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Simulation Modelling and Analysis

Law, Averill M., Kelton, W. David

This book provides a comprehensive guide to designing, implementing, and analyzing computer simulation models to understand and improve complex real-world systems.

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What it’s about

This book introduces simulation as a powerful tool for studying complex systems where analytical solutions are not feasible, covering applications from manufacturing and transportation to service organizations. It details the fundamental concepts of systems, models, and discrete-event simulation, including the next-event time-advance mechanism. Readers are given a step-by-step guide to building simulation models using general-purpose programming languages, illustrated with detailed examples of a single-server queueing system and an inventory system. Beyond just programming, the book emphasizes the importance of proper simulation methodology, including model validation and statistical analysis of output, to draw valid and reliable conclusions, making it an essential text for students and practitioners who need to leverage simulation for decision-making.

The through-line

Who it’s for
The reader is a student, analyst, engineer, or manager in fields like operations research, management science, or industrial engineering. They are tasked with understanding, analyzing, and improving complex real-world systems (like manufacturing plants, call centers, or supply chains), but find these systems too intricate for simple analytical methods.
The problem
They need to make informed decisions about system design, operation, or improvement (e.g., how many servers to staff, what inventory policy to use) but lack the tools to accurately predict the performance of different alternatives. They feel uncertain and overwhelmed by the complexity and randomness of the system. They are afraid of making costly or disruptive real-world changes that might not work, leading to wasted resources and poor performance.
The plan
  1. Understand the fundamental concepts of systems, models, and when to use simulation.
  2. Learn the core logic of discrete-event simulation, including the next-event time-advance mechanism and the key components of a simulation program.
  3. Build and run your own simulation models for classic systems (like queues and inventories) using a general-purpose programming language to master the details.
  4. Apply proper statistical techniques to analyze simulation output and make valid inferences.
The payoff
The reader will be able to confidently build valid simulation models of complex systems. · They will make better, data-driven decisions that improve system performance, reduce costs, and avoid risky real-world experiments. · They will become a trusted expert in a powerful and widely-used analytical technique.

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