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Management Science ~ 
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Ch 1 Introduction |
Ch 7 Integer-Goal Programming |
Ch 13 Simulation |
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Ch 2 Linear Programming |
Ch 8 Network Models |
Ch 14 Fundamentals of Decision Analysis |
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Ch 3 Linear Prog.Application |
Ch 9 Project Management |
Ch 15 Decision Trees, Multifactor decision making. |
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Ch 4 Linear Prog.Simplex |
Ch 10 Inventory Control |
Ch 16 Forecasting |
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Ch 5 Sensitivity Analysis |
Ch 11 Inventory models II |
Ch 17 Markov Analysis |
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Ch 6 Transportation / Assignment |
Ch 12 Waiting Lines: |
Ch 18 Dynamic Programming |
Management
Science 6/96
CSUS
COURSE OM 3000
CHAP 1 INTRODUCTION What is management science.
Application : New York city. Problem.
Tackling the problem.
The Quantitative analysis approach.
Defining the problem.
Developing the model.
Acquiring input data.
Developing a solution.
Testing a solution.
Analyzing the results.
Implementing the results.
OVERVIEW OF THIS BOOK.
Linear programming.
Other mathematical programming topics.
Network models and project management.
Inventory control.
Queuing theory and simulation.
Decision theory.
Forecasting.
Markov Analysis.
Dynamic programming.
Possible problems in the QA approach.
Defining the problem.
APOLLO launches and success example.
Conflicting viewpoints.
Impact on other departments.
Beginning assumptions.
Solution outdated.
DEVELOPING a model fitting the text book models.
Understanding a model.
ACQUIRING input data.
Using accounting data.
Validity of data.
DEVELOPING A SOLUTION. Hard to stand mathematics.
Only one answer is limiting.
TESTING THE SOLUTION.
Analyzing the results.
Implementation- not just the final step.
EXAMPLE : Texas state dept. of human resources.
Lack of commitment and resistance to change.
Lack of commitment by quantitative analysts.
Development of management science within an organization.
Prebirth.
Missionary.
Organizational development.
Sophisticated Projects.
Maturity.
Diffusion.
Management science, decision support systems and artificial intelligence. Management info. Systems.
Decision support systems.
Artificial intelligence and expert systems.
Applications of artificial intelligence.
Benefits of artificial intelligence.
Use of expert systems.
Use of microcomputers in management science.
Use of AB-QM in management science.
FORECASTING MODEL IN AB-QM. Note: equation of line.
QUANTITATIVE
ANALYSIS=
MODEL=
MATHEMATICAL MODEL=
ALGORITHM=
SENSITIVITY ANALYSIS=
DECISION SUPPORT SYSTEM=
ARTIFICIAL INTELLIGENCE.=
CHAPTER 2 LINEAR PROGRAMMING, GRAPHICAL METHODS. INTRODUCTION.
Requirements of a linear programming model.
Basic assumptions of linear programming.
Linear programming beginnings.
Formulating linear programming problems.
Graphical solution to above.
EXAMPLE of selecting tenants in a mall.
ISO PROFIT LINE solution method.
THE CORNER POINT SOLUTION METHOD.
Solving Minimization problems.
EXAMPLE: MANPOWER PLANNING AT UNITED AIRLINES.
EXAMPLE : HOLIDAY Meal Turkey Ranch.
Using the corner point for a solution.
Using the ISO COST APPROACH.
Summary of the graphical solution method.
Special issues in linear programming.
INFEASIBILITY.
UNBOUNDEDNESS.
REDUNDANCY.
ALTERNATIVE OPTIMAL SOLUTIONS.
EXAMPLE: PRODUCTION SCHEDULING WITH LP AT OWENS-CORNING.
TERMS.
LINEAR PROGRAMMING.
MATHEMATICAL PROGRAMMING.
OBJECTIVE FUNCTION.
CONSTRAINT.
INEQUALITY.
PRODUCT MIX PROBLEM.
NON NEGATIVITY CONSTRAINTS.
FEASIBLE REGION.
