Introduction to management science:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Upper Saddle River, NJ
Prentice Hall
1996
|
| Ausgabe: | 5. ed. |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007338967&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XXII, 902 S. Ill., graph. Darst. |
| ISBN: | 0132093219 |
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| 245 | 1 | 0 | |a Introduction to management science |c Bernard W. Taylor |
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| 300 | |a XXII, 902 S. |b Ill., graph. Darst. | ||
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Datensatz im Suchindex
| _version_ | 1819263213711130624 |
|---|---|
| adam_text | Brief Contents
Chapter 1
Management Science 1
Chapter 2
Linear Programming: Model Formulation and
Graphical Solution 14
Chapter 3
Linear Programming: Computer Solution and
Sensitivity Analysis 49
Chapter 4
Linear Programming: Modeling Examples 93
Chapter 5
The Simplex Solution Method 141
Chapter 6
Postoptimality Analysis with the
Simplex Method 188
Chapter 7
Transportation and Assignment Problems 224
Chapter 8
Integer Programming 280
Chapter 9
Goal Programming 317
Chapter 10
Probability and Statistics 350
Chapter 11
Decision Analysis and Game Theory 385
Chapter 12
Break Even Analysis with Calculus
Based Solution Techniques 445
Chapter 13
Markov Analysis 477
Chapter 14
Queuing Analysis 505
Chapter 15
Simulation 539
Chapter 16
Forecasting 581
Chapter 17
Statistical Quality Control 624
Chapter 18
Inventory Management: Certain Demand 661
Chapter 19
Inventory Management: Uncertain Demand 694
Chapter 20
Network Flow Models 717
Chapter 21
Project Management 755
Chapter 22
Dynamic Programming 804
Chapter 23
The Manager and Management Science:
Information Systems 835
Appendix A
Normal Table 851
Appendix B
Matrix Multiplication 852
Appendix C
The Poisson and Exponential Distributions 855
Appendix D
The Beta Distribution 857
Appendix E
Rules of Differentiation 858
Appendix F
Starting AB:QM 861
Appendix G
Starting QSB+ 867
Solutions to Odd Numbered Problems 875
Glossary 887
Index 895
Contents
Preface xxi
Chapter 1
Management Science 1
The Management Science Approach
to Problem Solving 2
Observation 3
Definition of the Problem 3
Model Construction 3
Time Out for Pioneers in Management Science 4
Management Science Application: Management
Science at American Airlines 5
Model Solution 6
Implementation of Results 7
Management Science as an Ongoing Process 7
Management Science Techniques 7
Linear Mathematical Programming Techniques 8
Probabilistic Techniques 8
Inventory Techniques 8
Network Techniques 8
Other Linear and Nonlinear Techniques 8
Business Usage of Management Science
Techniques 9
Management Science Application: Management
Science at Reynolds Metals Company 9
Management Science Software
and Computer Solutions 10
Management Science Application: Management
Science at Citgo 11
Summary 12
References 12
Questions 12
Chapter 2
Linear Programming: Model
Formulation and Graphical Solution 14
Model Formulation 15
A Maximization Model Example 15
lime Out for George B. Dantzig 16
Decision Variables 16
The Objective Function 16
Model Constraints 17
Graphical Solutions of Linear
Programming Models 18
Graphical Solution of a Maximization Model 18
Management Science Application: A Refinery Linear
Programming System at Citgo Petroleum 19
The Optimal Solution Point 23
The Solution Values 24
Summary of the Graphical Solution Steps 28
A Minimization Model Example 28
Decision Variables 29
The Objective Function 29
Model Constraints 29
Graphical Solution of a Minimization Model 30
Management Science Application: Chemical
Production at Monsanto 31
Irregular Types of Linear Programming
Problems 32
Multiple Optimal Solutions 32
An Infeasible Problem 33
An Unbounded Problem 34
Characteristics of Linear Programming
Problems 35
Properties of Linear Programming Models 35
Summary 36
x Contents
References 36
Example Problem Solutions 37
Questions 40
Problems 41
