DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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sustained oscillations. If the instability drives the system further into the nonlinear regime, more complicated temporal behavior may be generated. The route to chaos through subsequent period-doubling bifurcations, as certain parameters of the system are varied, is generic to large class of systems in physics, chemistry, biology, economics and other fields. Functioning in chaotic regime deprives the ability for long-term predictions about the behavior of the system, while short-term predictions may be possible sometimes. As a result, control and stabilization of such a system becomes very difficult. • Emergent behavior and Self-Organization: With the individual entities obeying a deterministic selection process, the organization of the overall supply chain emerges through a natural process of order and spontaneity. This emergence of highly structured collective behavior, over time from the interaction of the simple entities leads to fulfillment of customer orders. Demand amplification, inventory swing are some other but undesirable emergent phenomena that can also arise. For instance, the decisions and delays downstream in a supply chain often leads to amplifying non-desirable effect upstream, a phenomena commonly known as “Bull Whip” effect. • Adaptation and Evolution: Supply chain both reacts to and creates it environment. Generally speaking a supply chain interacts with almost every other conceivable network. Operationally, the environment depends on the chosen scale of analysis, for e.g. it can be taken as the customer market. Typically, significant dynamism exists in the environment which necessitates a constant adaptation of the supply network. However the environment is highly rugged making the co evolution difficult. The individual entities constantly observe what emerges from a supply network and make adjustments to organizational goals and supporting infrastructure. Another common way of adaptation is through altering boundaries of the network. The boundaries can change as a result of including or excluding particular entity and by adding or eliminating connections among entities, thereby changing the underlying pattern of interaction. As we discuss next, Supply chain management plays a critical role in making the network evolve in a coherent manner. 3.1 Supply Chain Management Supply chain management is defined as the integration of key business processes from endusers through original suppliers that provide products, services, and information and add value for customers and other stakeholders (Cooper et. al. 1997). It involves balancing reliable customer delivery with manufacturing and inventory costs. It is evolved around a customer-focused corporate vision, which drives changes throughout a firm’s internal and external linkages and then captures the synergy of inter-functional, inter-organizational integration and coordination. Due to the inherent complexity it is a challenge to coordinate the actions of entities across organizational boundaries so that they perform in a coherent manner. An important element in managing SCN is to control the ripple effect of lead-time so that the variability in supply chain can be minimized. Demand forecasting is used to estimate demand for each stage, and the inventory between stages for the network is used for protecting against fluctuations in supply and demand across the network. Due to the decentralized control properties of the SCN, control of ripple effect requires coordination between entities in performing their tasks. The problem of coordination has reached another dimension due to some other trends in the current supply chains. Two important organizational and market trends that are on their way have been the atomization of markets as well as that of organizational entities (Balakrishnan et al. 1999). In such a scenario product realization process has a continuous customer involvement in all phases - from design to delivery. Customization is not only limited to selecting from pre-determined model variants; rather, product design, process plans, and even the supply chain configuration have to be tailored for each customer. The product realization organization has to be formed on
the fly- as a consortium of widely dispersed organizations to cater to the needs of a single customer. Thus organizations consist of series of opportunistic alliances among several focused organizational entities to address particular market opportunities. For manufacturing organizations to operate effectively in this environment of dynamic, virtual alliances, products must have modular architectures, processes must be well characterized and standardized, documentation must be digitized and widely accessible, and systems must be interoperable. Automation and intelligent information processing is vital for diagnosing problems during product realization and usage, coordination, design and production schedules, searching for relevant information in multi-media databases. These trends exacerbate the challenges of coordination and collaboration as the number of product realization networks increase, and so does the number of partners in each network. Inventory is unwise approach to dealing with highly changing market demand and short life cycle products. Information is an appropriate substitute for inventory. Information about the material lead-time from different suppliers can be used for planning the material arrival, instead of building up inventory. The demand information can be transmitted to the manufactures on a timely basis, so that the orders can be fulfilled with less inventory costs. In fact it is widely realized, that the successful integration of the entire supply chain process depends heavily on the availability of accurate and timely information that can be shared by all members of the supply chain. Supply chain management now increasingly relies on Information Technology as discussed below. 3.2 Information Technology in Supply Chain Management Information technology with its capability of providing global reach and wide range of connectivity, enterprise integration, micro autonomy and intelligence, object and networked oriented computing paradigms and rich media support; has been key enabler for the management of future manufacturing enterprises. It is vital for eliminating collaboration and coordination costs, and to permit rapid setup of dynamic information exchange networks. Connectivity permits involvement of customers and other stakeholders in all aspects of manufacturing. Enterprise integration facilitates seamless interaction among global partners. Micro autonomy and intelligence permit atomic tracking and remote control. New software paradigms enable distributed, intelligent and autonomous operations. Distributed computing facilitates quick localized decisions without loosing vast data gathering potential and powerful computing capabilities. Rich media support, which includes capabilities like digitization, visualization tools and virtual reality, facilitate collaboration and immersion. Many improvements have occurred in supply chain management because IT enables changes to be made in inventory management and production, dynamically. It assists the managers in coping up with uncertainty and lead-time through improved collection and sharing of information between supply chain nodes. The success of an enterprise is now largely dependent on how its information resources are designed, operated and managed, especially with the Information technology emerging as a critical input to be leveraged for significant organizational productivity. However, the difficulty arises when trying to design an information system that can handle the information needs of supply chain nodes to allow efficient, flexible and decentralized supply chain management. The main hurdle in efficiently using information technology is the lack of our understanding of the organizational, functional and evolutionary principles of supply chains. Recognizing supply chains as CAS, can however lead to novel and effective ways to understand their emergent dynamics. It has been found that many of the diverse looking CAS share similar characteristics and problems and thus can be tackled through similar approaches. While at present networks are largely controlled by humans; the complexity, diversity and geographic distribution of the networks, makes it necessary that the networks maintain themselves in a sort of evolutionary sense, just as biological organisms do (Maxon 1990). Similarly, the problem of coordination, which is a challenge in supply chains, has been routinely
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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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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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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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Page 160 and 161: Understanding Agent Societies Using
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Page 176 and 177: Acknowledgements The work described
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Page 202 and 203: Min, H. and Zhou, G., 2002, Supply
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