Efnisyfirlit

• Front Matter
  • Dedication
  • Foreword
  • Acknowledgments
  • The Author
  • Introduction
• CHAPTER 1 Introduction to Quantitative Decision‐Making Methods in Health Care Management
  • Learning Objectives
  • Historical Background and the Development of Decision Techniques
  • The Health Care Manager and Decision Making
  • Information Technology (IT) and Health Care Management
  • The Scope of Health Care Services, and Recent Trends
    • TABLE 1.1. Total Expenditures on Health as % GDP for 30 OECD Countries
    • TABLE 1.2. Distribution of Health Providers and Health Workers in Health Services: 2006, and Expected Growth
  • Health Care Services Management
  • Distinctive Characteristics of Health Care Services
    • TABLE 1.3. Health Services by Occupation in 2006, and Projected Growth
    • Patient Participation
    • Simultaneous Production and Consumption
    • Perishable Capacity
    • The Intangible Nature of Health Care Outputs
    • The High Levels of Judgment Called Upon, and the Heterogeneous Nature of Health Care
  • Summary
  • Key Terms
• CHAPTER 2 Forecasting
  • Learning Objectives
  • Steps in the Forecasting Process
    • Identify the Goal of the Forecast
    • Establish a Time Horizon
    • Select a Forecasting Technique
    • Conduct the Forecast
    • Monitor Accuracy
  • Forecasting Approaches
    • Judgmental Forecasts
    • Time‐Series Approach
    • Techniques for Averaging
      • Naïve Forecasts
        • FIGURE 2.1. Seasonal Variation Characteristics.
        • FIGURE 2.2. Cycle Variation.
        • FIGURE 2.3. Random Variation and Trend.
      • Moving Averages (MA)
        • Example 2.1
          • Solution
            • FIGURE 2.4. Excel Template Solution: Moving Average (MA3) for OB/GYN Clinic.
      • Determining a Reasonable Number of Periods for the Moving Average
        • Example 2.2
          • Solution
      • Weighted Moving Average (WMA)
        • Example 2.3
          • Solution
            • FIGURE 2.5. Excel Template Solution: Weighted Moving Average (WMA3) for OB/GYN Clinic.
      • Single Exponential Smoothing (SES)
        • Example 2.4
          • Solution
            • FIGURE 2.6. Excel Template Solutions to the OB/GYN Example, Using Single Exponential Smoothing (SES) with α = 0.3 and α = 0.5
    • Techniques for Trend
      • Example 2.5
        • Solution
      • Forecasting Techniques Based on Linear Regression
        • Example 2.6
          • Solution
            • FIGURE 2.7. Excel Template Solutions to the OB/GYN Example, Using Single Exponential Smoothing (SES) with α = 0.0 and α = 1.0.
        • FIGURE 2.8. Linear Regression.
        • Example 2.7
          • Solution
            • FIGURE 2.9A
            • FIGURE 2.9B. Excel Setup – Linear Regression for the Multihospital System Example.
            • FIGURE 2.10. Excel Solution to the Multihospital System Example.
      • Linear Regression as a Trend Line
        • FIGURE 2.11. Excel Linear Regression as a Trend.
        • Example 2.8
          • Solution
            • FIGURE 2.12. Linear Regression as a Trend.
            • FIGURE 2.13. Excel Template Solution to the OB/GYN Example.
      • Trend‐Adjusted Exponential Smoothing
        • Example 2.9
          • Solution
            • FIGURE 2.14. Excel Template – SEST Solution to Example 2.9.
    • Techniques for Seasonality
      • Example 2.10
    • Solution
      • Quarterly indexes Technique
      • Monthly indexes Technique
        • TABLE 2.1. Heal‐Me Hospital Average Daily Patient Days
        • TABLE 2.2. Quarterly Indexes for Heal-Me Hospital.
        • TABLE 2.3. Monthly Indexes for Heal-Me Hospital.
        • TABLE 2.4. Daily Indexes for Heal-Me Hospital.
