ADAPTIVE MULTIAGENT SYSTEMS APPLIED ON TEMPORAL LOGISTICS NETWORKS. P. Knirsch (1) andi.j.timm (1)

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1 ADAPTIVE MULTIAGENT SYSTEMS APPLIED ON TEMPORAL LOGISTICS NETWORKS P. Knirsch (1) andi.j.timm (1) (1) Logistics Research Group, University of Bremen, P.O. Box , Bremen, Germany {knirsch, ABSTRACT Logistics is gaining increasing economical and theoretical interest. This trend is enforced by a radical change of mainly tayloristically and nationally focussed economy to a worldwide distributed one. In this framework, the relevancy of coordinating information flows and hence well-defined interfaces between participants is growing. Moreover, the range of cooperation between producers lasts from short-term to long-term duration. This yields the existence of temporal logistics networks that can persist for an arbitrary period of time. A modern approach in computer science to model distributed systems is the use of multiagent systems. This paper addresses to the problem of adequacy of their application in the field of temporal logistics networks. Especially dynamical aspects within the structure of logistics networks and environmental change can be modeled naturally. Agents are able to recognize changes and to update their own view of their environment and to recreate software systems partially automatically. This is due to their ability to sense their environment, to communicate, and their strict modularity. So this technique does not only support the first design but also its latter management and control. Because of its flexibility concerning the number of participants it enables the transition from alliance-based to open, in particular market-based, systems. INTRODUCTION The control and management of information flows in logistics is one of the main challenges in the global competition of our days. Especially short term cooperations between companies are defining new and difficult demands that have to be met by modern information (processing) systems. Another important factor within the global market is the group of consumers that plays a key role and in consequence companies have to concentrate on its demands, as well. This often leads to the manufacturing of products in small lot sizes and highly customized. According to our research activities, multiagent systems are well-suited to model networks in the domain of logistics. Former information and decision-support systems were intended to manage internal information flows along supply chains (Sadeh and Smith, 1993). But they can adept to structural changes of the whole setting neither automatically nor easily. This paper deals with the question of how modern software engineering technologies, i.e. the use of multiagent systems, influences the organization of businesses positively, especially concerning interbusiness transactions.

2 Due to the inner structure of agents, their application yields a more flexible handling of dynamical aspects. Agents do not only know about the system s configuration but even notice events occurring and react accordingly. This leads to a magnificent benefit considering that monolithic information systems would have to be adapted and reconfigured manually each time structural changes occur and are recognized. Furthermore, monolithic systems are centrally organized and therefore do not address to distributed organizations like logistics networks. For instance, most monolithic systems assume that all information are available, non-ambiguously and at any time. But a logistical scenario does not satisfy this assumption. Multiagent systems are supposed to become important, because of their natural benefits they provide for the specification and implementation of distributed systems: The ability to act and persist in dynamic environments, i.e. to recognize changes within the network and react accordingly, fast communication in particular multilateral - that is used for synchronization, automated negotiations and auctions, the ability to deal with distributed data bases that is used for information gathering and filtering, and the automated support of standard procedures. In the following, we as well present an approach to specify and visualize such networks, as an idea of how changes can be specified and handled flexibly. TEMPORAL LOGISTICS NETWORKS The global competition forces companies to face new challenges, especially those concerning the customer-side. Challenges like world-wide available and flexibly manufactured products and flexible services have to be faced by each company, if it wants to stay competitive. The result is a concentration on producers key competence and the trend to cooperate. Outsourcing and merging ranging from short to long term, make enterprises change significantly. Products are not only made by a single company but by a cooperation network of companies. But even these networks do not have to be static during the production processes, i.e. structural changes are quite normal. This yields the introduction of the term temporal logistics networks. Of course, these changing environments have a dramatic impetus on the participating actors. On the one hand they have to realize them and react to them sensibly. On the other hand they also could be part of it (Dinges, 1996). For cooperating within temporal logistics networks detailed exchange of information between the participating companies is necessary. But missing standardization has led to completely different, non-interoperating IT-environments used by the cooperating companies. If the organizational structure of the logistics networks is changing or the demands made, then a lot of information have to be spread throughout the whole organization which might evolve successively (Fox and Smith, 1995). To guarantee that the production process is not disturbed severely, it is necessary that information are carried around quite fast. They have to reach safely and understandably distant places. But not only geographical distances are crucial, also cultural, linguistic, political distances separate people and enterprises. On the technical level different protocols, platforms, and architecture have to be handled. For the increasing precision of production processes, as it is demanded for just-in-time concepts, and the reduction of unnecessary storekeeping, controlling the production within the networks gets more important, i.e. to give feedback about the state of the production and its deviation. Hence, communication is inevitable and it is not trivial