FEASIBLE SOLUTION.
INFEASIBLE SOLUTION.
ISO-PROFIT LINE.
CORNER POINT OR EXTREME POINT.
CORNER POINT METHOD.
SIMULTANEOUS EQUATION METHOD.
ISO COST LINE.
INFEASIBILITY.
UNBOUNDEDNESS.
REDUNDANCY.
ALTERNATIVE OPTIMAL SOLUTION.
CHAPTER 3 LINEAR PROGRAMMING APPLICATIONS.
INTRODUCTION. EXAMPLES.
MARKETING SELECTION.
MARKETING RESEARCH.
MANUFACTURING APPLICATIONS.
PRODUCTION SCHEDULING.
USING DOUBLE SUB SCRIPTED VARIABLES.
INVENTORY CONSTRAINTS.
EMPLOYEE SCHEDULING APPLICATIONS. ASSIGNMENT PROBLEMS.
LABOR PLANNING.
EXAMPLE: FLIGHT CREW OPTIMIZING.
FINANCIAL APPLICATIONS.
TRANSPORTATION APPLICATIONS. THE SHIPPING PROBLEM.
INGREDIENT BLENDING APPLICATIONS. DIET PROBLEMS.
INGREDIENT MIX AND BLENDING PROBLEMS.
CH 4 IS THE SIMPLEX METHOD WHICH IS MATRICES AND WAS SKIPPED FOR SANITY REASON
INTERESTING NOTES ON THIS CHAPTER.
CHAPTER 5 LINEAR PROGRAMMING: SENSITIVITY ANALYSIS AND DUALITY.
INTRODUCTION.
SENSITIVITY ANALYSIS.
EXAMPLE: HIGH NOTE SOUND COMPANY.
CHANGES IN THE OBJECTIVE FUNCTION COEFFICIENT.
CHANGES IN THE TECHNOLOGICAL COEFFICIENT.
CHANGES IN THE RESOURCES OR RIGHT SIDE VALUES.
RIGHT HAND SIDE RANGING.
THE DUAL IN LINEAR PROGRAMMING.
EXAMPLE: DAIRYMAN’S COOPERATIVE.
DUAL FORMULATION PROCEDURES.
SOLVING THE HIGH NOTE SOUND COMPANY’S PROBLEM.
KEY NOTES.
EXAMPLE: WORD PROCUREMENT IN CABINET MANUFACTURING.
COMPUTATIONAL ADVANTAGE OF THE DUAL.
THE ROLE OF SOFTWARE IN SENSITIVITY ANALYSIS: EX: HIGH NOTE SD CO…
EXAMPLES:
SUMMARY.
TERMS.
SENSITIVITY ANALYSIS.
RANGE OF INSIGNIFICANCE.
RANGE OF OPTIMALITY.
TECHNOLOGICAL COEFFICIENTS.
SHADOW PRICES.
RIGHT HAND SIDE RANGES.
PRIMARY DUAL RELATIONSHIP.
CHAPTER 6 TRANSPORTATION ASSIGNMENT PROBLEMS.
INTRODUCTION.
TRANSPORTATION MODEL.
ASSIGNMENT MODEL.
SPECIAL PURPOSE ALGORITHMS.
SETTING UP A TRANSPORTATION PROBLEM.
DEVELOPING AN INITIAL SOLUTION.: NORTHWEST CORNER RULE.
STEPPING STONE METHOD. FINDING THE LEAST COST SOLUTION..
TESTING THE SOLUTION FOR POSSIBLE IMPROVEMENT.
KEY IDEA.
OBTAINING AN IMPROVED SOLUTION.
KEY IDEAS.
MODI METHODS.
HOW TO USE THE MODI METHOD.~APPROACH.
SOLVING WITH MODI.
KEY NOTE.
VOGEL’S APPROXIMATION METHOD.
ANOTHER WAY TO FIND INITIAL SOLUTION.
UNBALANCED TRANSPORTATION PROBLEMS.
DEMAND LESS THAN SUPPLY.
DEMAND GREATER THAN SUPPLY.
