Multi-Agent Model for a distributed LogisticSystemGuide Words: Distributed logistic system, multi-agent system.Abstract:Problems approached by logistic system are typically complex in particular the planningof a distributed logistic system which is a complex process implying several constraints. Among theseconstraints we can mention: cooperation between the different organizational entities of the system,the hold in consideration of the diversity of the nature of the product routed and the category of theclient targeted. This paper introduces a new Multi-Agent modeling approach; our model represents adistributed logistic system. Flows are forwarded from a zone to the other one by way of a strategictransport. The needs in flows (medicines flow, clothes flow, foods flow...) are indicated by informationsystem placed in every zone. We want to optimize the routing of these flows from one zone to anotheras well as to satisfy the needs in every zone, knowing that these needs are different according toseveral criteria among which we mention: features of the geographic zone, the nature of the stocks, thestricken individuals in every zone. Indeed, these problems bring us to study several approaches:i Multi-Agent systemii Optimization methodsili Fuzzy logic approachiv Statistics Estimators.In this paper we propose the Multi-Agent technology and the fuzzy estimators as potentialsolutions for the resolution of thiskind of problemsI.INTRODUCTIONMilitary origin concept, the logistics made its entry in the enterprises, about thirty year ago. Itfirst appeared in the sector of the big distribution and the automobile industry. First attached to thetransport or the production, it became a fully-fledged function in the middle of the years 1970 [6],[8]The introduction ofthelogistics within the production system is born out of a need of structuringand supervision of these systems through the organization, the rationalization, hierarchization and thecoordination of the set of its flows.Nowadays, logistic companies must stand in a constant gait ofevolution to remain competitive, inorder to answer to waits and to client's needs. Systems corresponding of such exigencies must
Multi-Agent Model for a distributed Logistic System Guide Words：Distributed logistic system, multi-agent system. Abstract： Problems approached by logistic system are typically complex in particular the planning of a distributed logistic system which is a complex process implying several constraints. Among these constraints we can mention: cooperation between the different organizational entities of the system, the hold in consideration of the diversity of the nature of the product routed and the category of the client targeted. This paper introduces a new Multi-Agent modeling approach; our model represents a distributed logistic system. Flows are forwarded from a zone to the other one by way of a strategic transport. The needs in flows (medicines flow, clothes flow, foods flow.) are indicated by information system placed in every zone. We want to optimize the routing of these flows from one zone to another as well as to satisfy the needs in every zone, knowing that these needs are different according to several criteria among which we mention: features of the geographic zone, the nature of the stocks, the stricken individuals in every zone. Indeed, these problems bring us to study several approaches: i Multi-Agent system ii Optimization methods iii Fuzzy logic approach iv Statistics Estimators. In this paper we propose the Multi-Agent technology and the fuzzy estimators as potential solutions for the resolution of this kind of problems. I. INTRODUCTION Military origin concept, the logistics made its entry in the enterprises, about thirty year ago. It first appeared in the sector of the big distribution and the automobile industry. First attached to the transport or the production, it became a fully-fledged function in the middle of the years 1970 [6],[8]. The introduction of the logistics within the production system is born out of a need of structuring and supervision of these systems through the organization, the rationalization, hierarchization and the coordination of the set of its flows. Nowadays, logistic companies must stand in a constant gait of evolution to remain competitive, in order to answer to waits and to client’s needs. Systems corresponding of such exigencies must
maintain an elevated flexibility level. So, the flexible logistic system of production imposesconstraints of reliability very severe and the least dysfunction of the system can affect the process ofmanufacture. The mastery and the resource management of the logistic system of production areessential to support aflexibleand effectiveprocess ofproduction.The multi -agents system offer a setting of modeling and simulation of the logistic system ofproduction while proposing to represent their elements, their behaviors and their interactions directlyunder the shape of computer entities having their own autonomy.In this article, we will describe, in a first time, a distributed logistic system. After a briefdescription of this system, we will approach its complexity as well as these limits and we will proposea solution based on the multi- agents approach through the management of the set of resources of thelogistic system considered.Thereafter, we are going to adopt a new resolution approach based on the holonic agents at whichwe will apply the fuzzy estimators in the goal to have a better optimization of the distributed logisticsystem.IIPROLEMATICThe management of the flows of a Distributed Logistic System (DLS) spreads on several zones oftreatment while leaving from the supplier of resources and the product to arrive to the customer. Therouting of the flows through the zones is a complex process submitted to several constraints.The difficulty of communication between the different zones, that is an essential element for thepipeline of the information and the management of the products circulating through this chain, presentthe constraints to which the distributed system must make face.Another shutter of the general problematic of our DLS, that is just as complicate that the difficultyof communication between the zones is the optimization of the flows.We chose like tool ofoptimization: the statistical estimators, they will have the rule to inform us on the quantities ofexpanded resources, the speeds of routing of the flows...In current time, the dynamic treatment of the information by the estimators is integrated in aprocess of help at the decision of the DLS. We achieve some models to make appear of the balancesand attractors between the elements, to control the dynamics or even the viability of a system, to helpat the decision and to predict the possible evolutions.2.1 DISTRIBUTED LOGISISTIC SYSTEM (DLS)The distributed logistic system can be considered according to two different ways. We can
