 2.1 Actor Collaboration in Supply Chain Management
      2. 2.2 The Services Approach in Supply Chain Management
      3. 2.3 Related Work
    4. 3. A SERVICE-ORIENTED DEVELOPMENT PROCESS APPLIED TO OUTBOUND LOGISTICS
      1. 3.1 The Process
      2. 3.2. Generic Process Description
        1. 3.2.1. Analysis
        2. 3.2.2. Architectural Design
        3. 3.2.3. Detailed Design
      3. 3.3. Outbound Logistics
    5. 4. THE COLLABORIATIVE PLATFORM DEVELOPMENT
      1. 4.1. Analysis
      2. 4.2 Design
        1. 4.2.1 Upper Layer: User Client
        2. 4.2.2 Middle Layer: Service Center
        3. 4.2.3 Bottom Layer: Technical/Algorithm and Service Process
      3. 4.3 Implementation
    6. 5 FUTURE RESEARCH DIRECTIONS
    7. 6 CONCLUSION
    8. ACKNOWLEDGMENT
    9. REFERENCES
  17. I. Selected Readings
    1. 12. Applying Dynamic Causal Mining in Health Service Management
      1. ABSTRACT
      2. LITERATURE REVIEW
        1. Medical and Health Management
      3. APPLYING DATA MINING IN MEDICAL AND HEALTH MANAGEMENT
        1. Dynamic Causal Mining
        2. Association Mining and Process Mining
        3. System Thinking and System Dynamics
      4. BASIC CONCEPTS OF DYNAMIC CAUSAL MINING
      5. DATASET
        1. Time Stamp
        2. Data
      6. MEASUREMENTS
      7. RULE REPRESENTATION
        1. Dynamic Policy
      8. ILLUSTRATIVE EXAMPLE
      9. MINING ALGORITHM
        1. Problem Formulation
        2. Algorithm Description
      10. EXPERIMENT
        1. Data Preparation
      11. DISCUSSION
      12. CONCLUSION AND SUMMARY
      13. REFERENCES
    2. 13. Using Simulation Systems for Decision Support
      1. ABSTRACT
      2. INTRODUCTION
      3. RELEVANT WORK
      4. REQUIREMENTS FOR SIMULATION SYSTEMS
        1. Modeling of Relevant System Characteristics
        2. Ability to Obtain All Relevant Data
        3. Validation and Verification of Model, Simulation, and Data
        4. Creating Situation Adequate Behavior
        5. Additional Issues When Using Federations of Simulation Systems
      5. EXAMPLES OF DECISION SUPPORT SIMULATION APPLICATIONS
      6. CURRENT DEVELOPMENTS
      7. SUMMARY
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    3. 14. Genetic Algorithm to Solve Multi-Period, Multi-Product, Bi-Echelon Supply Chain Network Design Problem
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE SURVEY
      4. RESEARCH GAP ANALYSIS
        1. Aim
        2. Objectives
        3. Scope
        4. Problem Structure
        5. Problem Definition
        6. Methodology
          1. Assumptions
          2. Constraints
          3. Notations
        7. Mathematical Model
      5. METHODOLOGY OF GADIM
        1. The Procedure of the Classical GA is as follows
        2. Steps in Crossover
        3. Other Methodologies
          1. Complete Enumeration
          2. LINDO
          3. CPLEX
      6. ILLUSTRATIVE EXAMPLE FOR GADIM
      7. RESULTS
        1. Results Obtained Through GA
      8. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
      9. REFERENCES
    4. 15. Data Mining in Decision Support for Bioenergy Production
      1. ABSTRACT
      2. INTRODUCTION
      3. DECISION MAKING PROBLEMS
      4. MULTILEVEL DECISION MAKING LIFE CYCLE (MLDMLC)
      5. DATA MANIPULATION AND CLUSTERING
        1. Bioenergy Resources Data Preparation and Clustering
      6. GENERAL BIOENERGY DECISION SYSTEM (gBEDS)
        1. The gBEDS Conceptual Structure
        2. How gBEDS Works?
      7. HOW DATA MINING IS USED IN gBEDS
        1. Fuzzy C-means Clustering
        2. Number of Clusters
        3. Collection Points Clustering and Impacts Estimation
      8. CASE STUDY
        1. Study Area
        2. Scenario Results
        3. Clustering Session
        4. Data Mining and Optimization
      9. CONCLUSION
      10. FUTURE RESEARCH DIRECTIONS
      11. NOTE
      12. REFERENCES
      13. ADDITIONAL READING
    5. 16. Towards a Semiotic Metrics Suite for Product Ontology Evaluation
      1. ABSTRACT
      2. INTRODUCTION
      3. BASIC MODEL OF SUPPLY CHAIN AND PRODUCT ONTOLOGY
      4. PROPOSED METRICS SUITE
        1. Semiotics
        2. Metrics
        3. Syntactic Quality
        4. Semantic Quality
        5. Pragmatic Quality
        6. Social Quality
      5. PRELIMINARY VALIDATION
        1. Validation Scenario
        2. Results
        3. Implications
      6. RELATED WORK
      7. CONCLUSION
      8. REFERENCES
      9. ENDNOTES
  18. Compilation of References
  19. About the Contributors