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logistics performance indicator corresponds to these values. However, it shows slightly
higher fluctuations, which are caused by the agents always ordering the minimal amount
of products. This can lead to supply shortfalls even in the case of only partially refused
orders
3
.
These results reflect the capability of generating social order as it is observed by Dit-
trich et al. [3] in their original model. Thus, changing their interpretation of a dyadic
encounter between individuals to a more general understanding of double contingency
regarding alter a whole community of entities allows for transferring the properties of
their basic approach to a multiagent scenario. Therefore, an application of expectation-
based self-organization in MAS based on Luhmann's notion of double contingency is
possible without the requirement for a reduction of interaction to pairwise communica-
tion processes or the need for extended agent observation activities.
Concerning the logistics application, the results demonstrate that the expectation-
based approach to self-organizing agent interaction is not only capable of efficiently
structuring and operating the modeled supply network. In fact, it is even able to estab-
lish an optimal configuration of agent communication channels (one to one relation-
ships), leading to similar performance rates compared to the benchmark arrangement
in the course of the simulation. As the agents occasionally explore alternative inter-
action options, delivery failures occur from time to time, leading to slightly less than
optimal customer satisfaction and utilization rates due to the minimal order size and
inventory capacities. Regarding these measures, safety stocks and increased order sizes
may compensate the disturbances to further improve logistics performance.
To summarize, the feedback loop of agent observation and expectation-based selec-
tion of operations shows the ability to reach near optimal results without the require-
ment for a priori assumptions about agent characteristics
4
or repetitive negotiations
between several agents. Due to the dynamics of the logistics domain and the black box
nature of agents in open MAS, it is not generally possible to optimally prestructure a lo-
gistics network. In order to overcome this problem, expectation-based self-organization
provides a promising coordination method for supply systems, being adaptive as well
as operating efficiently.
6
Conclusions
This paper has identified the requirement for both adaptive and efficient supply net-
works. As multiagent systems provide a means for decentralized modeling of logistics
networks, possible coordination techniques have been investigated in terms of their ap-
plicability to address the challenges in supply network organization. In this context,
3
When exploring alternative sets of suppliers, an agent may split its orders over, e.g., two sup-
pliers. If one of the suppliers refuses that order and the other one sends a delivery message,
the production process utilization suffers from a supply shortage. The customer satisfaction,
however, is less affected by this partially refused order. Therefore, the product consumption
varies to a higher extend than the customer satisfaction.
4
In contrast to that, e.g., determining the benchmark configuration requires knowledge of the
agents' production and consumption rates.
