Considering the gent-based modeling and mapping in m anufacturing system, in this paper, some system models are described, which are including: Domain Based Hierarchical Structure (DBHS), Cascading Agent Struc ture (C...Considering the gent-based modeling and mapping in m anufacturing system, in this paper, some system models are described, which are including: Domain Based Hierarchical Structure (DBHS), Cascading Agent Struc ture (CAS), Proximity Relation structure (PRS), and Bus-based network structure (BNS). In DBHS, one sort of agent individually delegates Domain Agents, Res ources Agents, UserInterface Agents and Gateway Agents and the other one is a br oker of tasks and process flow. Static agents representing a subsystem may itsel f be an agent network and should learn as the mobile agents to deal with new sit uation. Mobile agents move around the network domains taking advantage of th e resources to fulfill their goals. In CAS, we use Unified Modelling Language (U ML) to build up the agent-based manufacturing system It is said Enterprise agen t (main agent) has factory agents together with some directly jurisdictional wor kshop agents, cell agents, and individual resource agents. Likewise, factory age nt has workshop agents together with some directly jurisdictional cell agents an d individual resource agents, and so on. In PRS, the resources agents are locate d together by it function and abilities. There is only one agent acts as the tas k-announcer. The communication just occurs among the Proximity Relational agent s. In BNS, It is very similar with the society of human being connected with a n etwork, some agents, such as ’cost calculating’, are just cope with the matter- of-fact job. And some agents run as the individual resources which can negotiat e with each other and advertise a necessary message within the whole domain or g iven group of agents. The administration just relies on the individual addre ss of agents and the group ID code of agents.展开更多
This paper introduces a multi-agent system which i nt egrates process planning and production scheduling, in order to increase the fle xibility of manufacturing systems in coping with rapid changes in dynamic market a...This paper introduces a multi-agent system which i nt egrates process planning and production scheduling, in order to increase the fle xibility of manufacturing systems in coping with rapid changes in dynamic market and dealing with internal uncertainties such as machine breakdown or resources shortage. This system consists of various autonomous agents, each of which has t he capability of communicating with one another and making decisions based on it s knowledge and if necessary on information provided by other agents. Machine ag ents which represent the machines play an important role in the system in that t hey negotiate with each other to bid for jobs. An iterative bidding mechanism is proposed to facilitate the process of job assignment to machines and handle the negotiation between agents. This mechanism enables near optimal process plans a nd production schedules to be produced concurrently, so that dynamic changes in the market can be coped with at a minimum cost, and the utilisation of manufactu ring resources can be optimised. In addition, a currency scheme with currency-l ike metrics is proposed to encourage or prohibit machine agents to put forward t heir bids for the jobs announced. The values of the metrics are adjusted iterati vely so as to obtain an integrated plan and schedule which result in the minimum total production cost while satisfying products due dates. To deal with the optimisation problem, i.e. to what degree and how the currencies should be adj usted in each iteration, a genetic algorithm (GA) is developed. Comparisons are made between GA approach and simulated annealing (SA) optimisation technique.展开更多
文摘Considering the gent-based modeling and mapping in m anufacturing system, in this paper, some system models are described, which are including: Domain Based Hierarchical Structure (DBHS), Cascading Agent Struc ture (CAS), Proximity Relation structure (PRS), and Bus-based network structure (BNS). In DBHS, one sort of agent individually delegates Domain Agents, Res ources Agents, UserInterface Agents and Gateway Agents and the other one is a br oker of tasks and process flow. Static agents representing a subsystem may itsel f be an agent network and should learn as the mobile agents to deal with new sit uation. Mobile agents move around the network domains taking advantage of th e resources to fulfill their goals. In CAS, we use Unified Modelling Language (U ML) to build up the agent-based manufacturing system It is said Enterprise agen t (main agent) has factory agents together with some directly jurisdictional wor kshop agents, cell agents, and individual resource agents. Likewise, factory age nt has workshop agents together with some directly jurisdictional cell agents an d individual resource agents, and so on. In PRS, the resources agents are locate d together by it function and abilities. There is only one agent acts as the tas k-announcer. The communication just occurs among the Proximity Relational agent s. In BNS, It is very similar with the society of human being connected with a n etwork, some agents, such as ’cost calculating’, are just cope with the matter- of-fact job. And some agents run as the individual resources which can negotiat e with each other and advertise a necessary message within the whole domain or g iven group of agents. The administration just relies on the individual addre ss of agents and the group ID code of agents.
文摘This paper introduces a multi-agent system which i nt egrates process planning and production scheduling, in order to increase the fle xibility of manufacturing systems in coping with rapid changes in dynamic market and dealing with internal uncertainties such as machine breakdown or resources shortage. This system consists of various autonomous agents, each of which has t he capability of communicating with one another and making decisions based on it s knowledge and if necessary on information provided by other agents. Machine ag ents which represent the machines play an important role in the system in that t hey negotiate with each other to bid for jobs. An iterative bidding mechanism is proposed to facilitate the process of job assignment to machines and handle the negotiation between agents. This mechanism enables near optimal process plans a nd production schedules to be produced concurrently, so that dynamic changes in the market can be coped with at a minimum cost, and the utilisation of manufactu ring resources can be optimised. In addition, a currency scheme with currency-l ike metrics is proposed to encourage or prohibit machine agents to put forward t heir bids for the jobs announced. The values of the metrics are adjusted iterati vely so as to obtain an integrated plan and schedule which result in the minimum total production cost while satisfying products due dates. To deal with the optimisation problem, i.e. to what degree and how the currencies should be adj usted in each iteration, a genetic algorithm (GA) is developed. Comparisons are made between GA approach and simulated annealing (SA) optimisation technique.
