Verfügbarkeit: Spreadsheet modeling and decision analysis

Spreadsheet modeling and decision analysis: a practical introduction to management science

Gespeichert in:

Bibliographische Detailangaben
1. Verfasser:	Ragsdale, Cliff T. (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Cincinnati, Ohio South Western College Publ. 2001
Ausgabe:	3. ed.
Schlagworte:	Decision making > Computer simulation Decision support systems > Computer programs Electronic spreadsheets Management science > Computer simulation Operations research > Computer simulation Tabellenkalkulation Management Operations Research Computersimulation
Online-Zugang:	Inhaltsverzeichnis
Beschreibung:	XXI, 794 S. 2 CD-ROM
ISBN:	0324021224

Internformat

MARC


LEADER	00000nam a2200000 c 4500
001	BV013345540
003	DE-604
005	00000000000000.0
007	t
008	000915s2001 \|\|\|\| 00\|\|\| eng d
020			\|a 0324021224 \|9 0-324-02122-4
035			\|a (OCoLC)247045217
035			\|a (DE-599)BVBBV013345540
040			\|a DE-604 \|b ger \|e rakwb
041	0		\|a eng
049			\|a DE-91G \|a DE-945
050		0	\|a T57.62
082	0		\|a 658.403028553
084			\|a QH 444 \|0 (DE-625)141591: \|2 rvk
084			\|a WIR 522f \|2 stub
084			\|a MAT 900f \|2 stub
100	1		\|a Ragsdale, Cliff T. \|e Verfasser \|4 aut
245	1	0	\|a Spreadsheet modeling and decision analysis \|b a practical introduction to management science \|c Cliff T. Ragsdale
250			\|a 3. ed.
264		1	\|a Cincinnati, Ohio \|b South Western College Publ. \|c 2001
300			\|a XXI, 794 S. \|e 2 CD-ROM
336			\|b txt \|2 rdacontent
337			\|b n \|2 rdamedia
338			\|b nc \|2 rdacarrier
650		4	\|a Decision making \|x Computer simulation
650		4	\|a Decision support systems \|x Computer programs
650		4	\|a Electronic spreadsheets
650		4	\|a Management science \|x Computer simulation
650		4	\|a Operations research \|x Computer simulation
650	0	7	\|a Tabellenkalkulation \|0 (DE-588)4117161-5 \|2 gnd \|9 rswk-swf
650	0	7	\|a Management \|0 (DE-588)4037278-9 \|2 gnd \|9 rswk-swf
650	0	7	\|a Operations Research \|0 (DE-588)4043586-6 \|2 gnd \|9 rswk-swf
650	0	7	\|a Computersimulation \|0 (DE-588)4148259-1 \|2 gnd \|9 rswk-swf
689	0	0	\|a Computersimulation \|0 (DE-588)4148259-1 \|D s
689	0	1	\|a Management \|0 (DE-588)4037278-9 \|D s
689	0		\|5 DE-604
689	1	0	\|a Computersimulation \|0 (DE-588)4148259-1 \|D s
689	1	1	\|a Operations Research \|0 (DE-588)4043586-6 \|D s
689	1		\|5 DE-604
689	2	0	\|a Tabellenkalkulation \|0 (DE-588)4117161-5 \|D s
689	2	1	\|a Operations Research \|0 (DE-588)4043586-6 \|D s
689	2		\|8 1\p \|5 DE-604
856	4	2	\|m HBZ Datenaustausch \|q application/pdf \|u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009101344&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA \|3 Inhaltsverzeichnis
999			\|a oai:aleph.bib-bvb.de:BVB01-009101344
883	1		\|8 1\p \|a cgwrk \|d 20201028 \|q DE-101 \|u https://d-nb.info/provenance/plan#cgwrk

