A broker in an open e-marketplace enables buyers and sellers to do business with each other. Although a broker plays an important role in e-marketplaces, theory and guidelines for matching between buyers and sellers i...A broker in an open e-marketplace enables buyers and sellers to do business with each other. Although a broker plays an important role in e-marketplaces, theory and guidelines for matching between buyers and sellers in multi-attribute exchanges are limited. Therefore, a challenge for a broker＇s responsibility is how to maximize a buyer＇s total satisfaction degree as its goals under the consideration of trade-off between a buyer＇s buying quantity and price paid to a seller, and other attributes. To solve this challenge, this paper proposes an economic model-based matching approach between a buyer＇s requirements and a seller＇s offers. The major contributions of this paper are that （i） a broker can model a seller＇s price policy as per a buyer＇s buying quantity through communication between a broker and a seller; （ii） due to each buyer＇s different quantity demand, a broker models a buyer＇s satisfaction degree as per a buyer＇s buying quantity based on communication between a broker and a buyer; and （iii） to carry out a broker＇s matching processes, an objective function and a set of constraints are generated to help a broker to maximize a buyer＇s total satisfaction degree. Experimental results demonstrate the good performance of the proposed approach.展开更多
Agent-based scheduling refers to applying intelligent agents to autonomously allocate resources to jobs.Decentralized agent-based scheduling approaches have achieved good performance in open and dynamic environments b...Agent-based scheduling refers to applying intelligent agents to autonomously allocate resources to jobs.Decentralized agent-based scheduling approaches have achieved good performance in open and dynamic environments because the relationships of agents are flexible.For new jobs and resources and unexpected events,decentralized agents can respond adaptively and flexibly.Besides,decentralized approaches are easy to be extended because there is no central control agent that limits the scalability.However,decentralized approaches might have low efficiency in large-scale environments because behaviors of agents may be self-interested and competitive,due to their local views during decision making.When interacting with a large number of agents,each agent may spend a considerable amount of time on failed attempts before reaching the final agreements with other agents.To improve the efficiency of decentralized agent-based scheduling approaches in large-scale environments,and to keep the flexibility and adaptability of decentralized agents for the decision-making on scheduling,this paper provides a new agent-based adaptive mechanism for efficient job scheduling.A new type of agent named host agent is introduced to coordinate self-interested behaviors of agents without participating in the decision making of agents during job scheduling.The proposed mechanism was developed in JADE and tested in open and large-scale environments.The experimental results indicate that the proposed mechanism is effective and efficient in open and large-scale environments.展开更多
Computer based modelling and simulation has become useful tools to facilitate humans to understand systems in different domains, such as physics, astrophysics, chemistry, biology, economics, engineering and social sci...Computer based modelling and simulation has become useful tools to facilitate humans to understand systems in different domains, such as physics, astrophysics, chemistry, biology, economics, engineering and social science. A complex system is featured with a large number of interacting components （agents, processes, etc.）, whose aggregate activities are nonlinear and self-organized. Complex systems are hard to be simulated or modelled by using traditional computational approaches due to the complex relationships of components and distributed features of resources, and dynamic work environments. Meanwhile, smart systems such as multi-agent systems have demonstrated advantages and great potentials in modelling and simulating complex systems.展开更多
文摘A broker in an open e-marketplace enables buyers and sellers to do business with each other. Although a broker plays an important role in e-marketplaces, theory and guidelines for matching between buyers and sellers in multi-attribute exchanges are limited. Therefore, a challenge for a broker＇s responsibility is how to maximize a buyer＇s total satisfaction degree as its goals under the consideration of trade-off between a buyer＇s buying quantity and price paid to a seller, and other attributes. To solve this challenge, this paper proposes an economic model-based matching approach between a buyer＇s requirements and a seller＇s offers. The major contributions of this paper are that （i） a broker can model a seller＇s price policy as per a buyer＇s buying quantity through communication between a broker and a seller; （ii） due to each buyer＇s different quantity demand, a broker models a buyer＇s satisfaction degree as per a buyer＇s buying quantity based on communication between a broker and a buyer; and （iii） to carry out a broker＇s matching processes, an objective function and a set of constraints are generated to help a broker to maximize a buyer＇s total satisfaction degree. Experimental results demonstrate the good performance of the proposed approach.
文摘Agent-based scheduling refers to applying intelligent agents to autonomously allocate resources to jobs.Decentralized agent-based scheduling approaches have achieved good performance in open and dynamic environments because the relationships of agents are flexible.For new jobs and resources and unexpected events,decentralized agents can respond adaptively and flexibly.Besides,decentralized approaches are easy to be extended because there is no central control agent that limits the scalability.However,decentralized approaches might have low efficiency in large-scale environments because behaviors of agents may be self-interested and competitive,due to their local views during decision making.When interacting with a large number of agents,each agent may spend a considerable amount of time on failed attempts before reaching the final agreements with other agents.To improve the efficiency of decentralized agent-based scheduling approaches in large-scale environments,and to keep the flexibility and adaptability of decentralized agents for the decision-making on scheduling,this paper provides a new agent-based adaptive mechanism for efficient job scheduling.A new type of agent named host agent is introduced to coordinate self-interested behaviors of agents without participating in the decision making of agents during job scheduling.The proposed mechanism was developed in JADE and tested in open and large-scale environments.The experimental results indicate that the proposed mechanism is effective and efficient in open and large-scale environments.
文摘Computer based modelling and simulation has become useful tools to facilitate humans to understand systems in different domains, such as physics, astrophysics, chemistry, biology, economics, engineering and social science. A complex system is featured with a large number of interacting components （agents, processes, etc.）, whose aggregate activities are nonlinear and self-organized. Complex systems are hard to be simulated or modelled by using traditional computational approaches due to the complex relationships of components and distributed features of resources, and dynamic work environments. Meanwhile, smart systems such as multi-agent systems have demonstrated advantages and great potentials in modelling and simulating complex systems.
