We find that for low level tasks AI planning shop floor control tasks arc reviewed. In the area of resource allocation in distributed manufacturing D'stems, techniques provide good coverage of the problems to be solved, but that the manner in which to apply specific the issue of resource reconfiguration is identified as a techniques becomes less clear as the scope of the neglected problem. A game theoretic planning model considered problem stretches to include issues of system suitable for use in a distributed shop floor control wide performance. Often this problem derives from environment is introduced. Autonomous machine characteristics of the shop floor control domain which are controllers consult the planning model to e tluate not encountered in the original application. However, in reconfiguration decisions as the controlled machine some cases important long range issues have not been well resource becomes idle. The results of the evaluation, an addressed for distributed environments. We ident ' one equilibrium play of the setup game describing the such issue, production resource reconfiguration, which is reconflguration decision, dictate any resource essential to the operation of manufacturing facilities and rcconfiguratious undertaken by the machine controller.
The complexity, heterogeneity and scale of electrical networks have grown far beyond the limits of exclusively human-based management at the Smart Grid (SG). Likewise, researchers cogitate the use of artificial intelligence and heuristics techniques to create cognitive and autonomic management tools that aim better assist and enhance SG management processes like in the grid reconfiguration. The development of self-healing management approaches towards a cognitive and autonomic distribution power network reconfiguration is a scenario in which the scalability and on-the-fly computation are issues. This paper proposes the use of Case-Based Reasoning (CBR) coupled with the HATSGA algorithm for the fast reconfiguration of large distribution power networks. The suitability and the scalability of the CBR-based reconfiguration strategy using HATSGA algorithm are evaluated. The evaluation indicates that the adopted HATSGA algorithm computes new reconfiguration topologies with a feasible computational time for large networks. The CBR strategy looks for managerial acceptable reconfiguration solutions at the CBR database and, as such, contributes to reduce the required number of reconfiguration computation using HATSGA. This suggests CBR can be applied with a fast reconfiguration algorithm resulting in more efficient, dynamic and cognitive grid recovery strategy.
The system consists of collection of services, running on several of nodes. The workload generated by services may cause nodes overload, which negatively impacts the response time of services. The reconfiguration of the system incrementally transforms one system state to the other. The goal of the reconfiguration is to transform the system state into one, which all services meet their response time. Finding the appropriate reconfiguration that satisfies all timing requirements is the dynamic, preemptive scheduling problem.
Ramaekers, Zachary (University of Nebraska, Omaha) | Dasgupta, Raj (University of Nebraska, Omaha) | Ufimtsev, Vladimir (University of Nebraska, Omaha) | Hossain, S. G. M. (University of Nebraska, Lincoln) | Nelson, Carl (University of Nebraska, Lincoln)
We consider the problem of dynamic self-reconfiguration in a modular self-reconfigurable robot (MSR). Previous MSR self-reconfiguration approaches search for new configurations only within the modules of the MSR that needs reconfiguration. In contrast, we describe a technique where an MSR that needs to reconfigure communicates with other MSRs in its vicinity to determine if modules can be shared from other MSRs, and then determines the best possible configuration among the combined set of modules. We model the MSR self-reconfiguration problem as a coalition structure generation problem within a coalition game theoretic framework. We formulate the coalition structure generation problem as a planning problem in the presence of uncertainty and propose an MDP-based algorithm to solve it. We have implemented our algorithm within an MSR called ModRED that is simulated on the Webots simulation platform. Our results show that using our self-reconfiguration algorithm, when an MSR needs to reconfigure, a new configuration that is within 5-7% of the globally optimal configuration can be determined. We have also shown that our algorithm performs comparably with another existing algorithm for determining optimal coalition structure.