Datensatz im Suchindex

_version_	1804128120497766400
adam_text	Brief Contents 1 Introduction to Modeling and Decision Analysis 1 2 Introduction to Optimization and Linear Programming 16 3 Modeling and Solving LP Problems in a Spreadsheet 44 4 Sensitivity Analysis and the Simplex Method 137 5 Network Modeling 177 6 Integer Linear Programming 230 7 Goal Programming and Multiple Objective Optimization 297 8 Nonlinear Programming Evolutionary Optimization 336 9 Regression Analysis 407 10 Discriminant Analysis 459 11 Time Series Forecasting 488 12 Introduction to Simulation Using Crystal Ball 560 13 Queuing Theory 628 14 Project Management 661 15 Decision Analysis 713 Index 789 vii Contents 1. Introduction to Modeling and Decision Analysis 1 Introduction 1 The Modeling Approach to Decision Making 2 Characteristics and Benefits of Modeling 4 Mathematical Models 5 Categories of Mathematical Models 6 The Problem Solving Process 8 Good Decisions vs. Good Outcomes 11 The World of Management Science 12 Summary 14 References 14 Questions and Problems 14 2. Introduction to Optimization and Linear Programming 16 Introduction 16 Applications of Mathematical Optimization 16 Characteristics of Optimization Problems 17 Expressing Optimization Problems Mathematically 18 Decisions 18 Constraints 18 Objective 19 Mathematical Programming Techniques 20 ix x Contents An Example LP Problem 20 Formulating LP Models 21 Steps in Formulating an LP Model 21 Summary of the LP Model for the Example Problem 22 The General Form of an LP Model 23 Solving LP Problems: An Intuitive Approach 24 Solving LP Problems: A Graphical Approach 25 Plotting the First Constraint 25 Plotting the Second Constraint 26 Plotting the Third Constraint 28 The Feasible Region 29 Plotting the Objective Function 29 Finding the Optimal Solution Using Level Curves 31 Finding the Optimal Solution by Enumerating the Corner Points 32 Summary of Graphical Solution to LP Problems 32 Special Conditions in LP Models 33 Alternate Optimal Solutions 34 Redundant Constraints 34 Unbounded Solutions 36 ^feasibility 38 Summary 39 References 39 Questions and Problems 40 3. Modeling and Solving LP Problems in a Spreadsheet 44 Introduction 44 Spreadsheet Solvers 45 Solving LP Problems in a Spreadsheet 45 The Steps in Implementing an LP Model in a Spreadsheet 46 A Spreadsheet Model for the Blue Ridge Hot Tubs Problem 47 Organizing the Data 47 Representing the Decisions Variables 48 Representing the Objective Function 48 Representing the Constraints 49 Representing the Bounds on the Decision Variables 50 How Solver Views the Model 51 Using Solver 53 Denning the Set (or Target) Cell 55 Defining the Variable Cells 56 Defining the Constraint Cells 57 Defining the Nonnegativity Conditions 58 Reviewing the Model 59 Options 59 Solving the Model 59 Goals and Guidelines for Spreadsheet Design 61 Make vs. Buy Decisions 63 Defining the Decision Variables 63 Defining the Objective Function 64 Defining the Constraints 64 Implementing the Model 64 Solving the Model 66 Analyzing the Solution 66 An Investment Problem 68 Defining the Decision Variables 69 Denning the Objective Function 69 Defining the Constraints 69 Implementing the Model 70 Solving the Model 71 Analyzing the Solution 72 Contents xi A Transportation Problem 73 Defining the Decision Variables 73 Defining the Objective Function 74 Defining the Constraints 74 Implementing the Model 75 Heuristic Solution for the Model 77 Solving the Model 78 Analyzing the Solution 78 A Blending Problem 79 Defining the Decision Variables 80 Defining the Objective Function 80 Defining the Constraints 80 Some Observations About Constraints, Reporting, and Scaling 81 Re scaling the Model 82 Implementing the Model 83 Solving the Model 85 Analyzing the Solution 85 A Production and Inventory Planning Problem 86 Defining the Decision Variables 87 Defining the Objective Function 87 Defining the Constraints 88 Implementing the Model 89 Solving the Model 91 Analyzing the Solution 91 A Multi Period Cash Flow Problem 92 Defining the Decision Variables 93 Defining the