EXAMPLE. NAVAL RECRUITS TO BASES TO THAILAND.
DEGENERACY IN TRANSPORTATION PROBLEMS.
DEGENERACY IN A INITIAL SOLUTION.
DEGENERACY DURING LATER SOLUTION STAGES.
EXAMPLE: MOVING SAND.
MORE THAN ONE OPTIMAL SOLUTION.
COMPUTER SOLUTIONS.
FACILITY LOCATION ANALYSIS.
EXAMPLE. MACHINE COMPANY LOCATION.
APPROACH OF THE ASSIGNMENT MODEL.
THE HUNGARIAN METHOD. FLOODS TECHNIQUE.
KEY NOTE.
STEPS.
DUMMY ROWS AND DUMMY COLUMNS.
EXAMPLE. SCHEDULING AMERICAN LEAGUE UMPIRES WITH ASSIGNMENT.
MAXIMIZING ASSIGNMENT PROBLEMS.
KEY IDEA.
USING THE COMPUTER TO SOLVE ASSIGNMENT PROBLEMS.
TERMS
SKIPPED. SUMMARY.
CASE STUDY.
CASE STUDY.
CASE STUDY.
CHAPTER 7 INTEGER AND GOAL PROGRAMMING.
INTRODUCTION.
INTEGER PROGRAMMING.
EXAMPLE: ELECTRIC COMPANY.
THE CUTTING PLANE METHOD.
KEY IDEA GET THISSSSSSSSSSSSSSSSSSSSSSSS.
TYPES OF INTEGER PROBLEMS.
A MIXED INTEGER PROGRAMMING PROBLEM. EXAMPLE.
EXAMPLE. AIRPLANE AIRLINES.
0 DASH 1 INTEGER PROBLEM EXAMPLE.
EXAMPLE. LOCKHEED WAREHOUSE.
THE BRANCH AND BOUND METHOD.
EXAMPLE . DECISION SUPPORT SYSTEMS. ASSIGNING CLASSES.
EXAMPLE. B.
SOLVING AN INTEGER PROGRAMMING PROBLEM WITH BRANCH AND BOUND.
KEY IDEA.
EXAMPLE FOLLOW UP ELECTRIC COMPANY.
SUB PROBLEMS.
GOAL PROGRAMMING.
EXAMPLE WITH ASSIGNING SALES AGENTS.
AN EXTENSION TO EQUALLY IMPORTANT MULTIPLE GOALS.
RANKING GOALS.
SOLVING GOAL PROGRAMMING GRAPHICALLY.
USING AB-QM TO SOLVE.
NONLINEAR PROGRAMMING.
A NONLINEAR OBJECTIVE FUNCTION.
EXAMPLE: CITY CAPITAL EXPENDITURES.
OTHER NONLINEAR PROBLEMS.
KEY IDEA.
TERMS.
INTEGER PROGRAMMING.
CUTTING PLANE METHOD.
ZERO ONE INTEGER PROGRAMMING.
BRANCH AND BOUND METHOD.
GOAL PROGRAMMING.
SATISFICING.
DEVIATIONAL VARIABLES.
NONLINEAR PROGRAMMING.
CHAPTER 8 NETWORK MODELS. INTRODUCTION.
MINIMAL SPANNING TREE TECHNIQUE.
MAXIMAL FLOW TECHNIQUE.
KEY IDEA.
SHORTEST
ROUTE TECHNIQUE.
USING THE COMPUTER TO SOLVE NETWORK PROBLEMS.
USING AB-QM TO SOLVE NETWORK PROBLEMS.
USING STORM’S DISTANCE NETWORK MODEL.
SUMMARY.
TERMS. MINIMAL SPANNING TREE TECHNIQUE.
MAXIMAL FLOW TECHNIQUE.
SHORTEST ROUTE TECHNIQUE.
AB-QM SOLVED EXAMPLE.
AB-QM SOLVED EXAMPLE # 2.
CHAPTER 9 PROJECT MANAGEMENT INTRODUCTION.
FRAMEWORK OF PERT AND CPM.
