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This book is a good survey of the techniques and theory in Operations Research. It includes all the important topics in OR and statistics with a judicious choice of examples, formulas, and exercises. In addition, it contains an appendix which provides a selection of problems from Operation Research for solution by students. The book will serve as an excellent textbook for undergraduates studying in this field. The proofs are illustrated with figures wherever necessary to make them meaningful to readers unfamiliar with mathematics. The book also tries to make the subject interesting to the other sections of engineering and technology students. But, uniformity in uniformity, other books on OR would be better. All the books should combine a comprehensive coverage of fundamentals with a high level of technical detail. The content, style and treatment should be uniform across all books. In particular, books should contain at least 100-200 problems involving applications of the theory to real life situations. They should have sufficient technical detail to allow students to understand and appreciate the mathematical structures behind important problems in OR and statistical theory. They should avoid a haphazard collection of exercises which merely give a set of formulas for solving numerical problems without going into any real detail about how to solve them. 1. Here, the criterion for an error criterion is not to give an answer which is within prescribed accuracy, but to stop when the answer becomes too close to the desired solution.2. If the number of errors is inversely proportional to the number of input bits used in solving a system, then this scheme would reduce the number of errors in practice by up to a factor of 10 at best! However, it still has some merit as a proof-of-concept scheme for more efficient error detection codes.3. Real time systems must often change their output on a short time interval or more frequently than once a second. For example, robot arms must move to new positions; computer-controlled devices must sense and compute their outputs; etc.4. The solution to the traveling salesman problem (TSP) is a tour that visits all the cities exactly once and returns to the starting city.5. If you said that the degree of freedom of each problem was 2, because it requires two decisions for each decision variable, you are correct! However, because it involves decision variables only, this example can be solved using branch and bound algorithms like those used for linear programming (LP). Thus, although there are only four variables involved in this model (four decision variables), there are 20(=4) decisions variables!6. This is clearly a problem without feedback, and therefore can be solved by branch and bound algorithms. We shall not attempt to go into the details of such algorithms here, but rather focus on the issue of maintaining real-time decisions. 7. The obvious solution with just one input involves only one decision variable, making this problem easily solvable with branch and bound algorithms. The other solutions are very difficult to solve because they involve two input variables each. The first requires many branches to handle all possible combinations of inputs, whereas the others can be handled by very few branches that become extremely long as the number of variables increases. Both are problems without feedback. cfa1e77820
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