      • Daily Indexes Technique
    • Employing Seasonal Indexes in Forecasts
      • FIGURE 2.15. Seasonality‐Removed Trend Data for Heal‐Me Hospital Patient Demand.
    • Accuracy of Forecasts
      • TABLE 2.5. Monthly and Daily Adjusted Forecasts for Heal-Me Hospital.
      • TABLE 2.6. Error Calculations.
    • Forecast Control
      • FIGURE 2.16. Alternative Forecasting Methods and Accuracy, Measured by MAD and MAPE.
      • FIGURE 2.17. Linear Trend with Tracking Signal for Patient Visit Forecast, Heal‐Me Hospital.
      • FIGURE 2.18. Tracking Signal for Patient Visit Forecast, Heal‐Me Hospital.
  • Summary
  • Key Terms
  • Exercises
    • TABLE EX 2.1.
    • TABLE EX 2.2.
    • TABLE EX 2.5.
    • TABLE EX 2.6.
    • TABLE EX 2.8.
• CHAPTER 3 Decision Making in Health Care Facilities
  • Learning Objectives
  • The Decision Process
    • What Causes Poor Decisions?
    • The Decision Level and Decision Milieu
    • Decision Making Under Uncertainty
      • Maximin
      • Maximax
      • Laplace
      • Minimax Regret
      • Hurwitz
    • Payoff Table
      • TABLE 3.1. Payoff Table.
      • Example 3.1
    • Maximin Case
      • TABLE 3.2. Demand for Additional MRIs.
    • Maximax Case
      • TABLE 3.3. Maximin Solution.
      • TABLE 3.4. Maximax Solution.
    • Hurwitz Case
    • Minimax Regret Case
      • TABLE 3.5. Sensitivity Analysis Using Hurwitz Optimism Parameters.
      • TABLE 3.6. Maximax Solution.
    • Laplace Case
      • TABLE 3.7. Laplace Strategy.
    • Decision Making Under Risk
    • Expected Value Model
      • TABLE 3.8. Payoff Table for EMV.
      • TABLE 3.9. Expected Opportunity Loss.
    • Expected Opportunity Loss
    • Expected Value of Perfect Information (EVPI)
      • TABLE 3.10. Best Outcomes Under Certainty.
      • TABLE 3.11. Total Cost of Alternatives Under Various Demand Conditions.
    • What If Payoffs Are Costs?
      • TABLE 3.12. Regret Table Using Costs.
  • The Decision Tree Approach
    • Analysis of the Decision Tree: Rollback Procedure
      • FIGURE 3.1. Decision Tree.
      • FIGURE 3.2. Rollback Method.
    • Excel Illustration of Payoff and Decision Tree Methods
  • Decision Analysis with Nonmonetary Values and Multiple Attributes
    • FIGURE 3.3. Payoff Table Analysis Using Excel Template for Decision Analysis.
    • Example 3.2
    • FIGURE 3.4. Decision Tree and Rollback Procedure Using Excel Template for Decision Analysis.
    • Dominance Procedure
      • TABLE 3.13. Summary of Supplier Proposals.
    • Minimum Attribute Satisfaction Procedure
    • Most Important Attribute Procedure
  • Summary
  • Key Terms
  • Exercises
    • TABLE EX 3.1.
    • TABLE EX 3.2.
    • TABLE EX 3.3.
    • TABLE EX 3.5.
    • TABLE EX 3.8.
    • TABLE EX 3.9.
    • TABLE EX 3.10.
    • TABLE EX 3.11.
    • TABLE EX 3.12.
• CHAPTER 4 Facility Location
  • Learning Objectives
  • Location Methods
    • Cost‐Profit‐Volume (CPV) Analysis
      • Example 4.1
        • Solution
      • FIGURE 4.1. Total Cost of Alternative Imaging Sites.
    • Factor Rating Methods
      • FIGURE 4.2. Profit Evaluation of Alternative Sites.
      • Example 4.2
      • TABLE 4.1. Factors to be Considered in Establishing a Satellite Clinic.
      • TABLE 4.2. Relative Scores on Factors for a Satellite Clinic.