Objective Function 94 Defining the Constraints 94 Implementing the Model 96 Solving the Model 99 Analyzing the Solution 100 Modifying the Taco Viva Problem to Account for Risk (Optional) 100 Implementing the Risk Constraints 102 Solving the Model 104 Analyzing the Solution 105 Data Envelopment Analysis (DEA) 105 Defining the Decision Variables 106 Defining the Objective 106 Defining the Constraints 106 Implementing the Model 107 Solving the Model 109 Analyzing the Solution 114 Summary 116 References 116 The World of Management Science 116 Questions and Problems 117 Cases 133 4. Sensitivity Analysis and the Simplex Method 137 Introduction 137 The Purpose of Sensitivity Analysis 137 Approaches to Sensitivity Analysis 138 An Example Problem 139 The Answer Report 140 The Sensitivity Report 141 Changes in the Objective Function Coefficients 141 A Note About Constancy 142 Alternate Optimal Solutions 143 Changes in the RHS Values 144 Shadow Prices for Nonbinding Constraints 144 A Note About Shadow Prices 145 Shadow Prices and the Value of Additional Resources 147 Other Uses of Shadow Prices 147 The Meaning of the Reduced Costs 149 Analyzing Changes in Constraint Coefficients 151 Simultaneous Changes in Objective Function Coefficients 152 A Warning About Degeneracy 153 The Limits Report 153 xii Contents The Sensitivity Assistant Add in (Optional) 154 Creating Spider Tables and Plots 155 Creating a Solver Table 158 Comments 161 The Simplex Method (Optional) 161 Creating Equality Constraints Using Slack Variables 161 Basic Feasible Solutions 162 Finding the Best Solution 164 Summary 165 References 165 The World of Management Science 166 Questions and Problems 167 Case 174 5. Network Modeling 177 Introduction 177 The Transshipment Problem 177 Characteristics of Network How Problems 178 The Decision Variables for Network Flow Problems 179 The Objective Function for Network Row Problems 180 The Constraint for Network Flow Problems 180 Implementing the Model in a Spreadsheet 182 Analyzing the Solution 183 The Shortest Path Problem 185 An LP Model for the Example Problem 186 The Spreadsheet Model and Solution 187 Network Flow Models and Integer Solutions 189 The Equipment Replacement Problem 189 The Spreadsheet Model and Solution 191 Transportation/Assignment Problems 193 Generalized Network Flow Problems 195 Formulating an LP Model for the Recycling Problem 196 Implementing the Model 197 Analyzing the Solution 199 Maximal Flow Problems 201 An Example of a Maximal Row Problem 201 The Spreadsheet Model and Solution 203 Special Modeling Considerations 205 Minimal Spanning Tree Problems 206 An Algorithm for the Minimal Spanning Tree Problem 208 Solving the Example Problem 208 Summary 209 References 210 The World of Management Science 210 Questions and Problems 211 Cases 224 i Contents xiii 6. Integer Linear Programming 230 Introduction 230 Integrality Conditions 230 Relaxation 231 Solving the Relaxed Problem 232 Bounds 234 Rounding 235 Stopping Rules 237 Solving IIP Problems Using Solver 239 Other ILP Problems 241 An Employee Scheduling Problem 243 Defining the Decision Variables 244 Defining the Objective Function 244 Defining the Constraints 244 A Note About the Constraints 245 Implementing the Model 246 Solving the Model 246 Analyzing the Solution 247 Binary Variables 248 A Capital Budgeting Problem 248 Defining the Decision Variables 249 Defining the Objective Function 249 Defining the Constraints 250 Setting Up the Binary Variables 250 Implementing the Model 250 Solving the Model 251 Comparing the Optimal Solution to a Heuristic Solution 252 Binary Variables and Logical Conditions 254 The Fixed Charge Problem 254 Defining the Decision Variables 255 Defining the Objective Function 256 Defining the Constraints 256 Determining Values for Big M 257 Implementing the Model 257 Solving the Model 259 Analyzing the Solution 259 Minimum Order/Purchase Size 262 Quantity Discounts 262 Formulating the Model 263 The Missing Constraints 263 A Contract Award Problem 264 Formulating the Model: The Objective Function and Transportation