Case Problems 47
Chapter 3
Linear Programming: Computer
Solution and Sensitivity Analysis 49
Standard Model Form 49
Converting s Constraints 50
Management Science Application: Optimal Aircraft
and Munitions Procurement in the Air Force 51
Converting a Constraints 53
= Constraint 54
Proper Constraint Form 54
Computer Solution 55
AB:QM 56
QSB+ 58
LINDO 60
Spreadsheets 62
Sensitivity Analysis 64
Changes in Objective Function Coefficients 64
Management Science Application: Collecting
Delinquent Credit Card Accounts at GE Capital
with Linear Programming 67
Objective Function Coefficient Ranges
with the Computer 69
Changes in Constraint Quantity Values 71
Constraint Quantity Value Ranges
with the Computer 74
Other Forms of Sensitivity Analysis 74
Shadow Prices 76
Summary 78
References 79
Example Problem Solution 79
Questions 82
Problems 84
Case Problem 91
Chapter 4
Linear Programming: Modeling
Examples 93
A Product Mix Example 94
Decision Variables 95
The Objective Function 95
Model Constraints 95
Model Summary 95
Computer Solution 96
Solution Analysis 97
A Diet Example 97
Decision Variables 98
The Objective Function 98
Model Constraints 98
Model Summary 98
Solution Analysis 99
Management Science Application: The Evolution
of the Diet Problem 99
An Investment Example 100
Decision Variables 100
The Objective Function 100
Model Constraints 101
Model Summary 101
Solution Analysis 102
A Marketing Example 102
Decision Variables 102
The Objective Function 102
Model Constraints 103
Management Science Application: A Linear
Programming Model for Optimal Portfolio Selection
at Prudential Securities, Inc. 103
Model Summary 104
Solution Analysis 104
A Transportation Example 104
Decision Variables 105
The Objective Function 105
Model Constraints 106
Model Summary 106
Computer Solution 106
Management Science Application: Gasoline Blending
at Texaco 107
Solution Analysis 108
A Blend Example 108
Decision Variables 109
The Objective Function 109
Model Constraints 109
Model Summary 110
Computer Solution 110
Solution Analysis 111
A Multiperiod Scheduling Example 112
Decision Variables 112
The Objective Function 113
Model Constraints 113
Model Summary 113
Computer Solution 114
Solution Analysis 115
Summary 115
References 116
Example Problem Solution 116
Questions 119
Problems 120
Case Problems 138
Chapter 5
The Simplex Solution Method hi
Converting the Model into Standard
Form 142
The Solution of Simultaneous Equations 143
The Simplex Method 145
Computing the z, and Cj — Zj Rows 148
The Entering Nonbasic Variable 149
The Leaving Basic Variable 150
Developing a New Tableau 152
Time Out for George B. Dantzig 154
The Optimal Simplex Tableau 154
Summary of the Simplex Method 156
Computer Solution with Simplex
Tableaus 156
Simplex Solution of a Minimization
Problem 158
Standard Form of a Minimization Model 158
The Simplex Tableau for a Minimization
Problem 160
Simplex Adjustments for a Minimization
Problem 162
A Mixed Constraint Problem 162
Contents xi
Irregular Types of Linear
Programming Problems 165
Multiple Optimal Solutions 165
An Infeasible Problem 167
An Unbounded Problem 168
Tie for the Pivot Column 169
Tie for the Pivot Row—Degeneracy 169
Management Science Application: Rapid Innovation
in the Microchip Industry 171
Negative Quantity Values 172
Summary of Simplex Irregularities 172
Summary 172
References 172
Example Problem Solutions 173
Questions 176
Problems 176
Case Problem 187
Chapter 6
Postoptimality Analysis
with the Simplex Method 188
The Dual 188
Interpreting the Dual Model 190
Additional Aspects of Formulating the Dual 194
A Mixed Constraint Problem 196
Time Out for John Von Neumann 197
Use of the Dual 197
Sensitivity Analysis 197
Changes in Objective Function Coefficients 198
Changes in Constraint Quantity Values 201
Additional Model Parameter Changes 206
Management Science Application: Optimal Wood
Procurement for Cabinetmaking 207
Summary 208
References 208
Example Problem Solution 208
Questions 211
Problems 211
Case Problem 223
