Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Inspection for circuit board assembly
Management Science
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Inspection allocation in manufacturing systems using stochastic search techniques
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Reinforcement learning approach to goal-regulation in a self-evolutionary manufacturing system
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Hi-index | 12.06 |
One of the most important characteristics of reentrant production systems is that the products are manufactured layer-by-layer, so it is difficult to inspect some defects after they are covered by the next layer. This study proposes a genetic algorithm (GA) approach that is very suitable for solving the inspection allocation problem, because the codes used in the chromosome of the GA approach are exactly the same as the representation of the inspection allocation policy for workstations in the production system. Meanwhile, this study shows better performance than the researches done in the literature and is very much closer to the optimization method based on complete enumeration. In addition, a discussion regarding GA parameters is performed to suggest proper parameters used for various production systems. The result obtained in this study is highly practical, extensible and applicable, so it can serve as a production planning tool to solve the inspection allocation problem in reentrant production systems.