Job shop optimization through multiple independent particle swarms

This study examines the optimization of the job shop scheduling problem (JSP) by a search space division scheme and use of the meta-heuristic method of particle swarm optimization (PSO) to solve it. The job shop scheduling problem (JSP) is a well known huge combinatorial problem from the field of deterministic scheduling. It is considered the one of the hardest in the class of NP-hard problems. The objective is to optimally schedule a finite number of operations to a finite number of resources while complying with ordering constraints. The particle swarm optimization algorithm (PSO) is a new meta-heuristic optimization method modeled after the behavior of a flock of birds in flight. "particles" are initialized in the search space of a particular problem by assigning them a position, which represents a solution to the objective function, and a velocity. They "fly" through the search space with out direct control, but are given both a cognitive personal component and a global or social component of the best positions (thereby solutions) in space. The PSO algorithm is considered a very fast algorithm and is emerging as a widely studied widely used algorithm for optimization problems. The proposed method uses this meta- heuristic to solve the JSP by assigning each machine in a JSP an independent swarm of particles.