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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
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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)
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