      • TABLE 4.3. Relative Factor Scores and Weights.
      • TABLE 4.4. Composite Scores.
    • Multi‐Attribute Methods
      • Dominance Procedure
        • TABLE 4.5. Satellite Clinic Factor Rankings and Minimum Acceptable Levels.
      • Minimum Attribute Satisfaction Procedure
      • Most Important Attribute Procedure
        • TABLE 4.6. Satellite Clinic Factor Minimum Acceptable Levels.
        • TABLE 4.7. Satellite Clinic Factor Importance Rankings.
    • Center‐of‐Gravity Method
      • FIGURE 4.3. Richmond Metropolitan Area Hospitals.
      • TABLE 4.8. Selected Richmond Metropolitan Area Hospitals.
      • TABLE 4.9. Selected Richmond Metropolitan Area Hospitals and Their Interaction with the Blood Bank.
    • Geographic Information Systems (GIS) in Health Care
      • FIGURE 4.4. Richmond Metropolitan Area Blood Bank Locations.
      • FIGURE 4.5. Geographic Information Systems.
  • Summary
  • Key Terms
  • Exercises
    • TABLE EX 4.2.
    • TABLE EX 4.3.
    • TABLE EX 4.4.
    • TABLE EX 4.5.
    • TABLE EX 4.6.
• CHAPTER 5 Facility Layout
  • Learning Objectives
  • Product Layout
  • Process Layout
    • Process Layout Tools
      • Example 5.1
        • FIGURE 5.1. Available Space for Layout of Long‐Term Care Facility.
        • FIGURE 5.2. Closeness Rating Chart for Long‐Term Care Facility.
        • FIGURE 5.3. A and X Closeness Representation.
        • FIGURE 5.4. Layout Solution.
    • Method of Minimizing Distances and Costs
    • Computer‐Based Layout Programs
    • Fixed‐Position Layout
      • EXHIBIT 5.1. From‐To Chart for a Small Hospital.
      • Example 5.2
        • TABLE 5.1. Distance and Flows Among Three Hospital Departments.
        • TABLE 5.2. Possible Assignment Configurations of Departments to Three Locations.
        • TABLE 5.3. Ranking Departments According to Highest Flow.
        • TABLE 5.4. Total Cost of a Layout.
        • FIGURE 5.5. Excel Template Solution.
      • FIGURE 5.6. Excel Template Solution and Final Layout for a Small Hospital.
  • Summary
  • Key Terms
  • Exercises
    • FIGURE EX 5.1
    • FIGURE EX 5.2
    • FIGURE EX 5.3
    • TABLE EX 5.4
    • TABLE EX 5.5
    • TABLE EX 5.6.1
    • TABLE EX 5.6.2
• CHAPTER 6 Reengineering
  • Learning Objectives
  • Work Design in Health Care Organizations
    • Work Design
      • FIGURE 6.1. Work Design—A Systems Perspective.
      • Job Design
        • FIGURE 6.2. Socio ‐ Technical School Approach.
    • Work Measurement Using Time Standards
      • Stopwatch Time Studies
      • Determination of Number of Cycles (Sample Size)
        • Example 6.1
          • Solution
        • TABLE 6.1. Typical Allowance Percentages for Varying Health Care Delivery Working Conditions.
        • Example 6.2
          • Solution
      • Standard Elemental Times and Predetermined Standards
        • TABLE 6.2. Observed Times and Performance Ratings for Nursing Unit Activities.
        • TABLE 6.3. Observed and Normal Time Calculations for Nursing Unit Activities.
    • Work Measurement Using Work Sampling
      • TABLE 6.4. Abridged Patient Care Tasks in a Nursing Unit.
      • TABLE 6.5. Work Sampling Data Collection Form for Nursing Unit.
      • Training Observers
      • Determination of Sample Size
        • Example 6.3
          • Solution
      • Development of a Random Observation Schedule
        • Example 6.4
          • Solution
            • TABLE 6.6. Random Numbers.
        • Example 6.5
          • Solution
            • TABLE 6.7. Development of the Schedule for a Work Sampling Study.