Constraints 265 Implementing the Transportation Constraints 265 Formulating the Model: The Side Constraints 266 Implementing the Side Constraints 267 Solving the Model 269 Analyzing the Solution 269 The Branch and Bound Algorithm (Optional) 270 Branching 271 Bounding 273 Branching Again 274 Bounding Again 274 Summary of B B Example 275 Summary 276 References 278 The World of Management Science 278 Questions and Problems 279 Cases 291 I xiv Contents 7. Goal Programming and Multiple Objective Optimization 297 Introduction 297 Goal Programming 298 A Goal Programming Example 298 Defining the Decision Variables 299 Defining the Goals 299 Defining the Goal Constraints 299 Defining the Hard Constraints 300 GP Objective Functions 301 Defining the Objective 302 Implementing the Model 303 Solving the Model 305 Analyzing the Solution 305 Revising the Model 305 Trade offs: The Nature of GP 307 Comments About Goal Programming 308 Multiple Objective Optimization 309 An MOLP Example 310 Defining the Decision Variables 310 Defining the Objectives 311 Defining the Constraints 311 Implementing the Model 311 Determining Target Values for the Objectives 312 Summarizing the Target Solutions 315 Determining a GP Objective 316 The MINIMAX Objective 317 Implementing the Revised Model 318 Solving the Model 319 Comments on MOLP 321 Summary 322 References 323 The World of Management Science 323 Questions and Problems 324 Case 334 8. Nonlinear Programming Evolutionary Optimization 336 Introduction 336 The Nature of NLP Problems 337 Solution Strategies for NLP Problems 338 Local vs. Global Optimal Solutions 339 Economic Order Quantity Models 342 Implementing the Model 344 Solving the Model 345 Analyzing the Solution 345 Comments on the EOQ Model 347 Location Problems 347 Defining the Decision Variables 349 Denning the Objective 349 Defining the Constraints 349 Implementing the Model 350 Solving the Model and Analyzing the Solution 350 Another Solution to the Problem 352 Some Comments About the Solution to Location Problems 353 Nonlinear Network Flow Problem 353 Defining the Decision Variables 354 Denning the Objective 355 Defining the Constraints 355 Implementing the Model 356 Solving the Model and Analyzing the Solution 356 Contents xv Project Selection Problems 358 Defining the Decision Variables 360 Defining the Objective Function 360 Defining the Constraints 360 Implementing the Model 361 Solving the Model 363 Optimizing Existing Financial Spreadsheet Models 364 Implementing the Model 365 Optimizing the Spreadsheet Model 366 Analyzing the Solution 366 Comments on Optimizing Existing Spreadsheets 367 The Portfolio Selection Problem 368 Defining the Decision Variables 369 Defining the Objective 370 Defining the Constraints 370 Implementing the Model 371 Analyzing the Solution 373 Handling Conflicting Objectives in Portfolio Problems 374 Sensitivity Analysis 376 Lagrange Multipliers 379 Reduced Gradients 379 Solver Options for Solving NLPs 380 Evolutionary Algorithms 381 Beating the Market 383 A Spreadsheet Model for the Problem 383 Solving the Model 383 Analyzing the Solution 384 The Traveling Salesperson Problem 385 A Spreadsheet Model for the Problem 386 Solving the Model 388 Analyzing the Solution 388 Summary 389 References 390 The World of Management Science 390 Questions and Problems 391 Cases 404 9. Regression Analysis 407 Introduction 407 An Example 407 Regression Models 410 Simple Linear Regression Analysis 411 Defining Best Fit 412 Solving the Problem Using Solver 413 Solving the Problem Using the Regression Tool 415 Evaluating the Fit 417 The R2 Statistic 420 Making Predictions 422 The Standard Error 422 Prediction Intervals for the New Values of Y 422 Confidence Intervals for Mean Values of Y 425 A Note About Extrapolation 425 Statistical Tests for Population Parameters 426 Analysis of Variance 426 Assumptions for the Statistical Tests 427 A Note About Statistical Tests 429 1 xvi Contents Introduction to Multiple Regression 429 A Multiple Regression Example 431 Selecting the Model 431 Models with One Independent Variable 433 