An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem

Flexible job-shop scheduling problem (fjsp) is very important in many research fields such as production management and combinatorial optimization. the fjsp problems cover two difficulties namely machine assignment problem and operation sequencing problem. in this paper, we apply particle swarm optimization (pso) algorithm to solve this fjsp problem aiming to minimize the maximum completion time criterion. various benchmark data taken from literature, varying from partial fjsp and total fjsp, are tested. experimental results proved that the developed pso is enough effective and efficient to solve the fjsp. our other objective in this paper, is to study the distribution of the pso-solving method for future implementation on embedded systems that can make decisions in real time according to the state of resources and any unplanned or unforeseen events. for this aim, two multi-agent based approaches are proposed and compared using different benchmark instances.