Optimization of Job Shop Scheduling Problem with Grey Wolf Optimizer and JAYA Algorithm

The design of optimal job schedules on parallel machines with finite time is a combinatorial optimization problem and plays a crucial role in manufacturing and production facilities. In this work, we evaluate the performance of two recently proposed computational intelligence techniques, Grey Wolf Optimizer (GWO) and JAYA on ten datasets arising from five job shop scheduling problems with parallel machines. The computational results have shown GWO to be efficient than the JAYA algorithm for problems with higher number of orders and machines.