DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

More documents

Recommendations

Info

Acknowledgements The work described here was performed under the DARPA UltraLog Grant#: MDA972-1-1-0038. The authors wish to acknowledge DARPA for their generous support. References [1] Arena. www.arenasimulation.com. Rockwell Automation. [2] Cougaar open source site. http://www.cougaar.org. DARPA. [3] Ultralog program site. http://www.ultralog.net. DARPA. Figure 7. Maneuver Plan Freshness using Simulation [4] Web-ontology (webont) working group. http://www.w3.org/2001/sw/WebOnt/. [5] G. Bolch, S. G. H. de Meter, and K. S.Trivedi. Queuing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications. John Wiley and Sons, Inc., 1998. The main contributions of this work is that we have identified that TechSpecs could serve as good template that can guide MAS design and model development in a concurrent fashion. We have codified the static attributes of MAS in such a way that QNMs may be constituted from distributed information, especially in realtime. This technique for adaptivity by using a model on demand to predict trends in QoS may be helpful in building survivable systems. [6] M. Brinn and M. Greaves. Leveraging agent properties to assure survivability of distributed multi-agent systems. Proceedings of the Second Joint Conference on Autonomous Agents and Multi-Agent Systems (Poster Session), 2003. [7] A. Cassandra, D. Wells, M. Nodine, and P. Pazandak. Techspecs: Content, issues and nomenclature. Technical Report, Telcordia Inc. and OBJS Inc., 2003. Currently, work is ongoing to identify an appropriate method of representation of TechSpecs that would have some reasoning and deduction capabilties such as OWL [4]. A module that could convert this representation of TechSpecs into queueing models automatically would be useful in this endeavor. An approach that would identify alternate choices for performance improvement is also necessary. Finally, a controller that actually uses the analysis from the QNMs to optimize the global utility is also being pursued. 64
Supply Chain Network: A Complex Adaptive Systems Perspective AMIT SURANA † , SOUNDAR KUMARA ‡* , MARK GREAVES**, USHA NANDINI RAGHAVAN In this era where on one hand, information technology is revolutionizing almost every domain of technology and society, on the other hand the “complexity revolution” is occurring in science at a silent pace. In this paper we look at the impact of the two, in the context of supply chain networks. With the advent of information technology, supply chains have acquired complexity almost equivalent to that of biological systems. However, one of the major challenges that we are facing in supply chain management is the deployment of coordination strategies that lead to adaptive, flexible and coherent collective behavior in supply chains. The main hurdle has been the lack of the principles that govern how supply chains with complex organizational structure and function arise and develop, and what organizations and functionality is attainable, given specific kinds of lower-level constituent entities. The study of Complex Adaptive Systems (CAS), has been a research effort attempting to find common characteristics and/or formal distinctions among complex systems arising in diverse domains (like biology, social systems, ecology and technology) that might lead to better understanding of how complexity occurs, whether it follows any general scientific laws of nature, and how it might be related to simplicity. In this paper we argue that supply chains should be treated as a CAS. With this recognition, we propose how various concepts, tools and techniques used in the study of CAS can be exploited to characterize and model supply chain networks. These tools and techniques are based on the fields of nonlinear dynamics, statistical physics and information theory. 1. Introduction Supply chain is a complex network with an overwhelming number of interactions and interdependencies among different entities, processes and resources. The network is highly nonlinear, shows complex multi-scale behavior, has structure spanning several scales, and evolves and selforganizes through a complex interplay of its structure and function. This sheer complexity of supply chain networks, with inevitable lack of prediction makes it difficult to manage and control them. Furthermore, the changing organizational and market trends requires the supply chains to be highly dynamic, scalable, reconfigurable, agile and adaptive: the network should sense and respond effectively and efficiently to satisfy customer demand. Supply chain management necessitates that decisions made by business entities take more global factors into considerations. The successful integration of the entire supply chain process now depends heavily on the availability of accurate