DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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Manuscript for IEEE Transactions on Automatic Control 14 D i (t) as a quantitative representation of the component’s position. D i (t) is the required time gap between the system’s and the component’s completion times at time t. Each component needs to complete at less than or equal to T-D i (t) to keep the completion time T. Components without successors have depth equal to 0 but components with successors have positive depths. A component a can keep its depth if its predecessors’ depth is D a (t) plus its total service time for the last arriving tasks in the worst case. So, a component i’s depth to keep the depths of its all successors is the maximal of the required depths from its successors represented as: D i( t ) = max[ Da( t ) + f a( va ) / MRAa ( t )] , (10) a∈i ∑ b∈a in which i denotes successors of component i and a predecessors of component a. Though it is possible to refine the naïve decision model by incorporating the depths as variables, the model complexity increases because each component’s constraint will be intertwined with the decision variables of all connected components. So, we simply estimate components’ depths through the decisions used at the last control point. At each control point each successor informs its predecessors of required depths and each predecessor chooses the maximal one as its depth. As a result, we can consider the depth as constant rather than variable so that the refined model has no increase in complexity. We call the refined model in (11) as stable decision model. If we don’t consider the depth, i.e., D i (t)=0, the model becomes equivalent to the naïve decision model. Also, the stable decision model becomes an exact CPM/PERT formulation as a special case found in project management literature when one consider D i (t) as variable.
Manuscript for IEEE Transactions on Automatic Control 15 Stable decision model Max s.t. ∑ i∈A [ R ( t ) + L ( t ) f ( v )] / MRA( t ) ≤ T − t − D ( t ) v L ( t )v i i i(min) ≤ v i i − CCT(T ) i ≤ v i i(max) i i for all for all i ∈ A i ∈ A (11) 6.3 System behavior under stable decision model To observe system behavior under the stable decision model, we experimented with the example network described in Fig. 3. Fig. 6 and 7 show the resultant behavior of the system, in which the decisions T * and v * i are stable. The stability indicates that the inefficiency of the naïve decision model is removed as a result of improved prediction accuracy. 1150 1120 Optimal T 1090 1060 1030 1000 0 200 400 600 800 1000 1200 Time Fig. 6. Behavior of T * under stable decision model
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Ultra*Log PSU/IAI Final Report for
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Contents Contents .................
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Executive Summary Ultra*Log is a De
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2.3 Gnanasambandam, N., Lee, S., Ku
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6 Characterization and analysis of
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timizing simultaneously the link de
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where ∆ 1 (j) ≥ 0 and ∆ 2 (i,
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Table 2. GA Results Agent N A D max
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connected component, in which a pat
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Random Small-world Scale-free with
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Growth mechanisms Start with a smal
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Table 2. The proposed network’s c
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DMAS Controller. The functional uni
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1000 500 0 -500 -1000 0.2 0.4 0.6 0
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tems. Proceedings of the Second Joi
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Proceedings of the 1st Open Cougaar
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Proceedings of the 1st Open Cougaar
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Page 44 and 45: 1 SITUATION IDENTIFICATION USING DY
Page 46 and 47: 3 3.2 Behavior In SSC society an ag
Page 48 and 49: 5 All the behavior parameters may n
Page 50 and 51: Estimating Global Stress Environmen
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Page 56 and 57: interactions as a dynamical system
Page 58 and 59: ehavior states under varying system
Page 60 and 61: Extensive testing and validation of
Page 62 and 63: 5 Conclusions and Future Research T
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Page 68 and 69: 6 stresses well. The Warehouse 1 ag
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Page 72 and 73: Table 1: Notation Symbol Descriptio
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Page 110 and 111: 6 S UB i ∑ = P LI / LI . (16) i p
Page 112 and 113: 8 policies while underutilizing in
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Page 118 and 119: component is a member of one of the
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Page 122 and 123: limit of large number of tasks. If
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Manuscript for IEEE TRANSACTIONS ON
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Coordinating Control Decisions of S
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directly. It has coarser scale. It
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Understanding Agent Societies Using
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• For tasks, the UID of the direc
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for tracking the dependencies betwe
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within the monitored enclave. With
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Figure 1. Agent Hierarchy in CPE So
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Figure 3. The World Model CPY Agent
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Table 2. TechSpecs: Infrastructure
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terms of the average waiting times
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Acknowledgements The work described
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key realization to tackle this prob
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are or ignored perturbations. The e
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sustained oscillations. If the inst
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solved by biological systems for li
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non-linear and non-stationarity. Ne
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D k : Outflow from high priority qu
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the system starts to perform self-s
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sustained in a supply chain. Resour
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which are necessary to make good mo
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focusing on the computational compl
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line of research that deals with ne
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ealizable. But the inherent complex
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Min, H. and Zhou, G., 2002, Supply
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In the rest of this paper we report
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shows relatively irregular behavior
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