            • TABLE 6.8. Final Work Sampling Schedule.
      • Random Observation Schedule Using Excel
        • Example 6.6
          • Solution
            • FIGURE 6.3. Random Observation Schedule.
    • Work Simplification
      • FIGURE 6.4. Stabilized Dates and Times.
      • Work Distribution Chart
        • FIGURE 6.5. Valid Dates and Times.
        • FIGURE 6.6. Final Observation Schedule.
        • TABLE 6.9. Partial Work Distribution Chart for Nursing Unit.
        • FIGURE 6.7. Flow Process Chart for Emergency Room Specimen Processing.
      • Flow Process Chart
        • FIGURE 6.8. Commonly Used Flow Chart Symbols.
      • Flow Chart
    • Worker Compensation
      • FIGURE 6.9. Flow Chart for Emergency Room Specimen Processing.
  • Summary
  • Key Terms
  • Exercises
    • TABLE EX 6.2
    • TABLE EX 6.3
    • TABLE EX 6.4
• CHAPTER 7 Staffing
  • Learning Objectives
  • Workload Management Overview
    • FIGURE 7.1. Workload Management
    • Establishment of Workload Standards and Their Influence on Staffing Levels
      • TABLE 7.1. Examples of Work Standards.
    • Patient Acuity Systems
      • GRASP System
      • NPAQ System
        • TABLE 7.2. Daily Census, Required Labor Hours, and Acuity Level Statistics for a Medical or Surgical Floor.
        • TABLE 7.3. Average Census, Required Labor Hours, and Acuity Level Statistics for a Medical or Surgical Floor
    • The Development of Internal Workload Standards
      • Utilization of FTEs
        • TABLE 7.4. Weighted Average Utilization for a Laboratory Based on Workload Fluctuations by Shift.
        • Example 7.1
          • Solution
            • TABLE 7.5. Workload Standards for Microscopic Procedures in Laboratory.
            • TABLE 7.6. Calculation of Staffing Requirements for Microscopic Procedures.
      • Determination of FTEs for Nurse Staffing
        • Example 7.2
          • Solution
      • Coverage Factor
        • TABLE 7.7. The Effect of Shift Alternatives on Staffing–The Coverage Factor.
      • Reallocation through Daily Adjustments
        • FIGURE 7.2. Distribution of Daily Workload on a Nursing Unit.
    • External Work Standards and Their Adjustments
      • FIGURE 7.3. Workload Standard Tolerance Ranges.
    • Productivity and Workload Management
  • Summary
  • Key Terms
  • Exercises
    • TABLE EX 7.1
    • TABLE EX 7.3
    • TABLE EX 7.6
    • TABLE EX 7.7
• CHAPTER 8 Scheduling
  • Learning Objectives
  • Staff Scheduling
    • FIGURE 8.1. Comparison of Eight‐ and Ten‐Hour Shifts.
    • The Eight‐, Ten‐, and Twelve‐Hour Shifts—Studies of Shift Patterns
      • FIGURE 8.2. Pattern of Alternating Eight‐ and Twelve‐Hour Shifts.
    • Cyclical Scheduling
      • EXHIBIT 8.1. Cyclical Staffing Schedules for Four and Five Weeks.
    • Flexible Scheduling
    • Computerized Scheduling Systems
    • Implementation of a New Work System
    • Surgical Suite Resource Scheduling
      • First Come/First Served (FC/FS)
      • Block Scheduling
      • Dynamic Block Scheduling
      • Longest Case First (LCF)
      • Shortest Case First (SCF)
      • Top Down/Bottom Up
      • Multiple Room System
        • EXHIBIT 8.2. An Example of OR Block Schedule: Surgical Suite Scheduling Method.