PERT.
EXAMPLE OF PERT.
DRAWING THE PERT NETWORK.
ACTIVITY TIMES.
OPTIMISTIC.
PESSIMISTIC.
MOST LIKELY.
HOW TO FIND THE CRITICAL PATH.
KEY IDEA.
EXAMPLE AIRLINE SCHEDULING.
CONCEPT OF SLACK IN CRITICAL PATH COMPUTATIONS.
KEY IDEA.
PROBABILITY OF PROJECT COMPLETION.
WHAT PERT WAS ABLE TO PROVIDE.
DUMMY ACTIVITIES IN PERT.
PERT / COST.
PLANNING AND SCHEDULING PROJECT COST: THE BUDGETING PROCESS.
USING PERT IN BRITISH AIRWAYS. EXAMPLE.
DEVELOPING WEEKLY BUDGET.
BUDGETING WITH LATEST START TIMES.
MONITORING AND CONTROLLING PROJECT COSTS.
CRITICAL PATH METHOD.
PROJECT CRASHING WITH CPM.
KEY IDEA.
PROJECTING WITH LINEAR PROGRAMMING.
OBJECTIVE FUNCTION.
CRASH TIME CONSTRAINTS.
PROJECT COMPLETION CONSTRAINT.
CONSTRAINTS DESCRIBING THE NETWORK.
USING THE COMPUTER TO SOLVE NETWORK PROBLEMS.
USING AB-QM TO SOLVE PERT AND CPM.
USING STORM FOR PROJECT MANAGEMENT.
COMMERCIAL SOFTWARE FOR PROJECT MANAGEMENT.
SUMMARY.
TERMS
PERT.
ACTIVITY.
EVENT.
IMMEDIATE PREDECESSOR.
NETWORK.
ACTIVITY TIME ESTIMATES.
OPTIMISTIC TIME.
PESSIMISTIC TIME.
MOST LIKELY TIME.
BETA PROBABILITY DISTRIBUTION.
EXPECTED ACTIVITY TIME.
VARIANCE OF ACTIVITY COMPLETION TIME.
EARLIEST START TIME.
EARLIEST FINISH TIME.
LATEST START TIME.
LATEST FINISH TIME.
FORWARD PASS.
BACKWARD PASS.
SLACK TIME.
CRITICAL PATH.
CRITICAL PATH ANALYSIS.
DUMMY PATH ACTIVITY.
PERT/COST.
CPM.
CRASHING.
PICK AN EXAMPLE ANY EXAMPLE.
CHAP 10 INVENTORY MODELS. INTRODUCTION.
IMPORTANCE OF INVENTORY CONTROL.
THE DECOUPLING FUNCTION.
STORING RESOURCES.
A HEDGE AGAINST INFLATION.
IRREGULAR SUPPLY AND DEMAND.
QUANTITY DISCOUNTS.
AVOIDING STOCKOUTS AND SHORTAGES.
THE INVENTORY DECISION.
KEY IDEA.
ECONOMIC ORDER QUANTITY. EOQ. ~ HOW MUCH TO ORDER.
INVENTORY COST.
FINDING THE ECONOMIC ORDER QUANTITY.
PURCHASING COST OF INVENTORY ITEMS.
REORDER POINT. ROP. DETERMINING WHEN TO ORDER.
KEY IDEA.
FIXED PERIOD INVENTORY CONTROL SYSTEM.
DETERMINING THE OPTIMAL NUMBER OF ORDERS PER YEAR.
EXAMPLE. INLAND STEEL.
DETERMINING THE OPTIMAL NUMBER OF DAYS BETWEEN ORDERS.
SENSITIVITY ANALYSIS.
SUMMARY.
TERMS.
EOQ.
AVERAGE INVENTORY.
REORDER POINT.
LEAD TIME.
SENSITIVITY ANALYSIS.
SOLVED PROBLEM.
CHAPTER 11 INVENTORY CONTROL MODELS II. INTRODUCTION.
EOQ WITHOUT THE INSTANTANEOUS RECEIPT ASSUMPTION.
