A hybrid algorithm based on particle swarm optimization and simulated annealing to holon task allocation for holonic manufacturing system

Manufacturing is currently undergoing a revolutionary transition with focus shifting from mass production to mass customization. This trend motivates a new generation of advanced manufacturing systems that can dynamically respond to customer orders and changing production environments. It is becoming increasingly important to develop control architectures that are reconfigurable and fault tolerant. A holonic manufacturing system (HMS) is a system of holons that can cooperate to achieve a common goal or objective. The holonic organization enables the construction of very complex systems that are efficient in the use of resources. This paper focuses on the dynamic re-configuration and task optimization of holonic manufacturing systems (HMS). The concept of dynamic virtual clustering is extended to the control process of a holarchy or holonic organization. A task-oriented clustering mechanism and a corresponding optimization algorithm are presented as an efficient approach to the holonic control in the HMS domain. The mediator-based dynamic virtual clustering mechanism is presented firstly. Then a negotiation strategy based on the contract net protocol is proposed for cooperative action among holons. Finally, a hybrid algorithm based on particle swarm optimization (PSO) and simulated annealing (SA) for holon task allocation is described to support the optimum organization of a holarchy. The hybrid algorithm combines the high speed of PSO with the powerful ability to avoid being trapped in local minimum of SA. Simulation results show that the proposed model and algorithm are effective.