Due date assignment and new dispatching rules for dynamic job shop scheduling

In this thesis, regression and neural network based due date assignment models have been proposed for dynamic job shop scheduling problem. Proposed methods have been compared with other methods available in the literature for dynamic shop scheduling problems in a test environment and for this, various of MATLAB simulation models have been developed. Simulation models have been verified by comparing with analytical results for this environment. The models give competitive results comparing other models. In addition, two new dispatching rules have been proposed for the test environment those consider both jobs and operations features. One of these competitive dispatching rules gives the best results comparing other dispatching rules that exist in the literature.