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flexibility and robustness. Increasing demands of the final consumers, for example, re-
quire structural modifications in the distribution part of the supply network in order to
fulfill those demands: Additional storage capacity has to be allocated and even com-
pletely new channels of product distribution must be established. Thus, the structures
in that part the supply network must be refined, i.e., additional or alternative options of
business relationships must be instantiated.
This is but an example for the dynamics in logistics that is further aggravated by
the
openness
of those systems [1]: Not only consumer demand changes as well as un-
foreseen failures of scheduled operations may happen (leading to the need of dynamic
replanning and reallocation of resources), but the logistics market itself may alter. New
competitors as well as new customers may enter, causing further changes in demand,
prices, and requirements of products and services. These developments evoke the neces-
sity for each participant to constantly adapt his relationships to customers and suppliers
in order to secure market shares and to fulfill the customers' demands. Such an adap-
tion, furthermore, affects other business relationships within the network, requiring an
extended refinement of supply partnerships therein.
Thus, modeling and operating supply networks with multiagent systems requires the
agents' ability to establish organizational configurations that allow for efficient oper-
ation, while being flexible enough (i.e., alterable) to cope with the dynamics of lo-
gistics processes. Hence, self-organizing MAS become necessary which autonomously
arrange their structure in accordance with dynamically changing conditions. In this con-
text, self-organization is therefore considered as the emergent evolvement and modifica-
tion of organizational structures defining business relationships between supply network
partners.
3
Agent Coordination
In order to be able to autonomously coordinate their activities (e.g., to establish and
operate logistics networks), artificial agents have to interact with each other. For this
purpose, agent communication languages modeling speech acts between the agents are
commonly used [4,5]. Based on these speech acts, a range of interaction and negotia-
tion protocols have been developed which coordinate agent behavior. Patterns of inter-
action reflect relationships between the participants and, thus, express the structure of
the multiagent system. Vice versa, structuring a supply network, modeled as a MAS,
means defining channels and modes of agent communication.
A wide variety of different structuring paradigms for MAS has been proposed [7].
These structures range from strict hierarchies [12] to market-based methods [2]. The
former use centralized decision-making at the top and distributed processing of specific
tasks at the bottom; the latter are completely decentralized and rely on negotiations for
each single task rather than on any middle or long term relationships. These predefined
mechanisms differ in their ability to handle changing conditions as well as in their
necessary effort for coordinating the actions of a network's members [16]. Therefore,
the expected dynamics of the application domain must be estimated in order to make
use of them.
