DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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D k : Outflow from high priority queue in the kth interval λ : Inflow to low priority queue from the outside in the kth interval k The system is characterized by the following parameters: µ a : Rate per unit of the schedule cycle at which the low priority queue can be served µ : Rate per unit of the schedule cycle at which the high priority queue can be served b l: The feedback interval in units of the schedule cycle The following four equations then completely describe the evolution of the system: A C B k + 1 = Ak + λk − Ck (1) Dk = min( Ak + λk , µ a (1 − )) (2) µ k b k +1 = Bk + Ck −l − Dk (3) D = min( B + C , µ ) (4) k k k −l b Equations (1) and (3) are merely conservation rules, while equations (2) and (4) model the constraints on the outflows and the interaction between the queues. This model while conceptually simple, exhibits surprisingly complex behaviors. Dynamical Behavior The analytic approach to solve for the flow model under constant arrivals (i.e. λ k = λ for all k) shows several classes of solutions. The system is found to batch its workload even for perfectly smooth arrival patterns. Following are the characteristics of behavior of the system: 1) Above a threshold arrival rate ( λ ≥ / 2 ), a momentary overload can send the system µ b into a number of stable modes of oscillations. 2) Each mode of oscillations is characterized by distinct average queuing delays. 3) The extreme sensitivity to parameters, and the existence of chaos, implies the system at a given time may be any one of a number of distinct steady-state modes. The batching of the workload can cause significant queuing delays even at moderate occupancies. Also such oscillatory behavior significantly lowers the real-time capacity of the system. For details of application of this model in supply chain context, refer to (Kumara et al. 2003). 5.1.2 Managerial Systems Decision-making is another typical characteristic in which the entities in a supply chain are continuously engaged in. Entities make decisions to optimize their self-interests, often based on local, delayed and imperfect information. To illustrate the effects of decisions on the dynamics of supply chain as a whole, we consider a managerial system, which allocates resources to its production and marketing departments in accordance with shifts in inventory and/or backlog (Rasmussen and Moseklide 1988). It has four level variables: resources in production, resources in sales, inventory of finished products and number of customers. In order to represent the time required to adjust production, a third order delay is introduced between production rate and inventory. The sum of the two resource variables is kept constant. The rate of production is determined from resources in production through a nonlinear function, which expresses a decreasing productivity of additional resources as the
company approaches maximum capacity. The sales rate, on the other hand, is determined by the number of customers and by the average sales per customer-year. Customers are mainly recruited through visits of the company salesman. The rate of recruitment depends upon the resources allocated to marketing and sales, and again it is assumed that there is a diminishing return to increasing sales activity: Once recruited, customers are assumed to remain with the company for an average period AT, the association time. A difference between production and sales causes the inventory to change. The Company is assumed to respond to such changes by adjusting its resource allocation. When the inventory is lower than desired, on the other hand, resources are redirected from sales to production. A certain minimum of resources is always maintained in both production and sales. In the model, this is secured by means of two limiting factors, which reduce the transfer rate when a resource floor is approached. Finally the model assumes that there is a feedback from inventory to customer defection rate. If the inventory of finished products becomes very low, the delivery time is assumed to become unacceptable to many customers. As a consequence, the defection rate is enhanced by a factor 1+H. Figure 3. Managerial System Dynamical Behavior The managerial system described is controlled by two interacting negative feedback. Combined with the delays involved in adjusting production and sales, these loops create the potential for oscillatory behavior. If the transfer of resources is fast enough, this behavior is destabilized and
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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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1 SITUATION IDENTIFICATION USING DY
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3 3.2 Behavior In SSC society an ag
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5 All the behavior parameters may n
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Estimating Global Stress Environmen
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chaotic deterministic time series.
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100% 1 95% 2 14 15 64% TAO 4 64% 62
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interactions as a dynamical system
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ehavior states under varying system
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Extensive testing and validation of
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5 Conclusions and Future Research T
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2 to function critically even under
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4 points into a corresponding inner
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6 stresses well. The Warehouse 1 ag
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Table 1: Notation Symbol Descriptio
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4 Conclusions and Future Work 4.1 C
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O, U Stresses Physical/Infrastructu
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Manuscript for IEEE Transactions on
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2 discuss problem domain and in Sec
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4 t Si t ∫ + ( ) i ) t When RA i
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6 S UB i ∑ = P LI / LI . (16) i p
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8 policies while underutilizing in
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architecture based on both the Grid
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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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Manuscript for IEEE TRANSACTIONS ON
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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
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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 176 and 177: Acknowledgements The work described
Page 178 and 179: key realization to tackle this prob
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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
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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
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