Skip to main content

Logistic Optimization

Logistic Optimization

Arie Dubi

ISBN: 978-0-470-66580-0

Nov 2018

300 pages

Select type: Hardcover

$120.00

Product not available for purchase

Description

Unlike other sciences, there is no common or underlying equation or approach for predicting the future in systems engineering. This leads to two questions; firstly, how can engineers reliably predict the future behaviour of a given system design, and secondly, once methods are found, how can the logistics and support provided to the system be optimized so that the best possible performance at the lowest possible cost is guaranteed? Trying to answer the above questions, previous books on the subject cover analytic solutions and models of systems but fail to cover the application of the Monte Carlo method to reliable models. In this arena, the insufficient nature of analytic methods brought about a situation in which models were constructed to fit the ‘available method’ rather than to reflect the true nature of the reality at hand. With this limied ability to predict the future of the system, ‘optimization’ has not been properly addressed. Genetic and annealing algorithms are elaborated upon for those unfamiliar with them. The author’s first book (Monte Carlo Applications in System Engineering, Wiley 1999) guided practicing reliability engineers through predicting future behaviour in system designs. The foundations were laid and now this new book follows on by suggesting new workable and proven methods to address the optimization problem in realistic systems. It covers the problem from its very foundations up to the practical detailed methods and solutions so that both the engineer and student can utilize the methods correctly and reliabily optimize the systems they design and operate. The book is divided into three sections; the first gives an introduction to systems modeling, the second analyses logistic optimization, and the third discusses applications (such as the energy, chemical and aviation industries) and models.