    • Assessment of Scheduling Alternatives
    • Estimation of Procedure Times
  • Summary
  • Key Terms
  • Exercises
• CHAPTER 9 Productivity
  • Learning Objectives
  • Trends in Health Care Productivity: Consequences of PPS
    • Productivity Definitions and Measurements
      • Example 9.1
        • Solution
      • Productivity Benchmarking
      • Multi‐factor Productivity
        • Example 9.2
          • Solution
    • Commonly Used Productivity Ratios
      • Hours per Patient Day (or Visit)
        • Example 9.3
          • Solution
        • Example 9.4
          • Solution
    • Adjustments for Inputs
      • Skill Mix Adjustment
        • Example 9.5
          • Solution
        • Example 9.6
          • Solution
    • Adjustments for Outputs
      • Service Mix Adjustments
        • Example 9.7
          • Solution
    • Case Mix Adjustments
      • Example 9.8
        • Solution
    • Productivity Measures Using Direct Care Hours
      • Percentage of Hours in Direct Care
      • Percentage of Adjusted Hours in Direct Care
        • Example 9.9
          • Solution
    • The Relationships Between Productivity and Quality in Hospital Settings
      • FIGURE 9.1. Productivity and Quality Trade‐Off.
    • Summary of Productivity‐Related Dilemmas in the Hospital Setting
    • Dealing with the Multiple Dimensions of Productivity: New Methods of Measurement
      • Technical Efficiency
        • FIGURE 9.2. Substitution of Physicians and Nurse Practitioners: A Look at Technical Efficiency.
      • Economic Efficiency
    • Data Envelopment Analysis
      • FIGURE 9.3. Example of DEA Efficiency Frontier Formulation.
    • Overview on Improving Health Care Productivity
  • Summary
  • Key Terms
  • Exercises
    • TABLE EX 9.1
    • TABLE EX 9.2
    • TABLE EX 9.3
    • TABLE EX 9.4
    • TABLE EX 9.5
    • TABLE EX 9.6
    • TABLE EX 9.7
• CHAPTER 10 Resource Allocation
  • Learning Objectives
  • Linear Programming
    • Maximization Models
      • Example 10.1
        • Solution
      • FIGURE 10.1. Graphic Solution for Insurance Company Problem.
      • FIGURE 10.2. Excel Setup for the Insurance Company Problem.
      • FIGURE 10.3. Excel Solver.
      • FIGURE 10.4. Identifying Constraints and Solution Cells.
      • FIGURE 10.5. Selection of Solution Reports.
      • FIGURE 10.6. Answer Report.
      • FIGURE 10.7. Sensitivity Report.
      • FIGURE 10.8. Limits Report.
      • FIGURE 10.9. Graphic Explanation of Sensitivity Analysis: Shadow Price and its Impact on Alternate Optimal Solutions.
    • Minimization Models
      • Example 10.2
        • FIGURE 10.10. Graphic Solution for Minimization Example.
    • Integer Linear Programming
      • FIGURE 10.11. Excel Setup for the Minimization Problem.
      • FIGURE 10.12. Solution to the Minimization Problem.
      • FIGURE 10.13. Minimization Problem Answer Report.
      • FIGURE 10.14. Minimization Problem Sensitivity Report.
      • FIGURE 10.15. Minimization Problem Limits Report.
      • Example 10.3
        • Solution
          • FIGURE 10.16. Integer Programming: Excel Setup for the Staff Scheduling Problem.
          • FIGURE 10.17. Identifying Constraints and Integer Values.
          • FIGURE 10.18. Solution to the Staff Scheduling Problem.
          • FIGURE 10.19. Answer Report for the Staff Scheduling Problem.
      • TABLE 10.1. Nurse Scheduling with Integer Programming.
  • Summary
  • Key Terms
  • Exercises
    • TABLE EX 10.2
    • TABLE EX 10.3
    • TABLE EX 10.4
    • TABLE EX 10.5.1.
    • TABLE EX 10.5.2.
    • TABLE EX 10.6
• CHAPTER 11 Supply Chain and Inventory Management
  • Learning Objectives
  • Health Care Supply Chain
    • FIGURE 11.1. Health Care Supply Chain.