Models with Two Independent Variables 434 Inflating R2 436 The Adjusted R2 Statistic 436 The Best Model with Two Independent Variables 437 Multicollinearity 437 The Model with Three Independent Variables 438 Making Predictions 439 Binary Independent Variables 440 Statistical Tests for the Population Parameters 441 Polynomial Regression 441 Expressing Nonlinear Relationships Using Linear Models 443 Summary of Nonlinear Regression 446 Summary 447 References 448 The World of Management Science 448 Questions and Problems 449 Case 457 10. Discriminant Analysis 459 Introduction 459 The Two Group DA Problem 460 Group Locations and Centroids 461 Calculating Discriminant Scores 463 The Classification Rule 465 Refining the Cutoff Value 467 Classification Accuracy 468 Classifying New Employees 469 The k Group DA Problem 471 Multiple Discriminant Analysis 472 Distance Measures 474 MDA Classification 476 Summary 478 References 479 The World of Management Science 479 Questions and Problems 480 Case 485 11. Time Series Forecasting 488 Introduction 488 Time Series Methods 489 Measuring Accuracy 489 Stationary Models 490 Contents xvii Moving Averages 491 Forecasting with the Moving Average Model 494 Weighted Moving Averages 494 Forecasting with the Weighted Moving Average Model 497 Exponential Smoothing 497 Forecasting with the Exponential Smoothing Model 499 Seasonality 501 Stationary Data with Additive Seasonal Effects 502 Forecasting with the Model 505 Stationary Data with Multiplicative Seasonal Effects 506 Forecasting with the Model 509 Trend Models 509 An Example 509 Double Moving Average 510 Forecasting with the Model 512 Double Exponential Smoothing (Holt s Method) 513 Forecasting with Holt s Method 515 Holt Winter s Method for Additive Seasonal Effects 516 Forecasting with Holt Winter s Additive Method 519 Holt Winter s Method for Multiplicative Seasonal Effects 521 Forecasting with Holt Winter s Multiplicative Method 523 Modeling Time Series Trends Using Regression 525 Linear Trend Model 525 Forecasting with the Linear Trend Model 526 Quadratic Trend Model 528 Forecasting with the Quadratic Trend Model 530 Modeling Seasonality with Regression Models 531 Adjusting Trend Predictions with Seasonal Indices 531 Computing Seasonal Indices 532 Forecasting with Seasonal Indices 533 Refining the Seasonal Indices 535 Seasonal Regression Models 536 The Seasonal Model 538 Forecasting with the Seasonal Regression Model 540 Crystal Ball Predictor 541 Using CB Predictor 542 Combining Forecasts 547 Summary 548 References 548 The World of Management Science 548 Questions and Problems 549 Cases 556 1 ! xviii Contents 12. Introduction to Simulation Using Crystal Ball 560 Introduction 560 Random Variables and Risk 561 Why Analyze Risk? 561 Methods of Risk Analysis 562 Best Case/Worst Case Analysis 562 What If Analysis 563 Simulation 564 A Corporate Health Insurance Example 564 A Critique of the Base Case Model 566 Spreadsheet Simulation Using Crystal Ball 567 Starting Crystal Ball 567 Random Number Generators 568 Discrete vs. Continuous Random Variables 570 Preparing the Model for Simulation 571 Alternate RNG Entry 574 Running the Simulation 574 Selecting the Output Cells to Track 574 Selecting the Number of Iterations 575 Determining the Sample Size 577 Running the Simulation 577 Data Analysis 578 The Best Case and the Worst Case 578 Viewing the Distribution of the Output Cells 579 Viewing the Cumulative Distribution of the Output Cells 579 Obtaining Other Cumulative Probabilities 581 Incorporating Graphs and Statistics into a Spreadsheet 581 The Uncertainty of Sampling 582 Constructing a Confidence Interval for the True Population Mean 583 Constructing a Confidence Interval for a Population Proportion 584 Sample Sizes and Confidence Interval Widths 585 The Benefits of Simulation 586 Additional Uses of Simulation 586 A Reservation Management Example 586 Implementing the Model 587 Using the Decision Table Tool 588 An Inventory Control Example 592 Creating the RNGs 593 Implementing the Model 593 Replicating the Model 597 Optimizing the