This work proposes a concept of adaptive memory programming (AMP) for grouping a number of generic optimization techniques used in combinatorial problems. The features driving these optimization techniques include artificial neural networks, genetic algorithms, tabu search and ant systems.

This book proposes a concept of adaptive memory programming (AMP) for grouping a number of generic optimization techniques used in combinatorial problems. The same common features seen in the use of memory and a local search procedure drive these emerging optimization techniques, which include artificial neural networks, genetic algorithms, tabu search and ant systems. The primary motivation for AMP, therefore, is to group and unify all these techniques so as to enhance the computational capabilities that they offer for combinatorial problems encountered in real life in the area of production planning and control.The text describes the theoretical aspects of AMP together with relevant production planning and control applications. It covers the techniques, applications and algorithms. The book has been written in such a way that it can serve as an instructional text for students and those who are taking tuition on their own. The numerical examples given are first solved manually to enhance the reader's understanding of the material, and that is followed by a description of the algorithms and computer results. This way, the student can fully follow the material. The algorithms described for each application are useful to both students and practitioners in grasping how to implement similar applications in computer code using emerging optimization techniques.