    • Manufacturers/Suppliers
    • Distributors, Wholesalers, and Electronic Data Interchange (EDI)
    • Group Purchasing Organizations (GPOs)
    • E‐Distributors
    • Flow of Materials
    • Supply Chain Management Issues for Providers
    • Contemporary Issues in Medical Inventory Management
      • Just‐in‐Time (JIT) and Stockless Inventories
      • Advantages and Disadvantages of JIT and Stockless Inventory
      • Single versus Multiple Vendors
    • Traditional Inventory Management
    • Requirements for Effective Inventory Management
    • Inventory Accounting Systems
      • Universal Product Codes (UPCs)
    • Lead Time
    • Cost Information
    • Classification System
      • TABLE 11.1. A-B-C Classification Analysis.
    • Economic Order Quantity Model
      • FIGURE 11.2. The Inventory Order Cycle for Basic EOQ Model.
      • FIGURE 11.3. The Economic Ordering Quantity Model.
      • Example 11.1
        • Solution
    • Excel Solution
      • FIGURE 11.4. Excel Solution to the Syringe Problem.
    • When to Reorder
      • FIGURE 11.5. Multi‐Item Inventory EOQ and ABC Analysis.
      • Example 11.2
        • Solution
      • Example 11.3
        • Solution
  • Summary
  • Key Terms
  • Exercises
    • TABLE EX 11.4
    • TABLE EX 11.5
    • TABLE EX 11.6
• CHAPTER 12 Quality Control
  • Learning Objectives
  • Quality in Health Care
    • FIGURE 12.1. Quality Measurement.
    • Quality Experts
    • Quality Certifications and Awards
    • Total Quality Management (TQM) and Continuous Quality Improvement (CQI)
      • FIGURE 12.2. The Deming Wheel/Shewhart Cycle.
    • Six Sigma
  • Quality Measurement and Control Techniques
    • Process Variability
    • Monitoring Variation through Control Charts
      • FIGURE 12.3. Process Capability.
    • Control Charts for Attributes
      • FIGURE 12.4. Control Limits, Random and Nonrandom Sample Observations.
      • c‐Chart
        • Example 12.1
          • Solution
            • FIGURE 12.5. ABC Medical Center Infection Control Monitoring.
      • p‐Chart
        • Example 12.2
          • Solution
            • FIGURE 12.6. Holistic Care Corporation's Quality Monitoring.
    • Control Charts for Variables
      • Mean Charts
        • Standard Deviation Approach
          • FIGURE 12.7. Use of Mean and Range Charts.
          • Example 12.3
            • Solution
        • Range Approach
          • TABLE 12.1. Factors For Determining Control Limits for Mean and Range Charts (for Three-Sigma or 99.7 Percent–Confidence Level).
          • Example 12.4
            • Solution
      • Range Charts
        • Example 12.5
          • Solution
    • Investigation of Control Chart Patterns
      • Run‐Based Pattern Tests
        • FIGURE 12.8. Identification of Runs.
        • Example 12.6
          • Solution
      • Zone Tests
      • Sensitizing Rules for Control Charts
        • FIGURE 12.9. Zone Test.
        • Example 12.7
          • Solution
    • Process Improvement
    • Methods for Generating New Ideas
      • The 5W2H Approach
      • Brainstorming
      • Nominal Group Technique
      • Interviewing
      • Focus Groups
      • Quality Circles “Kaizen Teams.”
      • Benchmarking
    • Tools for Investigating the Presence of Quality Problems and Their Causes
      • Check Sheet
        • FIGURE 12.10. A Check Sheet and Corresponding Histogram for Emergency Room Wait Times.
      • Histogram
      • Scatter Diagram
      • Flow Chart
      • Cause‐and‐Effect Diagram
        • FIGURE 12.11. Scatter Diagram.
        • FIGURE 12.12. A Flow Chart for the X‐Ray Order Process in an Emergency Department.
      • Pareto Diagram
  • Summary
    • FIGURE 12.13. Cause‐and‐Effect Diagram.
    • FIGURE 12.14. Pareto Diagram.
  • Key Terms
  • Exercises
    • TABLE EX 12.1
    • TABLE EX 12.2
    • TABLE EX 12.3
    • TABLE EX 12.4
    • TABLE EX 12.5
    • FIGURE EX 12.13.