Model 597 Comments on Using OptQuest 600 A Project Selection Example 601 A Spreadsheet Model 602 Solving the Problem with OptQuest 603 Finding a Solution with Less Risk 605 Summary 609 The World of Management Science 609 References 610 Questions and Problems 610 Cases 621 Contents xix 13. Queuing Theory 628 Introduction 628 The Purpose of Queuing Models 629 Queuing System Configurations 630 Characteristics of Queuing Systems 631 Arrival Rate 631 Service Rate 633 Kendall Notation 635 Queuing Models 635 The M/M/s Model 637 An Example 637 The Current Situation 637 Adding a Server 638 Economic Analysis 640 The M/M/s Model with Finite Queue Length 640 The Current Situation 641 Adding a Server 642 The M/M/s Model with Finite Population 642 An Example 644 The Current Situation 644 Adding Servers 646 The M/G/l Model 646 The Current Situation 649 Adding the Automated Dispensing Device 649 The M/D/l Model 650 Simulating Queues the Steady State Assumption 652 Summary 653 References 653 The World of Management Science 654 Questions and Problems 655 Case 660 14. Project Management 661 Introduction 661 An Example 662 Creating the Project Network 662 A Note on Start and Finish Points 664 CPM: An Overview 665 The Forward Pass 666 The Backward Pass 669 Determining the Critical Path 671 A Note on Slack 672 Project Management Using Spreadsheets 673 Determining Earliest and Latest Start Times Using LP 675 An LP Model for Earliest Start Times 676 Implementing the Model in a Spreadsheet 677 Solving the Model 679 An LP Model for Latest Start Times 679 Implementing and Solving the Model in a Spreadsheet 681 Summarizing the Results 681 xx Contents Project Crashing 682 An LP Approach to Crashing 684 Determining the Earliest Crash Completion Time 685 Implementing the Model 686 Solving the Model 688 Determining the Least Costly Crash Schedule 688 Crashing as an MOLP 689 Certainty vs. Uncertainty 690 PERT: An Overview 691 The Problems with PERT 692 Implications 694 Simulating Project Networks 695 An Example 695 Generating Random Activity Times 696 Implementing the Model 696 Running the Simulation 697 Analyzing the Result 699 Microsoft Project 699 Summary 702 References 703 The World of Management Science 704 Questions and Problems 704 Case 711 15. Decision Analysis 713 Introduction 713 Good Decisions vs. Good Outcomes 714 Characteristics of Decision Problems 714 An Example 715 The Payoff Matrix 716 Decision Alternatives 716 States of Nature 716 The Payoff Values 717 Decision Rules 718 Nonprobabilistic Methods 719 The Maximax Decision Rule 719 The Maximin Decision Rule 720 The Minimax Regret Decision Rule 720 Probabilistic Methods 723 Expected Monetary Value 724 Expected Regret 725 Sensitivity Analysis 727 The Expected Value of Perfect Information 729 Decision Trees 730 Rolling Back a Decision Tree 732 Using TreePlan 734 Adding Branches 734 Adding Event Nodes 735 Adding the Cash Flows 739 Determining the Payoffs and EMVs 741 Other Features 741 Multistage Decision Problems 743 A Multistage Decision Tree 744 Analyzing Risk in a Decision Tree 745 Risk Profiles 746 Strategy Tables 747 Contents xxi Using Sample Information in Decision Making 750 Conditional Probabilities 750 The Expected Value of Sample Information 752 Computing Conditional Probabilities 753 Bayes s Theorem 755 Utility Theory 756 Utility Functions 757 Constructing Utility Functions 758 Using Utilities to Make Decisions 760 The Exponential Utility Function 761 Incorporating Utilities in TreePlan 762 Multicriteria Decision Making 764 The Multicriteria Scoring Model 765 The Analytic Hierarchy Process 766 Pairwise Comparisons 767 Normalizing the Comparisons 768 Consistency 769 Obtaining Scores for the Remaining Criteria 771 Obtaining Criterion Weights 771 Implementing the Scoring Model 773 Summary 773 References 775 The World of Management Science 775 Questions and Problems 776 Case 786 Index 789
any_adam_object	1
author	Ragsdale, Cliff T.
author_facet	Ragsdale, Cliff T.
author_role	aut
author_sort	Ragsdale, Cliff T.
author_variant	c t r ct ctr
building	Verbundindex
bvnumber	BV013345540
