Emerging optimization techniques in production planning and control (Q2713390)

This volume provides a comprehensive exposition of current knowledge concerning the adaptive memory programming (AMP) for grouping a number of generic optimization techniques for combinatorial problems. They include artificial neural networks, genetic algorithms, tabu search and ant systems. NEWLINENEWLINENEWLINEThe volume describes the techniques and algorithms of AMP with production planning and control applications. The author presents in a systematic manner the superiority of implementing the emerging optimization techniques over the existing heuristics. This is illustrated with real life problems. NEWLINENEWLINENEWLINEThe book is organized into 9 chapters. The first chapter gives an overview of AMP and production planning and control. The basic components of production planning system and of production control system are presented in Chapters 2 and 3, respectively. Chapter 4 presents back propogation networks, self-organizing map, adaptive resonance theory, and Hopfield networks. Chapter 5 deals with the basic theory of genetic algorithms and Chapter 6, tabu search theory. Chapter 7 considers ant systems and Chapter 8, simulated annealing with an application to cell formation problems. Chapter 9 discusses programming techniques and a source code of a memory-based optimization technique. NEWLINENEWLINENEWLINEThe production planning and control applications are taken from the contributions of the author in international journals, as well as from lecture-notes that he has prepared for teaching graduate courses in operations/production planning and control. Algorithms for each application are given. It is therefore possible for readers to adapt the algorithms for other areas of application. NEWLINENEWLINENEWLINEThis book provides an easily accessible, but concise, introduction to the subject. The book is well organized, is written in a careful and systematic way and contains a huge amount of new material. For these reasons this book is strongly recommended as an instructional material for graduate students. It will also serve as an excellent resource material for production planners and controllers, operaions researchers, management scientists, applied mathematicians and computer scientists.