DETERMINING THE ANNUAL CARRYING COST.
FINDING THE ANNUAL ORDERING COST.
DETERMINING THE OPTIMAL ORDER QUANTITY AND PRODUCTION QUANTITY.
BROWN MANUFACTURING EXAMPLE.
QUANTITY DISCOUNT MODELS.
PLANNED SHORTAGES.
FINDING OPTIMAL ORDER AND BACK ORDER LEVELS.
Planned shortages example.
USE OF SAFETY STOCK.
SAFETY STOCK WITH KNOWN STOCKOUT COSTS.
SAFETY STOCK WITH UNKNOWN STOCKOUT COSTS.
ABC ANALYSIS AND JOINT ORDERING.
ABC ANALYSIS.
EXAMPLE ABC CONTROLS AND ANALYSIS IN HOSPITALS.
JOINT ORDERING.
DEPENDENT DEMAND.. THE CASE FOR MATERIAL REQUIREMENTS PLANNING. MRP.
EXAMPLE AT GENERAL MOTORS.
THE MATERIAL STRUCTURE TREE.
GROSS AND NET REQUIREMENTS PLAN.
TWO OR MORE END PRODUCTS.
THE KANBAN SYSTEM.
USING JUST IN TIME INVENTORY. EXAMPLE.
USING THE COMPUTER TO SOLVE INVENTORY CONTROL PROBLEMS.
AB-QM IN INVENTORY CONTROL. EXAMPLE.
AB-QM IN QUANTITY DISCOUNT.
AB-QM EXAMPLE IN ABC ANALYSIS.
USE OF STORM IN INVENTORY ANALYSIS.
SUMMARY.
TERMS.
INSTANTANEOUS INVENTORY RECEIPT.
PRODUCTION MODEL RUN.
ANNUAL SETUP COST ~ ORDER COST YEARLY.
QUANTITY DISCOUNT.
PLANNED SHORTAGES.
BACK ORDERING COST PER UNIT PER YEAR.
SAFETY STOCK.
STOCKOUT.
SAFETY STOCK WITH KNOWN STOCKOUT COSTS.
SAFETY STOCK WITH UNKNOWN STOCKOUT COSTS.
SERVICE LEVEL.
ABC ANALYSIS.
JOINT ORDERING.
MATERIAL REQUIREMENTS PLANNING.
JUST IN TIME INVENTORY. JIT.
KANBAN.
SOLVED PROBLEMS.
SOLVED PROBLEM # 2.
SOLVED PROBLEM # 3.
CHAPTER 12 QUEUING THEORY. INTRODUCTION.
WAITING LINE COSTS.
KEY IDEA.
EXAMPLE: THREE RIVERS SHIPPING COMPANY.
CHARACTERISTICS OF A QUEUING SYSTEM.
ARRIVAL CHARACTERISTICS.
SIZE OF THE CALLING POPULATION.
PATTERN ARRIVALS AT THE SYSTEM.
BEHAVIOR OF THE ARRIVALS.
WAITING LINE CHARACTERISTICS.
SERVICE FACILITY CHARACTERISTICS.
BASIC QUEUING SYSTEM CONFIGURATIONS.
EXAMPLE QUEUING AT KODAK.
SERVICE TIME DISTRIBUTION.
FOUR BASIC STYLES OF QUEUING CONFIGURATIONS.
SINGLE CHANNEL QUEUING MODEL WITH POISSON ARRIVALS AND EXPONENTIAL SERVICE TIMES.
ASSUMPTIONS OF THE MODEL.
QUEUING EQUATIONS.
EXAMPLE. MUFFLER SHOP CASE.
MULTIPLE CHANNEL QUEUING MODEL WITH POISSON ARRIVALS AND EXPONENTIAL SERVICE TIMES.
EQUATIONS FOR THE MULTI CHANNEL QUEUING MODEL.
ARNOLD’S MUFFLER SHOP AGAIN.
USE OF WAITING LINE TABLES.
CONSTANT SERVICE TIME MODULE
EQUATION FOR THE CONSTANT SERVICE TIME MODEL.