callnumber-first	T - Technology
callnumber-label	T57
callnumber-raw	T57.62
callnumber-search	T57.62
callnumber-sort	T 257.62
callnumber-subject	T - General Technology
classification_rvk	QH 444
classification_tum	WIR 522f MAT 900f
ctrlnum	(OCoLC)247045217 (DE-599)BVBBV013345540
dewey-full	658.403028553
dewey-hundreds	600 - Technology (Applied sciences)
dewey-ones	658 - General management
dewey-raw	658.403028553
dewey-search	658.403028553
dewey-sort	3658.403028553
dewey-tens	650 - Management and auxiliary services
discipline	Mathematik Wirtschaftswissenschaften
edition	3. ed.
format	Book
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02253nam a2200565 c 4500</leader><controlfield tag="001">BV013345540</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">000915s2001 \|\|\|\| 00\|\|\| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">0324021224</subfield><subfield code="9">0-324-02122-4</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)247045217</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV013345540</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91G</subfield><subfield code="a">DE-945</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">T57.62</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">658.403028553</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QH 444</subfield><subfield code="0">(DE-625)141591:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">WIR 522f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">MAT 900f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ragsdale, Cliff T.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Spreadsheet modeling and decision analysis</subfield><subfield code="b">a practical introduction to management science</subfield><subfield code="c">Cliff T. Ragsdale</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">3. ed.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cincinnati, Ohio</subfield><subfield code="b">South Western College Publ.</subfield><subfield code="c">2001</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XXI, 794 S.</subfield><subfield code="e">2 CD-ROM</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Decision making</subfield><subfield code="x">Computer simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Decision support systems</subfield><subfield code="x">Computer programs</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Electronic spreadsheets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Management science</subfield><subfield code="x">Computer simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Operations research</subfield><subfield code="x">Computer simulation</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Tabellenkalkulation</subfield><subfield code="0">(DE-588)4117161-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Management</subfield><subfield code="0">(DE-588)4037278-9</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Operations Research</subfield><subfield code="0">(DE-588)4043586-6</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Computersimulation</subfield><subfield code="0">(DE-588)4148259-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Computersimulation</subfield><subfield code="0">(DE-588)4148259-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Management</subfield><subfield code="0">(DE-588)4037278-9</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="1" ind2="0"><subfield code="a">Computersimulation</subfield><subfield code="0">(DE-588)4148259-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2="1"><subfield code="a">Operations Research</subfield><subfield code="0">(DE-588)4043586-6</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="2" ind2="0"><subfield code="a">Tabellenkalkulation</subfield><subfield code="0">(DE-588)4117161-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="2" ind2="1"><subfield code="a">Operations Research</subfield><subfield code="0">(DE-588)4043586-6</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="2" ind2=" "><subfield code="8">1\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">HBZ Datenaustausch</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009101344&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-009101344</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield></record></collection>