xii Contents
Chapter 7
Transportation and Assignment
Problems 224
The Transportation Model 225
Solution of the Transportation Model 227
The Northwest Corner Method 227
Time Out for Frank L. Hitchcock
and Tjalling C. Koopmans 228
The Minimum Cell Cost Method 229
Vogel s Approximation Method 231
The Stepping Stone Solution Method 233
The Modified Distribution Method 240
Time Out for Abraham Charnes
and William W. Cooper 242
The Unbalanced Transportation Model 244
Degeneracy 245
Management Science Application: Transporting
Sand for Airport Construction Landfill 247
Prohibited Routes 248
Computer Solution of a Transportation
Problem 248
AB:QM 248
QSB+ 249
The Assignment Model 251
Time Out for Harold W. Kuhn 255
Computer Solution of an Assignment
Problem 255
AB:QM 255
QSB+ 256
Management Science Application: Transporting
Military Inductees in Thailand 257
Summary 258
References 258
Example Problem Solution 258
Questions 260
Problems 261
Case Problems 277
Chapter 8
Integer Programming 280
Integer Programming Models 281
A Total Integer Model Example 281
A 0 1 Integer Model Example 282
A Mixed Integer Model Example 282
Management Science Application: Selecting
Freight Carriers at Reynolds Metals Company 283
Integer Programming Model Solution 284
The Branch and Bound Method 285
Management Science Application: Fleeting
the Schedule at Delta Airlines 295
Time Out for Ralph E. Gomory 296
Solution of the Mixed Integer Model 296
Solution of the 0 1 Integer Model 297
Computer Solution of Integer
Programming Problems 298
The 0 1 Integer Programming Problem
Solution 298
Management Science Application: Minimizing
Color Photographic Paper Waste at Kodak 299
The Total Integer Programming Problem
Solution 300
The Mixed Integer Programming Problem
Solution 302
Management Science Application: Optimal
Assignment of Gymnasts to Events 303
Summary 304
References 304
Example Problem Solution 304
Questions 307
Problems 307
Case Problems 314
Chapter 9
Goal Programming 317
Model Formulation 318
Labor Goal 318
Profit Goal 320
Material Goal 320
Alternative Forms of Goal Constraints 321
Graphical Interpretation of Goal
Programming 322
Management Science Application: Determining
a City s Tax System with Multiple Objectives 325
Time Out for Abraham Charnes
and William W. Cooper 326
Computer Solution of Goal
Programming Problems 326
UNDO 326
AB:QM 329
The Modified Simplex Method 330
Management Science Application: Assigning MBA
Students to Project Teams at the University
of South Carolina 331
Summary of the Steps of the Modified
Simplex Method 335
Summary 335
References 335
Example Problem Solution 336
Questions 338
Problems 338
Case Problems 346
Chapter 10
Probability and Statistics 350
Types of Probability 351
Objective Probability 351
Subjective Probability 352
Fundamentals of Probability 353
Management Science Application: Treasure
Hunting with Probability and Statistics 353
Statistical Independence
and Dependence 357
Independent Events 357
Probability Trees 358
The Binomial Distribution 358
Dependent Events 361
Bayesian Analysis 363
Expected Value 364
Management Science Application: A Probability
Model for Analyzing Coast Guard Patrol
Effectiveness 365
The Normal Distribution 367
Sample Mean and Variance 372
Statistical Analysis Using the Computer 374
Summary 375
Contents xiii
References 375
Example Problem Solution 375
Questions 376
Problems 377
Case Problem 383
Chapter 11
Decision Analysis and Game Theory 385
Components of Decision Making 386
Decision Making Without Probabilities 387
Management Science Application: Decision
Analysis at DuPont 387
Decision Making Criteria 388
The Maximax Criterion 388
The Maximin Criterion 389
The Minimax Regret Criterion 389
The Hurwicz Criterion 390
The Equal Likelihood Criterion 391
Summary of Criteria Results 392
Computerized Solution of Decision Making
Problems Without Probabilities 392
Decision Making with Probabilities 394
Expected Value 394
Expected Opportunity Loss 395
Computerized Solution Using Expected Value 395