maintain an elevated flexibility level. So, the flexible logistic system of production imposes constraints of reliability very severe and the least dysfunction of the system can affect the process of manufacture. The mastery and the resource management of the logistic system of production are essential to support a flexible and effective process of production. The multi -agents system offer a setting of modeling and simulation of the logistic system of production while proposing to represent their elements, their behaviors and their interactions directly under the shape of computer entities having their own autonomy. In this article, we will describe, in a first time, a distributed logistic system. After a brief description of this system, we will approach its complexity as well as these limits and we will propose a solution based on the multi- agents approach through the management of the set of resources of the logistic system considered. Thereafter, we are going to adopt a new resolution approach based on the holonic agents at which we will apply the fuzzy estimators in the goal to have a better optimization of the distributed logistic system. II PROLEMATIC The management of the flows of a Distributed Logistic System (DLS) spreads on several zones of treatment while leaving from the supplier of resources and the product to arrive to the customer. The routing of the flows through the zones is a complex process submitted to several constraints. The difficulty of communication between the different zones, that is an essential element for the pipeline of the information and the management of the products circulating through this chain, present the constraints to which the distributed system must make face. Another shutter of the general problematic of our DLS, that is just as complicate that the difficulty of communication between the zones is the optimization of the flows. We chose like tool of optimization: the statistical estimators, they will have the rule to inform us on the quantities of expanded resources, the speeds of routing of the flows. In current time, the dynamic treatment of the information by the estimators is integrated in a process of help at the decision of the DLS. We achieve some models to make appear of the balances and attractors between the elements, to control the dynamics or even the viability of a system, to help at the decision and to predict the possible evolutions. 2.1 DISTRIBUTED LOGISISTIC SYSTEM (DLS) The distributed logistic system can be considered according to two different ways. We can
maintain an anticipated action that leads us to treat a flows pushed of a supplier toward a customer orif we consider the problem in the opposite direction, from where the notion of drawn flows of thecustomertoward thesupplier.The system that we are going to study is represented by the figure 1.Figure1,Distributedlogisticsystem2.2DIFFICULTIESOFCOMMUNICATIONBETWEENTHEZONESAs we note it in the figure 1, this system presents several zones of treatment of flows andresources. The idea is to route the flows leaving from a regrouping zone via intermediate zones toreach the terminal zones (zones of distribution to the customers). In the terminal zones the flows areconsumed variable-speed.An optimal routing requires a communication between these differentzones.In the problem that interests us, cooperation and the relations of responsibility are essential. Theindependent treatment of the zones can generate redundancies of information or erroneous data sinceevery zone has the incomplete information and capacities limited to solve the problem. These limitswill be able to influence therefore on the global behavior of the system.For this reason, the coordination of the zones proves to be a key element for the reliability of thesystem. Thus, every actor of the chain is going to be able to play its own rule in the zone to which it isaffected on the one hand and associate to the other neighboring zone actors on the other hand.Lately the Multi - Agent modeling have been adopted for the resolution of the problems due to thecomplexity of the distributed logistic system.3MULTI-AGENTSYSTEM
maintain an anticipated action that leads us to treat a flows pushed of a supplier toward a customer or if we consider the problem in the opposite direction, from where the notion of drawn flows of the customer toward the supplier. The system that we are going to study is represented by the figure 1. Figure 1. Distributed logistic system 2.2 DIFFICULTIES OF COMMUNICATION BETWEEN THE ZONES As we note it in the figure 1, this system presents several zones of treatment of flows and resources. The idea is to route the flows leaving from a regrouping zone via intermediate zones to reach the terminal zones (zones of distribution to the customers). In the terminal zones the flows are consumed variable-speed. An optimal routing requires a communication between these different zones. In the problem that interests us, cooperation and the relations of responsibility are essential. The independent treatment of the zones can generate redundancies of information or erroneous data since every zone has the incomplete information and capacities limited to solve the problem. These limits will be able to influence therefore on the global behavior of the system. For this reason, the coordination of the zones proves to be a key element for the reliability of the system. Thus, every actor of the chain is going to be able to play its own rule in the zone to which it is affected on the one hand and associate to the other neighboring zone actors on the other hand. Lately the Multi - Agent modeling have been adopted for the resolution of the problems due to the complexity of the distributed logistic system. 3 MULTI-AGENT SYSTEM