• CHAPTER 13 Project Management
  • Learning Objectives
  • The Characteristics of Projects
    • The Project Manager
    • Managing Teams and Relationships on Projects
    • Planning and Scheduling Projects
    • The Gantt Chart
      • EXHIBIT 13.1. Gantt Chart for Launching a New Radiation Oncology Service.
      • TABLE 13.1. Activity Precedence Relationships.
    • PERT and CPM
    • The Network
      • FIGURE 13.1. Network Representations.
    • Critical Path Method (CPM)
      • FIGURE 13.2. AON Network Diagram for Radiation Oncology.
      • TABLE 13.2. Path Lengths for the Radiation Oncology Project.
      • FIGURE 13.3. Activity Start and Finish Times.
      • Computing ES and EF Times
      • Computing LS and LF Times
        • FIGURE 13.4. Excel Setup and Solution to the Radiation Oncology Project, CPM Version.
    • Probabilistic Approach
      • Example 13.1
        • TABLE 13.3. Probabilistic Time Estimates for Radiation Oncology Clinic.
      • TABLE 13.4. Calculation of Expected Time and Standard Deviations on Each Path for the Radiation Oncology Clinic.
      • FIGURE 13.5. Project Completion Probabilities by the Specified Time.
      • TABLE 13.5. Path Completion Probabilities.
      • FIGURE 13.6. Completion Probabilities for Sixty - Five Weeks.
    • The Case of a Dominant Critical Path
      • FIGURE 13.7. Excel Setup and Solution to the Probabilistic Radiation Oncology Project.
    • Project Compression: Trade‐Offs Between Reduced Project Time and Cost
      • TABLE 13.6. Project Completion Probabilities.
      • FIGURE 13.8. Project Duration and Compression (Crashing) Costs.
      • Example 13.2
        • Solution
          • FIGURE 13.9. Project Compression.
      • Iteration 1
      • Iteration 2
      • Iteration 3
        • FIGURE 13.10. Total Cost of Compression.
  • Summary
  • Key Terms
  • Exercises
    • FIGURE EX 13.1
    • FIGURE EX 13.2
    • TABLE EX 13.5
    • TABLE EX 13.6
    • FIGURE EX 13.8
    • TABLE EX 13.10
    • TABLE EX 13.11
    • TABLE EX 13.12
    • TABLE EX 13.14
    • TABLE EX 13.15
    • FIGURE EX 13.15
    • TABLE EX 13.15
• CHAPTER 14 Queuing Models and Capacity Planning
  • Learning Objectives
  • FIGURE 14.1. Queue Phenomenon.
  • FIGURE 14.2. Health Care Service Capacity and Costs.
  • Queuing System Characteristics
    • Population Source
      • FIGURE 14.3. Queuing Conceptualization of Flu Inoculations
    • Number of Servers
    • Arrival Patterns
      • FIGURE 14.4. Conceptualization of a Single‐Line, Multiphase System.
      • FIGURE 14.5. Multiple‐Line Queuing System.
      • FIGURE 14.6. Emergency Room Arrival Patterns.
      • FIGURE 14.7. Measures of Arrival Patterns.
      • FIGURE 14.8. Poisson Distribution.
    • Service Patterns
    • Queue Characteristics
      • FIGURE 14.9. Service Time for ER Patients.
      • EXHIBIT 14.1. Queuing Model Classification.
  • Measures of Queuing System Performance
  • Typical Infinite‐Source Models
  • Model Formulations
    • EXHIBIT 14.2. Queuing Model Notation.
    • Single Channel, Poisson Arrival, and Exponential Service Time (M/M/1)
      • Example 14.1
        • Solution
    • Excel Solution
      • FIGURE 14.10. Excel Setup and Solution to the Diabetes Information Booth Problem.
      • FIGURE 14.11. System Probability Summary for Diabetes Information Booth.
    • Multi‐Channel, Poisson Arrival, and Exponential Service Time (M/M/s > 1)
    • Solution
      • FIGURE 14.12. System Performance for Expanded Diabetes Information Booth.