EXAMPLE RECYCLING.
MORE COMPLEX QUEUING MODELS AND THE USE OF SIMULATION.
SUMMARY.
TERMS.
WAITING LINE.
QUEUING THEORY.
SERVICE COST.
WAITING COST.
OPERATING CHARACTERICS.
CALLING POPULATION.
UNLIMITED OR INFINITE POPULATION.
LIMITED OR FINITE POPULATION.
POISSON DISTRIBUTION.
BALKING.
RENEGING.
LIMITED QUEUE LENGTH.
UNLIMITED QUEUE LENGTH.
QUEUE
DISCIPLINE.
FIFO.
SINGLE CHANNEL QUEUING MODEL.
MULTIPLE QUEUING CHANNEL SYSTEM.
SINGLE PHASE SYSTEM.
MULTIPHASE SYSTEM.
NEGATIVE EXPONENTIAL PROBABILITY DISTRIBUTION.
M/M/1.
UTILIZATION FACTOR.
M/M/m.
WAITING LINE TABLES.
M/D/1.
COMPUTER SIMULATION.
KEY EQUATIONS.
SOLVED PROBLEM # 1
BLEM # 2
SOLVED PROBLEM # 3
CASE STUDY 1
CASE STUDY 2.
CHAPTER 13 SIMULATION. INTRODUCTION.
KEY IDEA.
ADVANTAGES AND DISADVANTAGES OF SIMULATION.
MONTE CARLO SIMULATION.
STEP 1 ESTABLISHING DISTRIBUTIONS.
STEP 2 BUILDING A CUMULATIVE PROBABILITY DISTRIBUTION FOR EACH VARIABLE.
STEP 3 SETTING RANDOM NUMBER INTERVALS.
STEP 4 GENERATING RANDOM NUMBERS.
SIMULATION THE EXPERIMENT.
SIMULATION AND INVENTORY ANALYSIS.
THE HARDWARE STORE AGAIN.
ANALYZING THE HARDWARE’S COSTS.
A COMPUTER SIMULATION FOR THE HARDWARE STORE WITH AB-QM.
SIMULATION OF A QUEUING PROBLEM.
EXAMPLE PORT OF NEW ORLEANS.
SIMULATION MODEL FOR A MAINTENANCE POLICY.
EXAMPLE POWER COMPANY.
EXAMPLE RAILWAYS.
COST ANALYSIS OF THE SIMULATION.
EXAMPLE: COLLEGE COMPUTER USAGE WITH SIMULATION.
TWO OTHER TYPES OF SIMULATION MODELS.
OPERATIONAL GAMING.
SYSTEM SIMULATION.
ROLE OF COMPUTERS IN SIMULATION.
SUMMARY.
TERMS.
SIMULATION.
MONTE CARLO SIMULATION.
RANDOM NUMBER.
RANDOM NUMBER INTERVAL.
FLOW DIAGRAM OR FLOWCHART.
OPERATION GAMING.
SYSTEM SIMULATION.
GENERAL PURPOSE LANGUAGE.
SPECIAL PURPOSE SIMULATION LANGUAGES.
SOLVED PROBLEMS. #1
SOLVED PROBLEM # 2
CHAPTER 14 FUNDAMENTALS OF DECISION ANALYSIS. INTRODUCTION.
THE SIX STEPS IN DECISION ANALYSIS.
TYPES OF DECISION MAKING ENVIRONMENTS.
DECISION MAKING UNDER RISK.
EXPECTED MONETARY VALUE.
KEY IDEA.
EXPECTED VALUE OF PERFECT INFORMATION.
OPPORTUNITY LOSS.
DECISION MAKING UNDER UNCERTAINTY.
MAXIMAX.
MAXIMIN.
EXAMPLE WEATHER SERVICE.
EQUALLY LIKELY (LAPLACE)
CRITERION OR REALISM (HURWICZ CRITERION)
MINIMAX
EXAMPLE FOREST MANAGEMENT.
