Programa típico de «Investigação Operacional»

    Inclui-se o índice dum livro de IO bastante divulgado e interessante, para referência e eventual comparação com o programa da cadeira.  (Não é este, aliás, o livro mais indicado como suporte do programa.)  Note-se que os objectivos da cadeira (no seu âmbito e com duração semestral) não coincidem, necessàriamente, com os de livros como o apresentado.

A. RAVINDRAN, Don T. PHILLIPS, James J. SOLBERG, 1987, "Operations Research — principles and practice"
John Wiley & Sons, 2nd ed., New York, N. Y. (sem data de 1.ª ed.), xviii+637 pp (preço ~3 000 $ em 1992)

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
2.1 Introduction
2.2 Formulation of Linear Programming models
2.3 Graphical solution of Linear Programming in two variables
2.4 Linear Programming in standard form
2.5 Solving systems of linear equations
2.6 Principles of the simplex method
2.7 Simplex method in tableau form
2.8 Computational problems
2.9 Finding a feasible basis
2.10 Computer solution of Linear Programming
2.11 Sensitivity analysis in Linear Programming
2.12 Applications
2.13 Additional topics in Linear Programming
   Recommended readings; references; exercises

Chapter 3: Network analysis (62 pp) 73
3.1 Some examples of network flow problems
3.2 Transportation problems
3.3 Assignment problems
3.4 Maximum-flow problems
3.5 Shortest-route problems
3.6 Minimal spanning tree problems
3.7 Project management
   Recommended readings; references; exercises

Chapter 4: Advanced topics in Linear Programming (86 pp) 135
4.1 The revised simplex method
4.2 Duality theory and its applications
4.3 The dual simplex method
4.4 Sensitivity analysis in Linear Programming
4.5 Parametric programming
4.6 Integer Programming
4.7 Goal Programming
   Recommended readings; references; exercises

Chapter 5: Decision analysis (30 pp) 221
5.1 Introduction
5.2 Characteristics of a decision problem
5.3 Terminal decisions based on prior information
5.4 Expected value of perfect information (EVPI)

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


Actualizado em: 22-Jul-2003