id	DE-604.BV013345540
illustrated	Not Illustrated
indexdate	2024-07-09T18:44:11Z
institution	BVB
isbn	0324021224
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-009101344
oclc_num	247045217
open_access_boolean
owner	DE-91G DE-BY-TUM DE-945
owner_facet	DE-91G DE-BY-TUM DE-945
physical	XXI, 794 S. 2 CD-ROM
publishDate	2001
publishDateSearch	2001
publishDateSort	2001
publisher	South Western College Publ.
record_format	marc
spelling	Ragsdale, Cliff T. Verfasser aut Spreadsheet modeling and decision analysis a practical introduction to management science Cliff T. Ragsdale 3. ed. Cincinnati, Ohio South Western College Publ. 2001 XXI, 794 S. 2 CD-ROM txt rdacontent n rdamedia nc rdacarrier Decision making Computer simulation Decision support systems Computer programs Electronic spreadsheets Management science Computer simulation Operations research Computer simulation Tabellenkalkulation (DE-588)4117161-5 gnd rswk-swf Management (DE-588)4037278-9 gnd rswk-swf Operations Research (DE-588)4043586-6 gnd rswk-swf Computersimulation (DE-588)4148259-1 gnd rswk-swf Computersimulation (DE-588)4148259-1 s Management (DE-588)4037278-9 s DE-604 Operations Research (DE-588)4043586-6 s Tabellenkalkulation (DE-588)4117161-5 s 1\p DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009101344&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk
spellingShingle	Ragsdale, Cliff T. Spreadsheet modeling and decision analysis a practical introduction to management science Decision making Computer simulation Decision support systems Computer programs Electronic spreadsheets Management science Computer simulation Operations research Computer simulation Tabellenkalkulation (DE-588)4117161-5 gnd Management (DE-588)4037278-9 gnd Operations Research (DE-588)4043586-6 gnd Computersimulation (DE-588)4148259-1 gnd
subject_GND	(DE-588)4117161-5 (DE-588)4037278-9 (DE-588)4043586-6 (DE-588)4148259-1
title	Spreadsheet modeling and decision analysis a practical introduction to management science
title_auth	Spreadsheet modeling and decision analysis a practical introduction to management science
title_exact_search	Spreadsheet modeling and decision analysis a practical introduction to management science
title_full	Spreadsheet modeling and decision analysis a practical introduction to management science Cliff T. Ragsdale
title_fullStr	Spreadsheet modeling and decision analysis a practical introduction to management science Cliff T. Ragsdale
title_full_unstemmed	Spreadsheet modeling and decision analysis a practical introduction to management science Cliff T. Ragsdale
title_short	Spreadsheet modeling and decision analysis
title_sort	spreadsheet modeling and decision analysis a practical introduction to management science
title_sub	a practical introduction to management science
topic	Decision making Computer simulation Decision support systems Computer programs Electronic spreadsheets Management science Computer simulation Operations research Computer simulation Tabellenkalkulation (DE-588)4117161-5 gnd Management (DE-588)4037278-9 gnd Operations Research (DE-588)4043586-6 gnd Computersimulation (DE-588)4148259-1 gnd
topic_facet	Decision making Computer simulation Decision support systems Computer programs Electronic spreadsheets Management science Computer simulation Operations research Computer simulation Tabellenkalkulation Management Operations Research Computersimulation
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009101344&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT ragsdaleclifft spreadsheetmodelinganddecisionanalysisapracticalintroductiontomanagementscience

Verfügbarkeit

Es ist kein Print-Exemplar vorhanden.

Fernleihe Bestellen Achtung: Nicht im THWS-Bestand! Inhaltsverzeichnis

MARC

Datensatz im Suchindex

Es ist kein Print-Exemplar vorhanden.

Ähnliche Einträge