Abstract
A method of finding the optimum solution for a stochastic discrete-event system is described. A simulation model of the system is first built and then used to train a neural network metamodel. The optimisation process consists of using the metamodel to find an approximate optimum solution. This solution is then used by the simulation as the starting point in a more precise search for an optimum. The approach is demonstrated with an example that finds the optimum number of kanbans needed to control a manufacturing system.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
