Applications of Modern Heuristic Methods

Many problems which arise in industry and commerce, including scheduling, strategic planning, routing and location prove extremely difficult to solve optimally. The mathematical models produced may require an unacceptable timescale to solve on even the most powerful of modern super computers. In practice, it is only feasible to use some method which produces a good, but not necessarily optimal solution. Heuristic methods exploit techniques which are intuitively reasonable, and which, by experience, can be shown to work acceptably well. Some modern heuristic methods exploit recent developments in artificial intelligence and, to some extent, attempt to mimic natural processes. This is the case for tabu search, simulated annealing, genetic algorithms and their hybrids. These methods provide fast and effective ways of solving some of the most important and difficult problems arising in industry. There is a skill to be acquired if they are to be used to best effect and this book, by covering a number of case studies, attempts to impart this skill.
This book should be of interest to IT professionals, corporate strategists, producting engineers, general management, as well as the academic community researching in the fields of management science, artificial intelligence and OR. There are two companion volumes to this book, "Neural Networks" and "Probability Reasoning and Bayesian Belief Networks", which individually stand alone, but combined form a set treating a broad but integrated spectrum of techniques and tools for undertaking complex tasks.
 
Many problems which arise in industry and commerce, including scheduling, strategic planning, routing and location prove extremely difficult to solve optimally. The mathematical models produced may require an unacceptable timescale to solve on even the most powerful of modern super computers. In practice, it is only feasible to use some method which produces a good, but not necessarily optimal solution. Heuristic methods exploit techniques which are intuitively reasonable, and which, by experience, can be shown to work acceptably well. Some modern heuristic methods exploit recent developments in artificial intelligence and, to some extent, attempt to mimic natural processes. This is the case for tabu search, simulated annealing, genetic algorithms and their hybrids. These methods provide fast and effective ways of solving some of the most important and difficult problems arising in industry. There is a skill to be acquired if they are to be used to best effect and this book, by covering a number of case studies, attempts to impart this skill.
This book should be of interest to IT professionals, corporate strategists, producting engineers, general management, as well as the academic community researching in the fields of management science, artificial intelligence and OR. There are two companion volumes to this book, "Neural Networks" and "Probability Reasoning and Bayesian Belief Networks", which individually stand alone, but combined form a set treating a broad but integrated spectrum of techniques and tools for undertaking complex tasks.