Job-Shop Scheduling using Random Key Encoding Scheme Particle Swarm Optimization

Job-Shop Scheduling Problem (JSSP) is one of extremely hard problems because it requires very large combinatorial search space and the precedence constraint between machines. The traditional algorithm used to solve the problem is the branch-and- bound method, which takes considerable computing time when the size of problem is large. We propose a new method for solving JSSP using Particle Swarm Optimization (PSO) and demonstrate its efficiency by the standard benchmarks of job-shop scheduling problems. Some important points of PSO are how to represent the schedules as particles and to design the operators for the representation in order to produce better results.