A Self-learning Particle Swarm Optimization Algorithm for Dynamic Job Shop Scheduling Problem with New Jobs Insertion

The dynamic job shop scheduling problem (DJSSP) is an NP-hard optimization challenge, characterized by unpredictable events such as new job arrivals during scheduling. Our goal is to enhance search efficiency by integrating a learning system into the Particle Swarm Optimization (PSO) algorithm. This integration involves updating the inertia weight and incorporating a local search to refine search directions, termed PSO-IWLS (PSO with Inertia Weight Local Search). The PSO-IWLS has demonstrated superior performance compared to state-of-the-art approaches on large-scale benchmarks, excelling in both computation time and solution quality.