DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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4 points into a corresponding inner product in high dimensional space without actual mapping. The principle of the mapping is based on Mercer’s theorem (Vapnik 1998). By doing so, the SVM overcomes non linear-separable cases. The selection of kernel functions depends on the problem. We should choose an appropriate function by performing experiments. Other control parameters for the SVM are the extra cost for errors (represented as C), the loss sensitivity constant (ε insensitivity ) and the maximum number of iterations. The extra cost for errors, C, is a cost assigned to training errors in non linear-separable cases. A larger C corresponds to assigning a higher penalty to errors (Burges 1998). The loss sensitivity constant (ε insensitivity ) represents the allowable error range for the prediction values. SVMs are basically developed as binary classifiers. Currently a lot of research is being done in the area of multi-class SVM. We use BSVM 2.0 which is the multi-class SVM program suggested by Hsu and Lin (2002). EXPERIMENT CONDITIONS We simulated the SSC under various stress conditions. 12 Stress conditions are made through the combination of the following factors; The number of retailer 1(zero, one, two), OPTEMPO of retailer 2 (LOW, HIGH), OPTEMPO of retailer 3 (LOW, HIGH). 219 data sets were used for training and 94 data sets were used for testing the prediction power from a total of 313 data sets. The conditions and number of experiments are shown in the table 1. Condition Retailer 1 Retailer 2 Retailer 3 Training Test Total 1 Zero LOW LOW 25 11 36 2 Zero HIGH LOW 19 8 27 3 Zero LOW HIGH 18 8 26 4 Zero HIGH HIGH 17 7 24 5 One LOW LOW 29 13 42 6 One HIGH LOW 17 7 24 7 One LOW HIGH 18 7 25 8 One HIGH HIGH 18 8 26 9 Two LOW LOW 21 9 30 10 Two HIGH LOW 12 5 17 11 Two LOW HIGH 13 6 19 12 Two HIGH HIGH 12 5 17 Grand Total - - - 219 94 313 Table 1. The stress condition and number of experiments TRAINING Through preliminary studies apart from the experiments tabulated above we found that all the stress conditions do not affect all the agents. Thus, we prepared different classification definitions for the training set depending on the agent (See the table 2). As the classification definitions are different for different agents, the input features are also different. The tasks used as input features in each agent are shown in table 3.
5 The option ‘multi-class bound-constrained support vector classification’ in BSVM 2.0 is selected. For others, we use default options of BSVM 2.0 such as the radial basis function with gamma = 1/(the number of input features). Agent name Retailer 2, Warehouse 2 Retailer 1 Factory 2, Supplier 2 Warehouse 1, Factory 1 Retailer 3, Distribution Center 2 Distribution Center 1, Supplier 1 Wholesaler 1 Class definition Class 1: condition 1,3,5,7,9,11 (Retailer 2 LOW) Class 2: condition 2,4,6,8,10,12 (Retailer 2 HIGH) Class 1: condition 1,2,3,4 (zero Retailer 1) Class 2: condition 5,6,7,8 (one Retailer 1) Class 3: condition 9,10,11,12 (two Retailer 1) Class 1: condition 1,3 (Retailer 2 LOW at zero Retailer 1) Class 2: condition 2,4 (Retailer 2 HIGH at zero Retailer 1) Class 3: condition 5,6,7,8,9,10,11,12 (All other cases) 12 Classes, Regard each condition as one class Class 1: condition 1,2,5,6,9,10 (LOW Retailer 3) Class 2: condition 3,4,7,8,11,12 (HIGH Retailer 3) Class 1: 1,2 (LOW Retailer 3 at zero Retailer 1) Class 2: 3,4 (HIGH Retailer 3 at zero Retailer 1) Class 3: 5,6 (LOW Retailer 3 at one Retailer 1) Class 4: 7,8 (HIGH Retailer 3 at one Retailer 1) Class 5: 9,10 (LOW Retailer 3 at two Retailer 1) Class 6: 11,12 (HIGH Retailer 3 at two Retailer 1) Class 1: condition 9,10,11,12 (Two Retailer 1) Class 2: condition 1,2,3,4,5,6,7,8 (other conditions) Table 2. The class definition by agents Agent Features Agent Features Retailer 2 PS, PW Warehouse 1 PS, PW, OS Distribution Center 1 W, OPS, OS Factory 1 TP, W, OPS, OS Retailer 3 PS, PW Supplier 1 TR, TP, OTP Distribution Center 2 PS, PW Retailer 1 PS, PW Factory 2 TP Warehouse 2 TP, W Supplier 2 OTP Wholesaler 1 S * PS = ProjectSupply, PW = ProjectWithdraw, W =Withdraw, TP = Transport, TR = Transit, S = Supply, OPS = ProjectSupply coming from outside, OS = Supply coming from outside, OTP = Transport coming from outside Table 3. The input features by agents Agent Success rate Agent Success rate Retailer 2 100% Warehouse 1 100% Retailer 1 100% Distribution Center 1 100% Retailer 3 100% Factory 1 22.34% Distribution Center 2 100% Supplier 1 40.43% Factory 2 100% Warehouse 2 84.04% Supplier 2 100% Wholesaler 1 86.17% Table 4. The success rate to classify the stress condition at each agent RESULTS The table 4 contains the test results. Overall performance is good. In addition, we can see the agent near the retailers in the supply chain can detect
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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,
Page 16 and 17: Table 2. GA Results Agent N A D max
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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
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Page 30 and 31: DMAS Controller. The functional uni
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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 72 and 73: Table 1: Notation Symbol Descriptio
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to machines (network topology) and
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component is a member of one of the
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denotes the immediate predecessors
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limit of large number of tasks. If
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Min-min heuristic algorithm Step 1:
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We set up eight different experimen
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0. 4 8057 8237 0.5 6439 6635 0.6 53
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pp. 191-200. [3] I. Foster, C. Kess
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Technology, Cambridge, MA, 1995. [2
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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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