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| The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics) | 
| Authors: David L. Applegate, Robert E. Bixby, Vasek Chvatal, William J. Cook
Publisher: Princeton University Press
Category: Book
List Price: $46.95
Buy New: $36.00
You Save: $10.95 (23%)
New (19) from $36.00
Avg. Customer Rating:  1 reviews
Sales Rank: 396697
Media: Hardcover
Number Of Items: 1
Pages: 606
Shipping Weight (lbs): 2.1
Dimensions (in): 9.2 x 6.4 x 1.7
ISBN: 0691129932
Dewey Decimal Number: 511.6
EAN: 9780691129938
ASIN: 0691129932
Publication Date: January 15, 2007
Availability: Usually ships in 24 hours
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| Customer Reviews:
Twenty years in the making July 14, 2007
6 out of 6 found this review helpful
The coauthors have been working at solving large scale traveling saleman problem instances for more than 20 years. This book, along with the publicly available Concorde code, is the culmination of that twenty years of work.
The first four chapters of the book (130 pages or so) are an extremely readable description of the use and history of the traveling salesman problem. For our field, the traveling salesman problem has been an exemplar of a hard combinatorial problem, commonly used to test new ideas in problem solving. It is no coincidence that the first papers on simulated annealing, DNA computing, and other approaches for combinatorial problems describe their methods in the context of the TSP: it is the most well known of all the problems in operations research.
The authors' primary emphasis is on computation: how can optimal tours be found? The history of TSP computation is very much the history of computational combinatorial optimization. From the fundamental work of Dantzig, Fulkerson, and Johnson in solving the famous 42-city example, through Held and Karp's relaxations andLin-Kernighan's improvement heuristics, to modern-day branch-and-cut, the TSP has been at the forefront of computational methods in our field. The description of this history is outstanding, and appropriately nontechnical, suitable for reading by beginners in operations research.
The main part of the book is on the computational approaches needed to solve large TSPs. This part is well-written, though beyond a beginner level. Despite that, it has broad interest in the way it melds computational issues (like data structures and heuristic ordering) with the theory.
At the end, this book is an exemplar for how to do research in computational operations research. While not aimed at the non-specialist, it is perfectly readable by those who have gone through an introduction in optimization or operations research.
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