Object for a long time of research in artificial intelligence, the Multi - Agents System form aninteresting type of modeling of societies, have very large application field, active until liberal arts.AMulti - Agent System (MAS) is a set of agents situated in a certain environment and interactingbetween them according to a certain organization [4]An agent is an entity characterized by the fact that it is, at least partially, autonomous. This maybe a process, a robot, a human being, etc.So the solution is gotten thanks to the individual behaviors and interactions. Then the Multi-Agents System represent a new approach for the analysis, the conception and the implantation of thecomputing complex systems [1]The MAS are characterized then by: [2]iA partial perception ofthe environment for every agent,ii The limited expertise that don't allow them to solve the problem individually,iiA decentralization of information,iv The treatments in asynchronous balanced mode3.1PRINCIPLE OF MODELING OFAMULTI-AGENTSA MAS is a network of agents (solvers) weakly coupled that cooperate to solve the problems thatpass the capacities or every agent's individual knowledge. These agents are autonomous and can be ofheterogeneous natures [2].The modeling is going to consist at the establishment of a certain number of distinctions toanalyze this complex reality.The first distinction consists of the separation of the structure of thesystem of agents actual of the one of the domain in which it operates. In our example, we consider asraising of the domain the notions of flows and zones of treatment.We will carry our attention on the modeling of a MAS. While following Ferber [3],[5], we choosethree essential components:i Models of agents (taking into account the individuality of the agents);ii Models of interactions (choose under shape of rules: a same agent capable to play severaldifferentrules);iii Organizational models (representing the global properties of the society of agents)3.2MULTI-AGENTARCHITECTUREPROPOSEDThe Multi - Agent System proposed is constituted of four types of agents: pilot Agent Agpregrouping resources agent AgR, intermediary zone agent Agl, and terminal zone agent AgT [9] The
Object for a long time of research in artificial intelligence, the Multi - Agents System form an interesting type of modeling of societies, have very large application field, active until liberal arts. A Multi - Agent System (MAS) is a set of agents situated in a certain environment and interacting between them according to a certain organization [4]. An agent is an entity characterized by the fact that it is, at least partially, autonomous. This may be a process, a robot, a human being, etc. So the solution is gotten thanks to the individual behaviors and interactions. Then the Multi – Agents System represent a new approach for the analysis, the conception and the implantation of the computing complex systems [1]. The MAS are characterized then by: [2] i A partial perception of the environment for every agent, ii The limited expertise that don't allow them to solve the problem individually, iii A decentralization of information, iv The treatments in asynchronous balanced mode. 3.1 PRINCIPLE OF MODELING OF A MULTI-AGENTS A MAS is a network of agents (solvers) weakly coupled that cooperate to solve the problems that pass the capacities or every agent's individual knowledge. These agents are autonomous and can be of heterogeneous natures [2]. The modeling is going to consist at the establishment of a certain number of distinctions to analyze this complex reality. The first distinction consists of the separation of the structure of the system of agents actual of the one of the domain in which it operates. In our example, we consider as raising of the domain the notions of flows and zones of treatment. We will carry our attention on the modeling of a MAS. While following Ferber [3],[5], we choose three essential components: i Models of agents (taking into account the individuality of the agents); ii Models of interactions (choose under shape of rules: a same agent capable to play several different rules); iii Organizational models (representing the global properties of the society of agents). 3.2 MULTI-AGENT ARCHITECTURE PROPOSED The Multi - Agent System proposed is constituted of four types of agents: pilot Agent Agp, regrouping resources agent AgR, intermediary zone agent Ag1, and terminal zone agent AgT [9] The
table1willincludethenature,theruleandeveryagent'sinteractionwithitsneighboursDeslghationruleInteractionspilot AgentTosupervise-AgkApthem variousstructures oftheAgnAgnDLS-AgrsAgmAgeregroupingStorage of the-Aspoftheresources andflows ready withresources-Agia --Agnthe routingAgentAgaAgent zoneToreceive-ABintemediaryresources andflow of the zone-Agriof regrouping ofAgrmthe resourcesand to distributeAgrthem towardsABa+variots finalAgnzonesfirul zoneAgnToreceive-A8Agertresources iandflowofthe-AgriintermediateAgrm!Zone Direetzone in contactwith Client(destinataire)AgtsTableI description ofthe agentsEvery agent is responsible for its zone, it can answer at a request coming either of the Pilotagent, either of an agent that is hierarchically superior to him.AgRAgiAgTsuppliersrecipientFigure.2Communication AgentsFigure 3 shows that agents of a same zone can cooperatebetween them to exchange theinformation, and also to cooperate with the agents of a neighbor zoneWe focus to optimize this communication on achieving organizations of agents from where theconceptofholonicagents
table 1 will include the nature, the rule and every agent's interaction with its neighbours. Table I description of the agents Every agent is responsible for its zone, it can answer at a request coming either of the Pilot agent, either of an agent that is hierarchically superior to him. Figure.2 Communication Agents Figure 3 shows that agents of a same zone can cooperate between them to exchange the information, and also to cooperate with the agents of a neighbor zone. We focus to optimize this communication on achieving organizations of agents from where the concept of holonic agents