USING THE COMPUTER TO SOLVE THE DECISION THEORY PROBLEMS.
SUMMARY.
TERMS.
ALTERNATIVE.
CONDITIONAL VALUE OR PAYOFF.
DECISION MAKING UNDER CERTAINTY.
DECISION MAKING UNDER UNCERTAINTY.
EXPECTED MONETARY VALUE.
EXPECTED VALUE OF PERFECT INFORMATION.
EXPECTED VALUE WITH PERFECT INFORMATION.
EXPECTED OPPORTUNITY LOSS.
MAXIMAX.
EQUALLY LIKELY.
COEFFICIENT OF REALISM.
MINIMAX.
SOLVED PROBLEM # 1.
SOLVED PROBLEM # 2.
CHAPTER 15.
DECISION TREES, UTILITY THEORY, AND MULTI FACTOR DECISION MAKING. INTRODUCTION.
REMEMBER TO ADD KEY EQUATIONS.
DECISION TREES.
EXPECTED VALUE OF SAMPLE INFORMATION.
HOW PROBABILITY VALUES ARE ESTIMATED BY BAYESIAN ANALYSIS.
EXAMPLE. USING DECISION TREES IN THE NAVY.
CALCULATING REVISED PROBABILITIES.
EXAMPLE: DECISION TREES IN SELECTING DRILLING SITES.
A POTENTIAL PROBLEM IN USING SURVEY RESULTS.
UTILITY THEORY.
MEASURING UTILITY AND CONSTRUCTING A UTILITY CURVE.
KEY IDEA.
KEY IDEA.
UTILITY AS A DECISION MAKING CRITERION.
MULTI-FACTOR DECISION MAKING.
THE MULTIFACTOR EVALUATION PROCESS.
THE ANALYTIC HIERARCHY PROCESS.
A COMPARISON OF MFEP AND AHP.
USE OF MICROCOMPUTERS IN DECISION THEORY.
USING AB-QM.
USE OF EXPERT CHOICE FOR AHP.
USE OF SUPER TREE FOR DECISION TREE ANALYSIS.
SUMMARY.
TERMS.
SEQUENTIAL DECISIONS.
UTILITY THEORY.
UTILITY ASSESSMENT.
UTILITY CURVE.
RISK AVOIDER.
RISK SEEKER.
MULTIFACTOR DECISION MAKING.
MULTIFACTOR EVALUATION PROCESS (MFEP).
ANALYTIC HIERARCHY PROCESS. (AHP).
FACTOR EVALUATIONS.
FACTOR WEIGHTS.
KEY EQUATIONS.
SOLVED PROBLEM 1
SOLVED PROBLEM 2
SOLVED PROBLEM 3
CASE STUDY.
CHAP 16 FORECASTING INTRODUCTION.
TYPES OF FORECASTS.
TIME SERIES MODELS.
CASUAL MODELS.
JUDGMENTAL MODELS.
SCATTER MODELS.
TIME SERIES FORECASTING MODELS.
DECOMPOSITION OF A TIME SERIES.
MOVING AVERAGES.
EXPONENTIAL SMOOTHING.
SELECTING THE SMOOTHING CONSTANT.
EXPONENTIAL SMOOTHING WITH TREND ADJUSTMENT
TREND PROJECTIONS.
EXAMPLE. NEW YORK SANITATION DEPARTMENT.
CASUAL FORECASTING METHODS.
USING REGRESSION ANALYSIS TO FORECAST.
STANDARD ERROR OF THE ESTIMATE.
CORRELATION COEFFICIENTS FOR REGRESSION LINES.
MULTIPLE REGRESSION ANALYSIS.
EXAMPLE. DECISION SUPPORT FOR SCHEDULING EVENTS.
MONITORING AND CONTROLLING FORECASTS.
ADAPTING SMOOTHING.
THE DELPHI TECHNIQUE.
USING THE COMPUTER TO FORECAST.
AB-QM SAMPLE COMPUTER RUNS.
STAPLE AB-QM PRINTOUTS HERE.
SUMMARY.
TERMS.
TIME SERIES MODELS.