Expected Value of Perfect Information 396
Decision Trees 397
Sequential Decision Trees 399
Computerized Decision Tree Analysis 402
Decision Analysis with Additional
Information 403
Management Science Application: Discount Fare
Allocation at American Airlines 405
Computerized Bayesian Analysis 406
Decision Trees with Posterior Probabilities 406
Computing Posterior Probabilities with Tables 409
The Expected Value of Sample Information 410
Determining Posterior Probabilities
with the Computer 410
Utility 412
Management Science Application: Drug Testing
of Athletes at Santa Clara University 413
Game Theory 414
xiw Contents
Types of Game Situations 414
The Two Person, Zero Sum Game 415
A Pure Strategy 416
Dominant Strategies 417
A Mixed Strategy 418
Time Out for John Von Neumann
and Oskar Morgenstern 419
Expected Gain and Loss Method 420
Computerized Solution of Game Theory
Problems 422
Summary 422
References 423
Example Problem Solution 423
Questions 426
Problems 427
Case Problems 443
Chapter 12
Break Even Analysis with Calculus
Based Solution Techniques 445
Components of Break Even Analysis 446
Volume 446
Costs 446
Profit 447
The Break Even Point 448
Variations in Volume 450
Profit Analysis 450
Price 451
Variable Costs 451
Fixed Costs 452
Probabilistic Analysis
of the Break Even Model 453
Computerized Break Even Analysis 455
Nonlinear Profit Analysis 456
Constrained Optimization 459
The Substitution Method 461
The Method of Lagrange Multipliers 464
The Meaning of A 465
Computer Solution of Nonlinear
Programming Problems 466
Management Science Application: Gas Production
Planning in Australia 467
Summary 468
References 468
Example Problem Solution 469
Questions 470
Problems 471
Case Problem 476
Chapter 13
Markov Analysis 477
The Characteristics of Markov Analysis 478
Markov Analysis Information 479
Time Out for Andrey A. Markov 480
The Transition Matrix 481
Steady State Probabilities 484
Direct Algebraic Determination of Steady
State Probabilities 485
Application of the Steady State Probabilities 486
Computer Determination of Steady States 487
Additional Examples
of Markov Analysis 488
Management Science Application: Measuring
Coast Guard Patrol Effectiveness 489
Special Types of Transition Matrices 490
The Debt Example 490
Performing Matrix Operations
on the Computer 493
Summary 494
References 494
Example Problem Solution 494
Questions 496
Problems 496
Case Problems 502
Chapter 14
Queuing Analysis 505
Elements of Waiting Line Analysis 506
The Single Server Waiting Line System 507
The Queue Discipline 507
Time Out for Agner Krarup Erlang 508
The Calling Population 508
The Arrival Rate 508
The Service Rate 508
The Single Server Model 509
The Effect of Operating Characteristics on
Managerial Decisions 511
Computer Analysis of the Single Server System 515
Undefined and Constant Service Times 516
Management Science Application: Reducing
Arrest to Arraignment Times in New York City 517
Finite Queue Length 518
Computer Analysis of the Finite Queue Model 520
Management Science Application: Providing
Optimal Telephone Order Service at L.L Bean 521
Finite Calling Population 522
Computer Analysis of the Finite Calling
Population Model 523
The Multiple Server Waiting Line 524
Computer Analysis of the Multiple Server System 527
Additional Types of Queuing Systems 527
Summary 528
References 529
Example Problem Solution 529
Questions 530
Problems 531
Case Problems 536
Chapter 15
Simulation 539
The Monte Carlo Process 540
The Use of Random Numbers 540
Management Science Application: Improving
the Red Cross Blood Donation Process Using
Simulation 543
Time Out for John Von Neumann 545
Computer Simulation 546
Simulation of a Queuing System 548
Contents xw
Management Science Application: Simulating
the Dispatching of Freight Carriers at Reynolds
Metals Company 549
Continuous Probability Distributions 551
Simulation of a Machine Breakdown
and Maintenance System 552
Computer Simulation of the Machine
Breakdown Example 555