3 since a lot of participants, even changing numbers of participants, interact. Today manufactures often only use old-fashioned technologies, like telephone calls, fax, posted documents and forms, and conferences with all their known disadvantages. In Figure 1 we have pictured a simple logistics network consisting of three companies cooperating all having substructures specified. Rectangles represent companies, filled boxes their inner structure. The communication between these interacting entities is symbolized by arcs in-between. It is easy to see that if we remove Company B from the network (dashed box) several communication relations have to be cut. This poses problems because the communication partners have to notice that their counterparts have gone. For reestablishing the connections companies have to know about the inner structure of their corresponding partners. Figure 1. Example of a logistics network Most of the information flows are standard procedures, e.g. ordering, time scheduling, queries, receipt acknowledgements that could as well be automated. To overcome the disadvantages of complex communication, we suggest the usage of agents - software objects, capable of taking tasks and resolving them autonomously. MULTIAGENT SYSTEMS Modern software engineering has to face the problem that the amount of information available is constantly growing, information have to be carried around: worldwide, fast, and secure. Furthermore, in the same degree as the computation power is increasing, problems that are considered get more complex. Consequently, the classical approach in software-engineering building monolithic software systems is not longer adequate in many cases. As for other areas like economics, a tendency to distributed systems can be identified in the last decades within software engineering. Considering the previously mentioned influences, multiagent systems gain increasing interest (Bradshaw, 1997). Multiagent systems, introduced in the late 1980ies, consist of distributed computational entities, the so-called agents (Jennings and Wooldridge, 1998). They are comparable to objects but are capable of sensing their environment and reacting according to the situation they find. Agents are goal-oriented, i.e. they get tasks and pursue them subsequently. Because agents are situated within a multiagent system with limited resources and because they work in parallel, they are in need of social behavior, i.e. they must be able to communicate and cooperate to reach their goals. Therefore, the most important feature of a multiagent system is the communication between its agents. The communication language determines the expressive power and therefore the problemsolving abilities, and the efficiency. Furthermore, they must have knowledge of

4 themselves and the existence and competence of other agents. This enables them to act in open systems (Burckhard, 1994). In particular, they need the capability to recognize agents entering or leaving the system. Internally, agents have their own views of their environment, and they need to adapt to and learn from the changes that occur at runtime. Because we want to determine if multiagent systems are adequate means for modeling organizations and temporary cooperation relations we have to identify characteristics: Regarding the different application areas of multiagent systems it is obvious that finding an appropriate general definition of multiagent systems covering all aspects is almost impossible. Moreover, it seems to be adequate to determine demands which must be fulfilled by the domain and the multiagent systems. In this context H. J. Müller (Müller, 1997) proposes three requirements to be satisfied by the domain to ensure that a multiagent systems can fruitfully be applied. First, the system should be characterized by natural distributivity, i.e. when mapping a distributed domain to a model, it is essential to keep up the distributivity, or the distributivity lays within the task structure. Second, the processes or objects which should be implemented with the help of a multiagentsystem are in need of complex interactions, e.g. they have to negotiate or exchange complex information. This is demanded by the term of flexible interaction. Thethird presupposition for the application of multiagent-systems is the demand for a dynamic environment. Dynamics does not only mean changing data of the environment but also changing the structure of the whole system, e.g. partners are giving up the cooperation or joining it. These assumptions directly correspond to temporal networks as previously described. MULTIAGENT SYSTEMS IN TEMPORAL LOGISTICS NETWORKS The focus of this paper lays on the control and management of the above introduced temporal logistics networks. We propose an agent-oriented approach for the automated support (see, i.e., Cantamessa, 1997). It should not only control and manage logistics networks but also integrate and optimize the distributed processes. In many companies standard production planning and control software (ppc) is used for an support of the internal organization and control of processes. The logistics networks are modeled manually. Based on these models implementations of distributed ppc systems have to be made. As these networks are only persisting temporarily constant, the structures are frequently changing and the models have to be adapted. Using classical methods, the costs of managing and controlling such networks are significant. One main cost factor can be found in the weak standardized interfaces between the companies. Most changes in the logistics network require a new definition of interfaces of different companies. Companies which participate in more than one logistics networks get numerous queries for information exchange. The multiagent system approach can help to overcome all these expensive bottlenecks of the management and control. Figure 2 shows an agent-oriented design of an enhanced logistics network compared to the one depicted above. Internal structures of the companies must not be addressed by others and therefore are hidden. The agents, depicted as rhombuses, represent parts of the companies that exclusively inter-operate with other agents using perception and communication, each again representing a company. This enables agents to react to changes within the logistics network; for example Company D's agent is about to join the network. Then it needs to understand as well the instructions from company-sides as the communication language within the multiagent system. Hence, two kinds of interfaces are necessary, symbolized as triangles, two of which building a rhombus, in the figure. One possible reaction to any

