Optimization of Assembly Sequence

The assembly sequence is one of the most time consuming and expensive manufacturing activities. Assembly sequence affects many of product development design and production and is relevant to many life cycle issues of the product, so assembly sequence analysis should be part of early product design. The cost of assembly on an average is 10-30% of the manufacturing cost of a commercial product. The ratio between cost and performance of assembly has increased with respect to other types of the manufacturing process and in recent years, this fact has caused a growing interest by industry in this area. Robotic assembly which is comes under the assembly sequence and also comes under the automated assembly system incorporates the use of robots for performing the useful and time taking assembly tasks. A variety of optimization tools are available for application to problem. It is difficult to model the present as an n-p problem. Finding the best assembly sequence generation involves the conventional methods or soft computing methods by following the procedure of search algorithms. Optimization of a correct and stable assembly sequence is essential for automated, semi-automated or manual assembly systems. Assembly sequence affects flexible and advanced manufacturing system in many aspects such as use of tool, cost, time, layout of area etc. To solve this kind of problems or time consuming geometric reasoning in assembly sequence, this research proposes a method to determine stable assembly sequence. The objective of the present work is to stable, generate feasible and optimal assembly sequence satisfying the assembly constraint with minimum assembly cost. The present project aims at evolving an approach for generating assembly sequence using the evolutionary technique considering of the instability of assembly motion and/or directions. To elaborate the effectiveness of the method, one soft computing method is applied to generate the optimized sequence(s)

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering