Rating: 
Summary: Complete handbook of discrete-event simulation modeling
Review: If you're a working stiff now, but you were a student as I was, who sent stuff "down the memory hole," only to discover years later that you really needed to DO simulation--AARGHH!!!--then Law and Kelton's book is what you'll need close at hand.
The coverage is complete, including basic material
on input probability distributions; random number generators
and testing (most useful for students); and output data analysis. Solid, more practice-oriented chapters cover variance reduction, experimental design (could borrow
more from Kleijnen's work), and software. A final chapter
gives an in-depth approach to manufacturing simulation.
Unfortunately, some material is dated. For example,
the software chapter addresses SIMAN/Cinema, which
Systems Modeling Corporation has (thankfully!) replaced with ARENA (though SIMAN is still the basic simulation
language). The book gives overviews of several simulation modeling languages; practitioners doing
trade studies choosing "the corporate simulation tool" will be grateful for this ecumenicism. The chapter covering validation and verification could afford to borrow more from current
software engineering practice. Also, simulation modeling
approaches for network computing and communications
would comprise a good extra chapter (Law gives a short
course on the subject).
Law and Kelton isn't light reading, but it's not a dry
handbook, either. If you've had some light programming
experience and vaguely recall your stats class, you'll find
it approachable.
But this book is mostly the timeless stuff you'll need to
get your work done. With the recent proliferation of inexpensive simulation software, companies are thrusting
simulation work on engineers and system/software analysts who have little or no training. They should keep
and use Law and Kelton's book, which provides both good skills and a roadmap to laying out and finishing a simulation project. But I give Law and Kelton's book one of my highest compliments: Expensive technical books
Rating:
Summary: Read it if you need to write your first simulation program
Review: Before reading this book I didn't have a clue about simulation modeling, but after reading the first few chapters I was able to write my first simulation program in C++. Although authors use C language and global data for simulation state, you can easily apply the concept of simulation state to any object-oriented language and replace global simulation state with object state.
Rating: 
Summary: Don't get it if you know statistics
Review: If you know anything about statstics and operations research don't but it thinking it will teach you new things. It is an OKAY reference. A better book to teach you the concepts so you can apply it to more modern languages (than the ones presented in the book) if you already know better programming languages is "Introduction to Operations Research" by Hillier and Lieberman (6th or later editions).
Rating: 
Summary: A useful reference
Review: Purchased this book in 1994 when spreadsheet modeling was no longer sufficient. This book gives a good top down view of what's involved in getting mathematically correct results (called verification, ensuring that the problem is being solved correctly). I found particularly useful the sections on variance reduction and output data reduction techniques. Validation (ensuring that the correct problem is being solved) is still the modelers job. People with technical (mathematical) backgrounds should find this book relatively easy reading. Non-technical (management) types will get bogged down in the first chapter. As in so many things, the devil's in the (mathematical) details. I basically used the book as a guide to writing an OO-based simulation tool (I ignored most of the included code examples), successfully implementing many of the techniques therein suggested. Where there wasn't sufficient detail, I found references to be sufficient. I still use the text as a reference.
Rating:
Summary: A useful reference
Review: Purchased this book in 1994 when spreadsheet modeling was no longer sufficient. This book gives a good top down view of what's involved in getting mathematically correct results (called verification, ensuring that the problem is being solved correctly). I found particularly useful the sections on variance reduction and output data reduction techniques. Validation (ensuring that the correct problem is being solved) is still the modelers job. People with technical (mathematical) backgrounds should find this book relatively easy reading. Non-technical (management) types will get bogged down in the first chapter. As in so many things, the devil's in the (mathematical) details. I basically used the book as a guide to writing an OO-based simulation tool (I ignored most of the included code examples), successfully implementing many of the techniques therein suggested. Where there wasn't sufficient detail, I found references to be sufficient. I still use the text as a reference.
Rating:
Summary: UJVAL REVIEW
Review: the book is extremely lucid and informative.
Rating:
Summary: Bible for Simulation
Review: There are other books for giving you the simulation techniques and languages but to know the theory and statistics of simulation this is THE book.It startes with basics of statisstics and deals everthing you need to know about simulation. At some point you will start wondering how the authors could give so much information in just one book and lose lots of royalty money on the succeeding books.
Rating:
Summary: Better understanding, rather than point and click
Review: This book is the ground foundation of simulation modeling. Currently, only few books in simulation will spend pages and time, dedicated to the theoretical issues as good as this book. If you're tired of point-and-click or "how-to making one server queueing system" and you want to know the fundamental of simulation, you can't overlook this piece of jewel. I don't agree with one review that you have to understand statistics in order to get this book. Simulation is a probabilistic model. Using simulation without understanding statistics results in serious trouble since it will be only a toy (data), not a tool (information.
Rating:
Summary: Better understanding, rather than point and click
Review: This book is the ground foundation of simulation modeling. Currently, only few books in simulation will spend pages and time, dedicated to the theoretical issues as good as this book. If you're tired of point-and-click or "how-to making one server queueing system" and you want to know the fundamental of simulation, you can't overlook this piece of jewel. I don't agree with one review that you have to understand statistics in order to get this book. Simulation is a probabilistic model. Using simulation without understanding statistics results in serious trouble since it will be only a toy (data), not a tool (information.
Rating:
Summary: A must read for anyone interested in simulation
Review: This is must read for simulation. It covers the background required for anyone to work on simulation and modeling. Its chapters about random-number generators, selecting right probability distributions and its parameters, output data analysis or variance reduction techniques are a must for anyone in this area. The treatment is easy to read but without sacrificing rigour. Nevertheless, a basic knowledge on statistics and probability is required. On the other hand, its chapters about modeling performed on fortran, C or pascal are outdated stuff since this software is outdated, but they are useful are a hands-on lesson on how simulation works and must be performed. All in all, a very profitable book.
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