Thermal Artificial Bee Colony Algorithm for Large Scale Job Shop Scheduling Problems

The job-shop scheduling problem (JSSP) is among the prominent issues in scheduling. Although swarm intelligence (SI) centered algorithms are executing effectively to solve JSSP, yet to find the optimum solution for large-scale JSSP instances is still an inspirational task. In SI algorithms, the artificial bee colony (ABC) algorithm is iconic for efficiently dealing with physical world optimization problems; however, its basic version may suffer from stagnation problem. A temperature-based solution search mechanism is mingled with ABC following the scout honeybee phase to overcome the above weakness. The proposed variant is designated as thermal ABC. Further, a discrete version of thermal ABC is designed to solve 105 large-scale instances of JSSP. The considered instances include 15 SWV, 40 DMU, and 50 TA instances. The obtained outcomes and statistical analysis validate the competitiveness of the proposed approach to solve large-scale JSSP.