  • Capacity Analysis and Costs
    • FIGURE 14.13. System Performance Summary for Expanded Diabetes Information Booth with M/M/3.
  • Summary
    • FIGURE 14.14. Capacity Analysis.
    • TABLE 14.1. Summary Analysis for M/M/s Queue for Diabetes Information Booth.
  • Key Terms
  • Exercises
    • TABLE EX 14.5.1
    • TABLE EX 14.5.2
    • TABLE EX 14.5.3
    • TABLE EX 14.5.4
    • TABLE EX 14.5.5
    • TABLE EX 14.7
• CHAPTER 15 Simulation
  • Learning Objectives
  • Simulation Process
    • TABLE 15.1. Simple Simulation Experiment for Public Clinic.
    • TABLE 15.2. Summary Statistics for Public Clinic Experiment.
    • Monte Carlo Simulation Method
      • Process of Monte Carlo Method
      • Empirical Distribution
      • Theoretical Distribution
        • TABLE 15.3. Patient Arrival Frequencies.
        • TABLE 15.4. Probability Distribution for Patient Arrivals.
      • Random Number Look‐Up
        • FIGURE 15.1. Random Numbers*.
        • TABLE 15.5. Cumulative Poisson Probabilities for λ = 1.7.
        • TABLE 15.6. Cumulative Poisson Probabilities for Arrivals: λ = 1.7.
        • TABLE 15.7. Monte Carlo Simulation Experiment for Public Health Clinic.
        • TABLE 15.8. Summary Statistics for Public Clinic Monte Carlo Simulation Experiment.
        • FIGURE 15.2. Excel‐Based Simulated Arrivals.
  • Performance Measures and Managerial Decisions
    • FIGURE 15.3. Excel Program for Simulated Arrivals.
    • FIGURE 15.4. Performance‐Measure‐Based Managerial Decision Making.
  • Summary
  • Key Terms
  • Exercises
• Back Matter
  • APPENDIX A
    • Standard Normal Distribution P(0 < z < x)
  • APPENDIX B
    • Standard Normal Distribution P(–3.5 < z < 3.5)
  • APPENDIX C
    • Cumulative Poisson Probabilities
  • APPENDIX D
  • REFERENCES
  • INDEX

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  Rafbók til eignar þarf að hlaða niður á þau tæki sem þú vilt nota innan eins árs frá því bókin er keypt.
  
  Þú kemst í bækurnar hvar sem er
  Þú getur nálgast allar raf(skóla)bækurnar þínar á einu augabragði, hvar og hvenær sem er í bókahillunni þinni. Engin taska, enginn kyndill og ekkert vesen (hvað þá yfirvigt).
  
  Auðvelt að fletta og leita
  Þú getur flakkað milli síðna og kafla eins og þér hentar best og farið beint í ákveðna kafla úr efnisyfirlitinu. Í leitinni finnur þú orð, kafla eða síður í einum smelli.
  
  Glósur og yfirstrikanir
  Þú getur auðkennt textabrot með mismunandi litum og skrifað glósur að vild í rafbókina. Þú getur jafnvel séð glósur og yfirstrikanir hjá bekkjarsystkinum og kennara ef þeir leyfa það. Allt á einum stað.
  
  Hvað viltu sjá? / Þú ræður hvernig síðan lítur út
  Þú lagar síðuna að þínum þörfum. Stækkaðu eða minnkaðu myndir og texta með multi-level zoom til að sjá síðuna eins og þér hentar best í þínu námi.
  
  
  
  Fleiri góðir kostir
  - Þú getur prentað síður úr bókinni (innan þeirra marka sem útgefandinn setur)
  - Möguleiki á tengingu við annað stafrænt og gagnvirkt efni, svo sem myndbönd eða spurningar úr efninu
  - Auðvelt að afrita og líma efni/texta fyrir t.d. heimaverkefni eða ritgerðir
  - Styður tækni sem hjálpar nemendum með sjón- eða heyrnarskerðingu