CASUAL MODELS.
JUDGMENTAL MODELS.
SCATTER DIAGRAMS.
MOVING AVERAGE.
WEIGHTED MOVING AVERAGE.
EXPONENTIAL SMOOTHING.
SMOOTHING CONSTANT.
MEAN ABSOLUTE DEVIATION. (MAD)
MEAN SQUARED ERROR. (MSE)
MEAN ABSOLUTE PERCENT ERROR. (MAPE).
BIAS.
LEAST SQUARES.
REGRESSION ANALYSIS.
STANDARD ERROR OF THE ESTIMATE.
CORRELATION COEFFICIENT.
TRACKING SIGNAL.
DELPHI.
DECISION MAKING GROUP.
KEY EQUATIONS.
SOLVED PROBLEM 1
SOLVED PROBLEM 2
CASE STUDY. AIRLINES.
CHAP 17 MARKOV ANALYSIS. INTRODUCTION
STATES AND STATE PROBABILITIES: GROCERY STORE EXAMPLE.
MATRIX OF TRANSITION PROBABILITIES.
TRANSITION PROBABILITIES FOR THE THREE GROCERY STORES.
PREDICTING FUTURE MARKET SHARES.
MARKOV ANALYSIS OF MACHINE OPERATIONS.
EXAMPLE. USING MARKOV ANALYSIS FOR PAVEMENT MANAGEMENT.
EQUILIBRIUM CONDITIONS.
ABSORBING STATES AND THE FUNDAMENTAL MATRIX. EXAMPLE
ACCOUNTS RECEIVABLE.
EXAMPLE. MARKOV ANALYSIS. LONG TERM CARE.
USING THE COMPUTER FOR MARKOV ANALYSIS.
SUMMARY.
TERMS.
MARKOV ANALYSIS.
STATE PROBABILITY.
VECTOR OF STATE PROBABILITIES.
MARKET SHARE.
KEY EQUATIONS.
SOLVED PROBLEM 1
SOLVED PROBLEM 2.
CASE STUDY EXAMPLE SUGAR MILL.
CASE STUDY EXAMPLE BURGER CHAIN.
CHAPTER 18 DYNAMIC PROGRAMMING INTRODUCTION.
A SHORTEST ROUTE EXAMPLE SOLVED BY DYNAMIC PROGRAMMING.
EXAMPLE DYNAMIC PROGRAMMING AT WEYERHAEUSER.
EXAMPLE DYNAMIC PROGRAMMING IN SPORTS.
DYNAMIC PROGRAMMING TERMINOLOGY.
EXAMPLE SEASONAL STAFFING PROBLEM SOLVED BY DYNAMIC
PROGRAMMING.
DYNAMIC PROGRAMMING NOTATION.
THE KNAPSACK PROBLEM.
TYPES OF KNAPSACK PROBLEMS.
ROLLERS AIR TRANSPORT PROBLEM
TRANSFORMATION AND RETURN FUNCTIONS.
STAGE BY STAGE SOLUTION.
THE PRODUCTION PROBLEM.
OVERVIEW OF THE PRODUCTION PROBLEM.
PRODUCTION PROBLEM FOR MELISSA GAINY.
USING THE COMPUTER TO SOLVE DYNAMIC PROGRAMMING PROBLEMS.
USING AB-QM.
PRINT OUTS WILL GO HERE.
USING AB-QM TO SOLVE NON NETWORK DYNAMIC PROGRAMMING PROBLEMS.
SUMMARY.
TERMS.
DYNAMIC PROGRAMMING.
STAGE.
STATE VARIABLE.
DECISION VARIABLE.
OPTIMAL POLICY.
TRANSFORMATION.
KEY EQUATIONS.
SOLVED PROBLEM 1
SOLVED PROBLEM 2
USING COMPUTERS TO SOLVE GAME THEORY PROBLEMS.
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BACK TO 100 examples in Business, Operations and
Engineering. |
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Do it once, do it right, and do it now.
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Back to Lawson Computing Homepage
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Apply as needed,
when needed. |
