A Lagrangian Dual based approach

The Job Shop Scheduling (JSS) problem can be viewed as an integer optimization program with linear objective function and linear, disjunctive constraints. The Lagrangian-based deep learning model does not necessarily produce feasible schedules directly. The model presented below is used to construct solutions that are integral, and feasible to the original problem constraints. The experimental setting, as defined by the training and test data, simulates a situation in which some component of a manufacturing system'slows down', causing processing times to extend on The model training follows the selection of parameters presented in Table 3.Parameter V alue Parameter V alue Epochs 500 Batch Size 16 Learning rate [1 . Finally, Constraints (23) capture Kirchho ff's Current Law and Constraints (24) capture Ohm's Law.