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.

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

The paper presents a new approach for robots tasks allocation and scheduling in order to minimize the total execution time (makespan) of a mission while satisfying all the tasks constraints. This approach is implemented in a multi-agent architecture for remote control of heterogeneous multi-robot systems. The agents controlling the robots coordinate their actions and cooperate to achieve the assigned mission. The proposed approach is carried out in two phases. During the first phase, a negotiation process (based on the Contract-net protocol) is carried out between the agents of the system to allocate the tasks to the robot proposing the minimal execution time (cost). In the second phase, a local scheduling process based on priority rules is carried out by the corresponding agent for each allocated task in order to minimize its inoccupation (idle) time.

Multi-agent approach for task allocation and scheduling in cooperative heterogeneous multi-robot team: Simulation results

The negotiation between agent and multi-agent is an important method for solving the conflict and realizing the cooperation in the multi-agent system of manufacturing enterprise supply chain. In th...

https://journals.sagepub.com/doi/pdf/10.1177/1687814018783625

Negotiation model and tactics of manufacturing enterprise supply chain based on multi-agent:

In the multi-agent system of manufacturing enterprise supply chain, each node enterprise has different demands on the price, quality, cost, delivery time, quantity, response time, service conditions and other factors, that will cause lots of conflicts between supply chain partners, and negotiation is the basic means to solve these conflicts. In this paper, a multi-objective negotiation model will be presented, and then negotiating tactics and steps between purchasing agent and supplier agent will be discussed. Next, an example is discussed and simulated by computer technology for validating the negotiating model. It is helpful to save negotiation time and improve negotiation efficiency, and optimize the manufacturing enterprise supply chain management. Based on this, manufacturing enterprise can maximize the benefits and improve the operational efficiency of supply chain.

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Research on Negotiation of Manufacturing Enterprise Supply Chain Based on Multi-agent

Holonic manufacturing systems (HMS) provide a flexible and decentralized manufacturing environment to accommodate changes dynamically. This paper presents a framework to model and control HMS based on Petri net and MAS theory. A Time Petri net (TPN) model was proposed to achieve this goal. A TPN represents a set of established contracts among the agents in HMS to fulfill an order. A scheduling architecture which integrates TPN models and AI techniques is proposed. By introducing dynamic individuals into the reproducing pool randomly according to their fitness, a variable population-size genetic algorithm is presented to enhance the convergence speed of GA. Based on the Novel GA and the particle swarm optimization (PSO) algorithms, a Hybrid PSO-GA algorithm (HPGA) is also proposed in this paper. Simulation results show that the proposed method is effective for the optimization problems.

Zeng Xiao-qing

Papers

Multi-agent Learning Negotiation Research in Virtual Enterprise Based on Contract Net

Multi-agent-based Cooperation in Virtual Enterprises