A computer package for optimal multi-objective VAr planning in large scale power systems
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This paper presents a simulated annealing based computer package for multi-objective, VAr planning in large scale power systems-SAMVAR. This computer package has three distinct features. First, the optimal VAr planning is reformulated as a constrained, multi-objective, nondifferentiable optimization problem. The new formulation considers four different objective functions related to system investment, system operational efficiency, system security and system service quality. The new formulation also takes into consideration load, operation and contingency constraints. Second, it allows both the objective functions and equality and inequality constraints to be nondifferentiable; making the problem formulation more realistic. Third, the package employs a two-stage solution algorithm based on an extended simulated annealing technique and the epsilon -constraint method. The first-stage of the solution algorithm uses an extended simulated annealing technique to find a global, noninferior solution. The second-stage uses the simulated annealing technique to find the global optimal solution. A salient feature of SAMVAR is that it allows planners to find an acceptable, global noninferior solution for the VAr problem. Simulation results indicate that SAMVAR has the ability to handle the multi-objective VAr planning problem and meet with the planner's requirements.< Keywords:
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The Last Planner method1 represents a radically different manner of controlling production in construction. Even if its benefits are widely observed, it is also a common observation that the introduction of the Last Planner method to a site, into a company or into a country is not an easy and uncomplicated task. This paper reports on the experiences and lessons gained during the introduction of the Last Planner to Finland. A simplified explanation of Last Planner is presented. The experiences and lessons gained are contrasted with those presented in prior literature.
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An intelligent tutoring system (ITS) that will assist first-year medical students in learning the causal relationships between the parameters of the circulatory system and in solving problems of disturbances to the system is discussed. The central component of the ITS, the instructional planner, is responsible for determining what to do next at each point during a tutoring session. The approach taken is to build the planner by combining the capabilities of lesson planning and discourse planning in order to provide globally coherent and adaptive instruction. The planner consists of two parts: a lesson planner and a discourse planner. The lesson planner generates lesson plans, which the discourse planner carries out. A sample dialogue, extracted from the transcript of an actual human tutor-student interaction, is used to provide a framework for the development of the overall system, especially from the planner's point of view.< >
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Determination of the Pareto front of location problem solutions represents one of the very complex and computational time demanding tasks, when solved by exact means of mathematical programming. This paper is motivated by possible application of the metaheuristic simulated annealing to the process of obtaining a close approximation of the Pareto front by a set of non-dominated solutions of the p-location problem. Contrary to the other approaches, the suggested method is based on minimization of non-dominated solution set area, which directly describes quality of the approximation. Elements of the simulated annealing method are used for random breaking some limits imposed on local characteristics of the improving process. The presented results of the numerical experiments give an insight to relations among the simulated annealing parameters and optimization process efficiency.
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Aimed at the common problems of multi-constraint optimization in optimal design of machinery,a new kind of solving method was presented.Utilizing the agglomeration features of KS function,the total or partial constraints of optimization problems were agglomerated to an approximate and draw near precision,a constraint to be controlled by one parameter only.And then choose proper constraint optimization method to solve the optimal problem after the compression of constraints.The geometric explaination of constraints agglomeration was presented by an example of feasible region figure in three dimensional space.
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Abstract Abstract The public image of the planner depicts him as an artist; as the master planner who intuitively prepares the best plans for the community. Planners, through their professional organizations, try to maintain an image of comprehensiveness. Neither image provides an adequate description of the planning profession. Neither explains why the plans prepared by the urban planner fail to gain real social commitment. What urban planners do and why they do it becomes more understandable when they are seen as governmental functionaries. A description of government's role, which is to order and regulate things, becomes a description of the planners' role. A description of the government the planner is subordinated to can serve to define the practical limitations of the plans urban planners prepare.
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Case-based planning is based on the idea that a machine planner should make use of its own past experience in developing new plans, relying on its memories instead of a base of rules. Memories of past successes are accessed and modified to create new plans. Memories of past failures are used to warn the planner of impending problems and memories of past repairs are called upon to tell the planner how to deal with them.
Successful plans are stored in memory, indexed by the goals they satisfy and the problems they avoid. Failures are also stored, indexed by the features in the world that predict them. By storing failures as well as successes, the planner is able to anticipate and avoid future plan failures.
These ideas of memory, learning and planning are implemented in the case-based planner CHEF, which creates new plans in the domain of Szechwan cooking.
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The nationally-recognized Susquehanna
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