An Inventory Simulation Example 557
Simulation of an Inventory System
with Uncertain Demand and Lead Time 558
Random Number Generators 559
Model Experimentation 560
Optimization Using Simulation 560
Validation of Simulation Results 561
Areas of Simulation Application 562
Queuing 562
Inventory Control 562
Production and Manufacturing 562
Finance 563
Marketing 563
Management Science Application: Simulating
a 10 km Race in Boulder, Colorado 563
Public Service Operations 564
Environmental and Resource Analysis 564
Simulation Languages 564
Summary 564
References 565
Example Problem Solution 565
Questions 568
Problems 568
Case Problem 578
Chapter 16
Forecasting 581
Forecasting Components 582
Forecasting Methods 583
Time Series Methods 584
Moving Average 584
Weighted Moving Average 587
Exponential Smoothing 587
xvi Contents
Adjusted Exponential Smoothing 591
Linear Trend Line 592
Management Science Application: Forecasting
Demand for Discount Fares at American Airlines 593
Seasonal Adjustments 595
Forecast Accuracy 596
Mean Absolute Deviation 597
Cumulative Error 598
Time Series Forecasting Using the Computer 600
Regression Methods 602
Linear Regression 602
Management Science Application: Forecasting
Wholesale Prices and Product Volume at Citgo
Petroleum 603
Correlation 605
Multiple Regression 606
Regression Analysis with the Computer 606
Management Science Application: Forecasting
Aircraft Parts Demand at American Airlines 607
Summary 608
References 608
Example Problem Solutions 608
Questions 611
Problems 612
Case Problems 622
Chapter 17
Statistical Quality Control 624
The Meaning of Quality 625
Quality from the Consumer s Viewpoint 625
Quality from the Producer s Viewpoint 626
Total Quality Management 626
Statistical Process Control 627
Time Out for Walter Shewhart and W. E. Deming 628
Inspection 629
Controlling the Production Process 630
Quality Measures: Attributes and Variables 630
Control Charts 631
Management Science Application: Statistical
Process Control at Kurt Manufacturing 631
Control Charts for Attributes 633
Management Science Application: Using Statistical
Process Control to Achieve Six Sigma Quality
at Motorola 633
p Chart 634
Constructing a p Chart 634
c Chart 637
Constructing a c Chart 637
Control Charts for Variables 638
Mean (x) Chart 639
Constructing a Mean Chart 639
Range (R) Chart 640
Constructing an R Chart 640
Management Science Application: Using x Charts
at Frito Lay 641
Using x Charts and R Charts Together 642
An x Chart and R Chart Used Together 643
Control Chart Patterns . 644
Sample Size Determination 646
Design Tolerances and Process
Capability 646
Computer Statistical Process
Control 648
Management Science Application: Achieving
Design Tolerances at Harley Davidson Company 650
Summary 650
References 651
Example Problem Solution 651
Questions 652
Problems 652
Case Problems 658
Chapter 18
Inventory Management: Certain
Demand 661
Elements of Inventory Management 662
The Role of Inventory 662
Demand 663
Inventory Costs 663
Inventory Control Systems 664
Continuous Inventory Systems 665
Periodic Inventory Systems 665
Time Out for Ford Harris 666
Economic Order Quantity Models 666
The Basic EOQ Model 666
Carrying Cost 667
Ordering Cost 669
Total Inventory Cost 669
EOQ Analysis over Time 672
The EOQ Model with a Reorder Point 672
The EOQ Model with
Noninstantaneous Receipt 673
The EOQ Model with Shortages 676
Management Science Application: Parts Inventory
Management at IBM 679
EOQ Analysis with the Computer 680
Quantity Discounts 681
Quantity Discounts with Constant Carrying
Costs 681
Quantity Discounts with Carrying Costs
as a Percentage of Price 683
Computer Analysis of the EOQ Model
with Quantity Discounts 684
Material Requirements Planning 685
Summary 686
References 686
Example Problem Solution 686
Questions 687
Problems 688
Case Problems 692
Chapter 19
Inventory Management:
Uncertain Demand 694
The EOQ Model with Safety Stocks 694
Determination of the Safety Stock 696
Determining Safety Stocks Using
