Created: 2003 — Last modified: 2010-02-28
The contents of a well-known book on OR is shown, for reference and possible comparison with the course syllabus. (This book isn't, however, the most adequate as support of the course.) The objectives of the course (in its context and semester duration) do not coincide with those of books like this one.
A. RAVINDRAN, Don T. PHILLIPS, James J. SOLBERG, 1987, "Operations Research — principles and practice"Contents
| Chapter 1:
The nature of Operations Research (12 pp) 1
1.1 The history of Operations Research 1.2 The meaning of Operations Research 1.3 Models in Operations Research 1.4 Principles of modeling Chapter 2: Linear Programming (60 pp) 13
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5.5 Decision trees
5.6 Sequential decisions 5.7 Information acquisitions decisions Summary Recommended readings; references; exercises Chapter 6: Random processes (54 pp) 251 6.1 Introduction I Discrete time processes 6.2 An example 6.3 Modeling the process 6.4 A numerical example 6.5 The assumptions reconsidered 6.6 Formal definitions and theory 6.7 First-passage and first-return probabilities 6.8 Classification terminology 6.9 Ergodic Markov chains 6.10 Absorbing Markov chains II Continuous time processes 6.11 An example 6.12 Formal definitions and theory 6.13 The assumptions reconsidered 6.14 Steady-state probabilities 6.15 Birth-death processes 6.16 The Poisson process 6.17 Conclusions Recommended readings; references; exercises Chapter 7: Queuing models (38 pp) 305 7.1 Introduction 7.2 An example 7.3 General characteristics 7.4 Performance measures 7.5 Relations among the performance measures 7.6 Markovian queueing models 7.7 The M/M/1 model 7.8 Limited queue capacity 7.9 Multiple servers 7.10 An example 7.11 Finite sources 7.12 Queue discipline 7.13 Non-Markovian queues 7.14 Networks of queues 7.15 Conclusions Recommended readings; references; exercises |
| Chapter 8:
Inventory models (32 pp) 343
8.1 Introduction I Deterministic models 8.2 The classical Economic Order Quantity 8.3 A numerical example 8.4 Sensitivity analysis 8.5 Nonzero lead time 8.6 The EOQ with shortages allowed 8.7 The production Lot-size model 8.8 Other deterministic inventory models II Probabilistic models 8.9 The newsboy problem: a single period model 8.10 A lot size, reorder point model 8.11 Some numerical examples 8.12 Variable lead times 8.13 The importance of selecting the right model 8.14 Conclusions Recommended readings; references; exercises Chapter 9: Simulation (62 pp) 375 I Basic concepts 9.1 Introduction 9.2 The philosophy, development and implementation of simulation modeling 9.3 Design of simulation models II Examples of simulation modeling 9.4 Performance of a baseball hitter 9.5 Simulation of a tool crib 9.6 Production line maintenance III Pseudo-random numbers 9.7 The uniform distribution and its importance to simulation 9.8 Generation of random numbers 9.9 The logic in generating uniform random variates via a congruential method 9.10 Testing a uniform random number generator IV Techniques for generating random deviates 9.11 The inverse transformation method 9.12 The rejection technique 9.13 The composition method 9.14 Mathematical derivation technique The Box and Muller technique for generating normal deviates 9.15 Approximation techniques 9.16 Special probability distributions V Simulation languages 9.17 An overview 9.18 Comparison of selected existing simulation languages GPSS, SIMSCRIPT, MAP/1, SIMULA, DYNAMO, SLAM II, SIMAN 9.19 The microcomputer revolution in simulation applications |
VI Advanced concepts in
simulation analysis
9.20 Design of simulation experiments 9.21 Variance reduction techniques 9.22 Statistical analysis of simulation output 9.23 Optimization of simulation parameters 9.24 Summary and conclusions Selected reference texts; references; exercises Chapter 10: Dynamic Programming (50 pp) 437 I Basic concepts 10.1 Introduction 10.2 Historical background II The development of Dynamic Programming 10.3 Mathematical description 10.4 Developing an optimal decision policy 10.5 Dynamic Programming in perspective III Illustrative examples 10.6 A problem in oil transport technology 10.7 A facilities selection problem 10.8 The optimal cutting stock problem 10.9 A problem in inventory control IV Continuous state Dynamic Programming 10.10 Introduction 10.11 A nonlinear programming problem 10.12 A problem in mutual fund investment strategies V Multiple state variables 10.13 The "curse of dimensionality" 10.14 A nonlinear, Integer Programming problem 10.15 Elimination of state variables VI Stochastic systems 10.16 Stochastic Dynamic Programming — a brief overview 10.17 Summary and conclusions References; exercises Chapter 11: Nonlinear Programming (100 pp) 487 I Basic concepts 11.1 Introduction 11.2 Taylor's series expansions; necessary and sufficient conditions II Unconstrained optimization (11.3, .4, .5) III Constrained optimization problems: equality constraints (11.6, .7, .8) IV Constrained optimization problems: inequality constraints (11.9, .10, .11, .12) V The general nonlinear programming problem (11.13, .14, .15) References; exercises (p 587) n |
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http://web.ist.utl.pt/~mcasquilho/acad/or/or_RaviPS.php Created: 2003 — Last modified: 2010-02-28 |