5 modification of the system would be to alert the users, another more sophisticated would be an autonomous adaptation. If Company B is removed from the scenario, as shown in the figure (dashed box), it is obvious that there is only the agent to be removed instead of explicit communication channels between companies. Figure 2. Agents mediating a temporal logistics network Therefore, with the help of multiagent systems the change from a strictly organized structure to an open system can be done. It supports the transition from alliances to open, market-based systems, because in such multiagent systems it is not necessary that all companies provide different services. Manual recreation of the whole software system as a result of a structural, i.e. organizational, change can be avoided by using agents that are able to communicate, e.g. on the one hand they can instruct a newly inserted part of the logistics network, or on the other hand they can recognize agents leaving the system. Not only for updating the logistics network multiagent systems are advantageous but also in the design phase they are helpful. Because of the strict modularity of agents, models can be generated using top-down design based on the refinement of agents. This also provides a security advantage, as only access mechanisms of interfaces between different companies are exchanged, i.e. a common communication language, and not detailed knowledge of the company's internal structure. Note, as we saw in the sections above, we intuitively choose graphical representations when specifying systems, especially logistics networks. Therefore, it seems quite natural to use graphical notions to report on the operational behavior of those systems. In our approach, we use rule-based graph transformation (Rozenberg, 1997) to meet the requirements of temporal networks. We are able to specify the overall system structure, the dynamics within the structure, i.e. temporary networks, and the operational behavior of the system, i.e. the system at runtime. The basic operational entity of our approach is a graph rule, a rule which transforms one graph into another by exchanging local parts of the graph. This is only a very simplified notion of what is done when applying a graph rule. There are two operational layers in which changes take place: On the one hand, agents manipulate the overall system structure, e.g. join or leave the multiagent system, on the other each agent has its own rules he tries to apply on the problem-representing graph in a special order. With help of graph rules, agents change the system's graphical representation. Hence, we do visual programming. Furthermore, using the application of the graph rules and the

6 referring parts of the graph the rule is applied to, we can protocol the system changing. For more details on distributed graph transformation refer to (Taentzer, 1996a). CONCLUSIONS This paper discusses the application of multiagent systems to temporal logistics networks. For a comprehensible, efficient design of temporarily changing networks, we propose a new approach based on graph transformation. With the help of multiagent systems, the effort of changes can be minimized, and because of the modularization, the system s structure keeps comprehensible. The graphical approach enables the user of modeling the domain in a visual, intuitive manner. The answer to the question why multiagent systems have not yet reached a major position within the organization of networks can be found in a lack of standardization, as well in the theoretical framework as in practical applications, in defective technical presupposition, and in unsatisfied security issues. The situation concerning the technical presupposition has changed a lot in the last decade, especially due to the spread of global networks. The here presented technologies seem to be well-suited concerning the domain of temporal logistics networks. Fast changing structures as in these networks profit from an approach which takes all kinds of dynamical aspects into account. The roughly presented concept of graph transformation-based multiagent systems is subject to further investigations in the graduate college for "Modeling, Controlling and Managing of Supply Chains" of the Logistics Research Group at the University of Bremen. REFERENCES Bradshaw J.M. (1997): Software Agents. MenloPark, CA: TheMITPress. Burckhard H.D. (1994), "Agent-oriented Programming for Open Systems" in Wooldridge M.J., Jennings N.R. (Ed.), Workshop on Agent Theory, Architectures, and Languages, Berlin: Springer. Cantamessa M. (1997): "Agent-based Modeling and Management of Manufactoring Systems". Computers in Industry, 34(1997), pp Dinges, Büttner (1996), "Effiziente Logistik durch Integration von Dienstleistern" in Little A.D. (Ed.), Management im vernetzten Unternehmen, Wiesbaden: Th. Gebler. Fox M.S., Smith S.F. (1984): "ISIS: A Knowledge-based System for Factory Scheduling". Expert Systems, 1(1), pp Jennings N.R., Wooldridge, M.J. (1998): Agent Technology: Foundation, Applications, and Markets, New York: Springer. Müller H.J. (1997): "Towards Agent Systems Engineering". International Journal on Data and Knowledge Engineering, Special Issue on Distributed Expertise, 23, pp Rozenberg G. (1997): Handbook of Graph Grammars and Computing by Graph Transformation, Vol. I: Foundations, Singapore: World Scientific. Sadeh N., Smith S.F. (1993): Knowledge-Based Supply Chain Management: An Overview of Ongoing Research at Carnegie Mellon University. Intelligent Coordination and Logistics Laboratory, Carnegie Mellon University, Internal Report. Taentzer G. (1996): Parallel and Distributed Graph Transformation: Formal Description and Application to Communication-Based Systems, PhD Thesis, Berlin: Shaker Verlag.