Solving the Flexible Job Shop Scheduling Problem Using a Discrete Improved Grey Wolf Optimization Algorithm

The flexible job shop scheduling problem (FJSP) is of great importance for realistic manufacturing, and the has been proven to be NP-hard (non-deterministic polynomial time) because its high computational complexity. To optimize makespan critical machine load FJSP, a discrete improved grey wolf optimization (DIGWO) algorithm proposed. Firstly, combined with random Tent chaotic mapping strategy heuristic rules, hybrid initialization presented improve quality original population. Secondly, update operator (DGUO) designed by discretizing hunting process so that can solve FJSP effectively. Finally, an adaptive convergence factor introduced global search ability algorithm. Thirty-five international benchmark problems as well twelve large-scale FJSPs are used test performance proposed DIGWO. Compared algorithms in recent literature, DIGWO shows better solution accuracy at different scales.