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ased simulation involves agents and aims at maximize or minimum the fitness functions. Agent behavior is defined by embedded schema, which is both interpretive and action-oriented in nature (Kevin, 2002).Agent-based simulation is quite different from the two methods above. The above methods aims on variables and event, while agent-based simulation focus on organizational participants, such as companies, teams and employees and their behavior. Researchers regard this method as “sentient beings” in computer, and take them with interpretive and behavioral in their nature. While this method requires the discipline of complexity science, with the fast development of complexity science, it seems to become the dominant method in the future (Anderson,2000).
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2.2 Discrete Event Simulation

Discrete event model can be traced back to 1960s when Gordon (1961) first came up with this concept, and subsequently evolved it for GPSS and applied to IBM.Law and Kelton (1982) said that the organization system simulation being discrete, dynamic and stochastic can be characterized as discrete event simulation.
It is best suited for the situation when a collection of variables and corresponding states could fully represent the given system, and along with the occurrence of random events,the value of the variables states would change a finite number of timesin a rule-oriented manner (Andrei and Alexei, ).Andrei and Alexei () conceived discrete event model as an approach illustrating entity flow andresource allocation grounded on a set of entities, resource and governing rules.

About 100 commercial tools are existed that are used for building the discrete event model according to Andrei and Alexei’s () calculation. Some of them are aimed at special niches, such as service industry, manufacturing and logistics sector. The rest are designed for general purpose. Underneath the distinct user interface caused by specialization, all of these different tools have discrete event simulation engine to mimic the real situation.There a

Entities are passive objects that represent people, parts, documents, tasks, messages, etc. They travel through the blocks of the flowchart where they stay in queues, are delayed, processed, seize and release resources, split, combined, etc.

3. Fusion of JIT and Discrete-Event SimulationJIT与离散事件仿真的融合

Due to the technical obstacles existing in the implementation of JIT production, simulation as a useful tool was introduced to identify the weakness and risk of manufacturing operation by many experts (Fernando, 2002).
For example, to tackle the design problem of a chemical company, Welgama and Mills (1995) have changed its traditional system to JIT system by means of simulation.
Lars Holst (2001) mentioned in his book:
“Discrete-event simulation thus provides analysis, description and evaluation capabilities of systems, and if successfully applied can support collaborative work across organizational boundaries and thereby improve information and communication.”
Furthermore, Hollocks (1992) has listed the general advantages of using DES in manufacturing content. Manufactures will benefit from reducing risk and operation cost, shortening production cycle, improving customer service. Detty (2000) has proved that discrete event computer simulation is able to help with adopting