DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

2.2 Role of Predictors
As mentioned earlier, when deployed in a battlefield
this agent society is subject to several stresses related to
varying wartime loads, kinetic and information warfare.
These stresses may result in node failures, denial of
service and other network related faults that will result
in the lack of communications between agents. In this
decentralized application the inability of customer/
suppliers to make/meet requests can significantly impact
the performance of the various operational units.
In this setting, predictors can play an important role
in maintaining the supply chain connectivity, while
network related faults are restored. The predictors can
provide the ability to approximate the “expected
behavior” by continuing to make appropriate
demand/supply projections.
We focus on two classes of predictors (i) a customer
predictor that resides at the supplier agent and estimates
the customer’s demand when communications are lost
(ii) a supplier predictor inserted at the customer agent
that predicts the allocation results for the tasks generated
by the supplier. As shown in Figure 2, the customer
predictor residing on FSB forecasts each customer’s
(ARBN and INFBN) demand when communications are
lost. In a similar fashion the supplier predictor residing
on INFBN agent predicts the supplier’s behavior. These
agents use the predicted values and continue execution
of their functionality.
Depending on the accuracy of the predictors,
typically predicted states are not identical to the actual
states. Thus when communications are restored, and
actual demands/supply values are available, any errors
between estimated and actual values will need to be
resolved. This process, termed as “reconciliation”,
requires any surplus tasks to be rescinded and new tasks
added for any shortfalls. The predictors in turn will need
to update their models based on available data.
2.3 Predictor Design
2.3.1 Customer Predictor
The customer predictor is implemented in the form of
two plugins. One plugin is used during the planning
mode where it collects the data about the customersupplier
relationship and items involved. It also collects
the Optempo of these items. Another plugin is used
during the execution mode. This Plugin monitors the
demand from the customer and predicts the demand
when there is a communication loss. Figure 3 shows the
framework of the customer predictor during the
execution mode.
Figure 3. Customer Predictor
2.3.2 Supplier Predictor
This predictor is also built in the form of two plugins.
One plugin resides at the supplier and other resides at
the customer. The Plugin at the supplier periodically
sends the snapshots of the inventory levels for each item
in all the supply classes to the Plugin at the customer.
The plugin at the customer uses this information for
predicting the allocation results of the demand task. The
design of the supplier predictor is represented
diagrammatically in Figure 4.
Figure 4. Supplier Predictor
2.4 Predictor Algorithms
Based on the nature of the supply chain dynamics
(uncertainty in demand, model complexity), duration of
communication loss, computational requirements
different approaches for the predictors were investigated.
These ranged from dynamical systems, to classification
theory to traditional forecasting [3,4,6,7,8]. While some
of our research [5] and prototypes indicates that we may
get better prediction results by using a non parametric
method such as support vector machine, radial basis
function neural network etc., from an
implementation/computational perspective this becomes
impractical. The primary reason is that as the society
size scales it becomes increasingly difficult to generate
historical patterns for each agent and classify its