Multi-agent Based Integration of Scheduling Algorithms

ncy or operation robustness.

 We may call a scheduling system that uses multi-agent technique as Multi-agent Scheduling System (MASS). By reviewing some important literatures, we find that MASS can be divided into two types:

 1)     Entity-type MASS

Agents in such MASS map physical entities in real-life systems as jobs and resources (machine, conveyance, storage, etc.). The major feature of such MASS is the reciprocity between resource agents and job agents. Every agent has intention of itself, goal and benefit. They are capable of self-advancement and self-control. They can also be distinguished from environmental information and then take action. Resource agents and job agents, as supplier and customer in market, achieve their maximal benefits and system goals through negotiation or transaction.

Research of Entity-type MASS is very plentiful. Lin et al.[1] used agents to response functions and entities (machine, job, database, etc.) of manufacturing system in their framework. And they used mark-like model to realize negotiation among agents. Ramos[2] also put forward a scenario that compose of resource agents and job agents. Gomes et al.[3]  view a MASS as an three level organization. Agents are signed different roles and functions depending on their position within the structure of the system. Agents of the low level are classified resource agents and job agents. Ouelhadj et al.[4] defined an “actor” architecture where agents is associated with particular functions which are distributed over resource agents and use contact net protocol for dynamic scheduling. Rabelo et al.[5] studied multi-agent based scheduling in virtual enterprise environments on the base of HOLOS scheduling system, which is a framework devoted to derive “instance” of agile scheduling system. 

2)     Process-type MASS

 Predominant agents in such MASS are called process agents. They map processes that realize a function [6], a computation [7], an activity [8], etc. Each process agent can only solve part of a problem. Different agents work together by collaboration to achieve system’s goal, as people coming from different fields to a team will do.

 Unlike Entity-type MASS that mainly composes of resource agents and job agents, Process-type MASS has no typical architecture.  There is much difference among researches of such system by now. Lau[6] defined a MASS for FMS scheduling, which is capable of individual learning and group learning. Agents in the syst

em are scheduling models that have ability of predictive scheduling and making reaction toward environment or other agents. Morikawa et al.[7] use agent maps genetic algorithm in his research of scheduling in process of CIM. The whole process of solving problem is divided into several stages. Each agent responses one stage. They work one by one. One agent gets input from upriver agents and output result to downriver agents. Gary Knotts[8] present a multi-agent scheduling method to solve multimode, resource-constrained project scheduling problem. Agents map activities of project.

 Baker[9] reviewed most scheduling algorithms and proved that they can be used into multi-agent heterarchy. So we consider to integrating more scheduling algorithms into one framework to a

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