and timely information that can be shared by all members of the supply chain. Information Technology with its capability of setting up dynamic information exchange network has been a key enabling factor in shaping supply chains to meet such requirements. A major obstacle remains, however in the deployment of coordination and decision technologies to achieve complex, adaptive, and flexible collective behavior in the network. This is due to the lack of our understanding of organizational, functional and evolutionary aspects in supply chains. A † Department of Mechanical Engineering, The Massachusetts Institute of Technology, Cambridge, MA, 02139, email: surana@mit.edu ‡ The Harold and Inge Marcus Department of Industrial & Manufacturing Engineering, University Park, PA 16802, email: skumara@psu.edu. * Corresponding Author **IXO, DARPA, 3701 North Fairfax Drive, Arlingon, VA 22203, 1714, mgreaves@darpa.mil
Page 1 and 2:
Ultra*Log PSU/IAI Final Report for
Page 3 and 4:
Contents Contents .................
Page 5:
Executive Summary Ultra*Log is a De
Page 8 and 9:
2.3 Gnanasambandam, N., Lee, S., Ku
Page 10 and 11:
6 Characterization and analysis of
Page 12 and 13:
timizing simultaneously the link de
Page 14 and 15:
where ∆ 1 (j) ≥ 0 and ∆ 2 (i,
Page 16 and 17:
Table 2. GA Results Agent N A D max
Page 18 and 19:
connected component, in which a pat
Page 20 and 21:
Random Small-world Scale-free with
Page 22 and 23:
Growth mechanisms Start with a smal
Page 24 and 25:
Table 2. The proposed network’s c
Page 26 and 27:
¡ ¡ ¢ £ ¤ ¥ ¦ £ § ¨ © ¥
Page 28 and 29:
© © ¤ ¢ ¨ ¤ £ ¦ ¨ © §
Page 30 and 31:
DMAS Controller. The functional uni
Page 32 and 33:
1000 500 0 -500 -1000 0.2 0.4 0.6 0
Page 34 and 35:
tems. Proceedings of the Second Joi
Page 36 and 37:
Proceedings of the 1st Open Cougaar
Page 38 and 39:
Page 40 and 41:
Page 42 and 43:
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
Page 52 and 53:
chaotic deterministic time series.
Page 54 and 55:
100% 1 95% 2 14 15 64% TAO 4 64% 62
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
Page 64 and 65:
2 to function critically even under
Page 66 and 67:
4 points into a corresponding inner
Page 68 and 69:
6 stresses well. The Warehouse 1 ag
Page 70 and 71:
© £ ¨ ¥ ¤ § ¢ ¥ ©
Page 72 and 73:
Table 1: Notation Symbol Descriptio
Page 74 and 75:
4 Conclusions and Future Work 4.1 C
Page 76 and 77:
O, U Stresses Physical/Infrastructu
Page 78 and 79:
Manuscript for IEEE Transactions on
Page 80 and 81:
Page 82 and 83:
Page 84 and 85:
Page 86 and 87:
Page 88 and 89:
Page 90 and 91:
Page 92 and 93:
Page 94 and 95:
Page 96 and 97:
Page 98 and 99:
Page 100 and 101:
Page 102 and 103:
Page 104 and 105:
Page 106 and 107:
2 discuss problem domain and in Sec
Page 108 and 109:
4 t Si t ∫ + ( ) i ) t When RA i
Page 110 and 111:
6 S UB i ∑ = P LI / LI . (16) i p
Page 112 and 113:
8 policies while underutilizing in
Page 114 and 115:
architecture based on both the Grid
Page 116 and 117:
to machines (network topology) and
Page 118 and 119:
component is a member of one of the
Page 120 and 121:
denotes the immediate predecessors
Page 122 and 123:
limit of large number of tasks. If
Page 124 and 125:
Min-min heuristic algorithm Step 1:
Page 126 and 127: We set up eight different experimen
Page 128 and 129: 0. 4 8057 8237 0.5 6439 6635 0.6 53
Page 130 and 131: pp. 191-200. [3] I. Foster, C. Kess
Page 132 and 133: Technology, Cambridge, MA, 1995. [2
Page 134 and 135: Manuscript for IEEE TRANSACTIONS ON
Page 156 and 157: Coordinating Control Decisions of S
Page 158 and 159: directly. It has coarser scale. It
Page 160 and 161: Understanding Agent Societies Using
Page 162 and 163: • For tasks, the UID of the direc
Page 164 and 165: for tracking the dependencies betwe
Page 166 and 167: within the monitored enclave. With
Page 168 and 169: Figure 1. Agent Hierarchy in CPE So
Page 170 and 171: Figure 3. The World Model CPY Agent
Page 172 and 173: Table 2. TechSpecs: Infrastructure
Page 174 and 175: terms of the average waiting times
Page 178 and 179: key realization to tackle this prob
Page 180 and 181: are or ignored perturbations. The e
Page 182 and 183: sustained oscillations. If the inst
Page 184 and 185: solved by biological systems for li
Page 186 and 187: non-linear and non-stationarity. Ne
Page 188 and 189: D k : Outflow from high priority qu
Page 190 and 191: the system starts to perform self-s
Page 192 and 193: sustained in a supply chain. Resour
Page 194 and 195: which are necessary to make good mo
Page 196 and 197: focusing on the computational compl
Page 198 and 199: line of research that deals with ne
Page 200 and 201: ealizable. But the inherent complex
Page 202 and 203: Min, H. and Zhou, G., 2002, Supply
Page 204 and 205: In the rest of this paper we report
Page 206: shows relatively irregular behavior
show all

DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?