Service Levels 698
Reorder Point with Variable Demand 699
Management Science Application: Inventory
Control for Desktop Printers at Hewlett Packard 699
Reorder Point with Variable Lead Time 700
Contents xvii
Reorder Point with Variable Demand
and Lead Time 701
Order Quantity for a Periodic
Inventory System 701
Order Quantity with Variable Demand 702
Determining the Order Quantity
with Payoff Tables 703
Simulation of Inventory 705
Computer Simulation Example 707
Summary 709
References 709
Example Problem Solution 709
Questions 710
Problems 710
Case Problem 715
Chapter 20
Network Flow Models 717
Network Components 718
The Shortest Route Problem 719
The Shortest Route Solution Approach 720
Computer Solution of the Shortest Route
Problem 723
The Minimal Spanning Tree Problem 725
The Mimimal Spanning Tree Solution
Approach 726
Management Science Application: Modeling
a Conveyor Network for the Air Force 727
Computer Solution of the Minimal Spanning
Tree Problem 729
Time Out for E. W. Dijkstra, L R. Ford, Jr.,
and D.R. Fulkerson 731
The Maximal Flow Problem 731
The Maximal Flow Solution Approach 732
Management Science Application: Improving
Service for Yellow Freight System s Terminal Network 733
Computer Solution of the Maximal Flow
Problem 735
Summary 737
References 737
xviii Contents
Example Problem Solution 737
Questions 739
Problems 740
Case Problems 752
Chapter 21
Project Management 755
The Elements of Project Management 756
The Project Team 756
Project Planning 757
Project Control 757
Time Out for Morgan R. Walker, James E. Kelley, Jr.,
and D. G. Malcolm 758
The Project Network 758
Management Science Application: Project
Management Teams at IBM 759
Concurrent Activities 760
Time Out for Henry Gantt 761
The Critical Path 761
Activity Scheduling 763
Activity Slack 765
Probabilistic Activity Times 767
Probability Analysis of the Project Network 771
Computer CPM/PERT Analysis 772
Project Crashing and Time Cost
Trade off 775
Computer Project Crashing 779
The General Relationship of Time and Cost 780
Formulating the CPM/PERT Network
as a Linear Programming Model 780
Management Science Application: Project
Management Trends in the Utilities Industry 781
Project Crashing with Linear Programming 784
Summary 786
References 787
Example Problem Solution 787
Questions 789
Problems 790
Case Problems 801
Chapter 22
Dynamic Programming 804
The Dynamic Programming Solution
Approach 805
Time Out for Richard Bellman 806
Stage 1—Allocation to the Southern Region 806
Stage 2—Allocation to the Eastern Region 807
Stage 3—Allocation to the Northern Region 809
Review of the Solution Steps
for Dynamic Programming 811
The Knapsack Problem 813
Stage 1—Denim Jeans 814
Stage 2—Radio/Tape Cassette Players 815
Management Science Application: Municipal
Waste Treatment and Disposal 815
Stage 3—Tape Cassettes 816
The Stagecoach Problem 817
Stage 1—The Last Leg of the Journey 818
Stage 2—The Second Leg 818
Stage 3—The First Leg 819
Computer Solution of Dynamic Programming
Problems 819
Additional Examples of Dynamic
Programming 822
Management Science Application: Scheduling
Military Airlifts in the Persian Gulf War 823
Summary 824
References 824
Example Problem Solution 825
Questions 826
Problems 827
Case Problem 833
Chapter 23
The Manager and Management
Science: Information Systems 835
Management Information Systems 836
Data Collection and Organization 836
The Computer System 836
Management Information 837
Management Science Application: An Executive
Information System (EIS) at Westinghouse 837
Decision Support Systems 838
Interactive Decision Making 839
Management Decisions 840
Expert Systems and Artificial
Intelligence 841
Management Science Application: A DSS
for Evaluating Freight Transportation
Alternatives at Heinz 841
Artificial Intelligence 843
Implementation 843
The Causes of Implementation Problems 844
Strategies for Successful Implementation 845
Management Science Application: Developing
DSS Shells for Ship and Aircraft Acquisition
by the Coast Guard 845
The Cost of Management Science 846
Management Science
in the Organization 847
Management Science Application: Successful
Implementation of Manufacturing Software 847
Summary 847
References 848
Questions 848
Case Problem 849
Appendix A
Normal Table 851
Appendix B
Matrix Multiplication 852
The Inverse of a Matrix 853
Appendix C
The Poisson and Exponential
Distributions 855
The Poisson Distribution 855
Contents xix
The Exponential Distribution 856
Appendix D
The Beta Distribution 857
Appendix E
Rules of Differentiation 858
Appendix F
Starting AB:QM 861
Hardware Requirements 861
Starting AB:QM 861
The AB:QM Main Menu 862
AB:QM Commands and Editing Features 863
The Program Description 864
Storing and/or Printing Results 864
Getting Out of AB:QM 864
An AB:QM Example 864
Appendix G
Starting QSB+ 867
Hardware Requirements 867
Starting QSB+ 867
The QSB+ Menu 867
Using a Program Module 868
Editing the Problem 873
Saving a Problem in a File 874
Exiting QSB+ 874
Solution to Selected Odd Numbered
Problems 875
Glossary 887
Index 895
|
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| author | Taylor, Bernard W. |
| author_facet | Taylor, Bernard W. |
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| author_sort | Taylor, Bernard W. |
| author_variant | b w t bw bwt |
| building | Verbundindex |
| bvnumber | BV010969132 |
| callnumber-first | T - Technology |
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| callnumber-search | T56 |
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| callnumber-subject | T - General Technology |
| classification_rvk | QP 300 |
| ctrlnum | (OCoLC)32087324 (DE-599)BVBBV010969132 |
| dewey-full | 658.4/03 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.4/03 |
| dewey-search | 658.4/03 |
| dewey-sort | 3658.4 13 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| edition | 5. ed. |
| format | Book |
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| genre | 1\p (DE-588)4123623-3 Lehrbuch gnd-content |
| genre_facet | Lehrbuch |
| id | DE-604.BV010969132 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T10:04:02Z |
| institution | BVB |
| isbn | 0132093219 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007338967 |
| oclc_num | 32087324 |
| open_access_boolean | |
| owner | DE-945 DE-11 |
| owner_facet | DE-945 DE-11 |
| physical | XXII, 902 S. Ill., graph. Darst. |
| publishDate | 1996 |
| publishDateSearch | 1996 |
| publishDateSort | 1996 |
| publisher | Prentice Hall |
| record_format | marc |
| spellingShingle | Taylor, Bernard W. Introduction to management science Besluitvorming gtt Kwantitatieve methoden gtt Management gtt Management science Management (DE-588)4037278-9 gnd Operations Research (DE-588)4043586-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd Optimierung (DE-588)4043664-0 gnd |
| subject_GND | (DE-588)4037278-9 (DE-588)4043586-6 (DE-588)4114528-8 (DE-588)4043664-0 (DE-588)4123623-3 |
| title | Introduction to management science |
| title_auth | Introduction to management science |
| title_exact_search | Introduction to management science |
| title_full | Introduction to management science Bernard W. Taylor |
| title_fullStr | Introduction to management science Bernard W. Taylor |
| title_full_unstemmed | Introduction to management science Bernard W. Taylor |
| title_short | Introduction to management science |
| title_sort | introduction to management science |
| topic | Besluitvorming gtt Kwantitatieve methoden gtt Management gtt Management science Management (DE-588)4037278-9 gnd Operations Research (DE-588)4043586-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd Optimierung (DE-588)4043664-0 gnd |
| topic_facet | Besluitvorming Kwantitatieve methoden Management Management science Operations Research Mathematisches Modell Optimierung Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007338967&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT taylorbernardw introductiontomanagementscience |