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A Unified Distributed System Architecture for Goalbased Interaction with Smart Environments
, 2009
"... The vision of Ambient Intelligence is based on the ubiquity of information technology, the presence of computation, communication, and sensorial capabilities in an unlimited abundance of everyday appliances and environments. It is now a significant challenge to let ambient intelligence effortlessly ..."
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The vision of Ambient Intelligence is based on the ubiquity of information technology, the presence of computation, communication, and sensorial capabilities in an unlimited abundance of everyday appliances and environments. It is now a significant challenge to let ambient intelligence effortlessly emerge from the devices that surround the user in his environment. Future ambient intelligent infrastructures (e.g., Smart Environments) must be able to configure themselves from the available components in order to be effective in the real world. They require software technologies that enable adhoc ensembles of devices to spontaneously form a coherent group of cooperating components. This is specifically a challenge, if the individual components are heterogeneous in nature and have to engage in complex activity sequences in order to achieve a user goal. Typical examples of such ensembles are smart environments. It will be argued that enabling an ensemble of devices to spontaneously and coherently act on behalf of the user, requires software technologies that support unsupervised spontaneous cooperation. This thesis will illustrate why a goal based approach is reasonable and how explicit goals can be used to find device spanning strategies that assist the user. In order to solve the challenges noted above, an overall concept and architecture based on goal based interaction will be illustrated. Furthermore different concepts of cooperation strategies will be introduced and finally an evaluation will prove the validity of the approach.
Solving the quadratic assignment problem by means of general purpose mixed integer linear programming solvers
, 2010
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Solving Quadratic Assignment Problem (QAP) Using Invasive Weed Optimization Algorithm
"... Abstract A new powerful optimization algorithm inspired from colonizing weeds is utilized to solve the wellknown quadratic assignment problem (QAP) which is of application in a large number of practical areas such as plant layout, machinery layout and so on. A set of reference numerical problems f ..."
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Abstract A new powerful optimization algorithm inspired from colonizing weeds is utilized to solve the wellknown quadratic assignment problem (QAP) which is of application in a large number of practical areas such as plant layout, machinery layout and so on. A set of reference numerical problems from QAPLIB is taken in order to evaluate the efficiency of the algorithm compared with the previous ones which had been applied to solve the addressed problem. The results indicate that the algorithm outperforms the competitive ones for a sizable number of the problems as the problems' dimensions increase.
Genetic Algorithm for a Quay Management Problem
, 2011
"... Abstract: In this paper the Quay Management Problem (QMP) is defined as a Quadratic Assignment problem (QAP) which consists of assigning customers to loading positions and satisfying their demands. This work aims to help the decision maker to assign customer to a list of possible loading positions, ..."
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Abstract: In this paper the Quay Management Problem (QMP) is defined as a Quadratic Assignment problem (QAP) which consists of assigning customers to loading positions and satisfying their demands. This work aims to help the decision maker to assign customer to a list of possible loading positions, select storage areas to serve the customer and to assign a number of lifting trucks to each loading position. This work means to minimise cost and residence time. A Genetic Algorithm is applied to find the best solution to the problem. The nearest neighbours exploration is used with recombination procedure and maintaining the elements with the lower cost. The best solution found is always saved. The GA has performed better in terms of computational time for the different instances tested.
Optimizing Network Topology to Reduce Aggregate Traffic in Systems of Mobile Agents
"... Abstract Systems of networked mobile robots, such as unmanned aerial or ground vehicles, will play important roles in future military and commercial applications. The communications for such systems will typically be over wireless links and may require that the robots form an ad hoc network and comm ..."
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Abstract Systems of networked mobile robots, such as unmanned aerial or ground vehicles, will play important roles in future military and commercial applications. The communications for such systems will typically be over wireless links and may require that the robots form an ad hoc network and communicate on a peertopeer basis. In this chapter, we consider the problem of optimizing the network topology to minimize the total traffic in a network required to support a given set of data flows under constraints on the amount of movement possible at each mobile robot. We consider a subclass of this problem in which the initial and final topologies are trees, and the movement restrictions are given in terms of the number of edges in the graph that must be traversed. We develop algorithms to optimize the network topology while maintaining network connectivity during the topology reconfiguration process. Our topology reconfiguration algorithm uses the concept of prefix labelling and routing to move nodes through the network while maintaining network connectivity. We develop three algorithms to determine the final network topology. These include an optimal, but computationally complex algorithm, as well as a greedy algorithm and a simulated annealing algorithm that trade optimality for reduced complexity. We present simulation results to compare the performance of these algorithms.
“Evaluation and optimization of innovative production systems of goods and services” STUDY OF DIFFERENT PRINCIPLES FOR AUTOMATIC IDENTIFICATION OF GENERALIZED SYSTEM OF CONTRADICTIONS OUT OF DESIGN OF EXPERIMENTS
"... ABSTRACT: Problems in design of technical systems can be solved by optimization or inventive solving principles. Two representation models are studied: Generalized System of Contradictions (GSC) as inventive principle and Design of Experience (DoE) as optimisation principle. Our purpose is to improv ..."
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ABSTRACT: Problems in design of technical systems can be solved by optimization or inventive solving principles. Two representation models are studied: Generalized System of Contradictions (GSC) as inventive principle and Design of Experience (DoE) as optimisation principle. Our purpose is to improve the capacity of design problems resolution by using the both solving principles articulated to one representation model. We will show how it is possible to shift from DoE representation model to GSC representation model by using different methods. On the one side this transition can be done by the identification of Generalized System of Contradictions out of Design of Experiments based on a set of equations to resolve. On the other side methods of data analysis can be used to visualise and reorganise the DoE matrix in the form of “contradiction blocks ” reflecting the set of equations. This reorganisation of representation model will be illustrated on a simple technical system.
Quadratic Assignment Problem and its Relevance to the Real World: A Survey
"... The Quadratic Assignment Problem (QAP) is the well known and significant combinatorial optimization problem. For several decades, it has been of keen interest for researchers and its improvement is still in progress. QAP is very important because it plays an important role in various complex real wo ..."
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The Quadratic Assignment Problem (QAP) is the well known and significant combinatorial optimization problem. For several decades, it has been of keen interest for researchers and its improvement is still in progress. QAP is very important because it plays an important role in various complex real world problems. In this survey some of the prominent applications of QAP are illustrated which have been optimally applied to real world problems in diverse areas. Here inherent descriptions of various applications are summarized by highlighting the shortcomings of their applications related to QAP. This paper gives the future directions and strong conclusions on the survey of quadratic assignment problem and justify that the performance improvement of QAP is important.
A Survey of the Quadratic Assignment Problem
"... Support The quadratic assignment problem (QAP) is very challengeable and interesting problem that can model many reallife problems. In this paper, we will simply discuss the meaning of quadratic assignment problem, solving techniques and we will give a survey of some developments and researches. ..."
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Support The quadratic assignment problem (QAP) is very challengeable and interesting problem that can model many reallife problems. In this paper, we will simply discuss the meaning of quadratic assignment problem, solving techniques and we will give a survey of some developments and researches.
Performance Analysis
"... Abstract—Quadratic Assignment Problem (QAP) is an NPhard combinatorial optimization problem, therefore, solving the QAP requires applying one or more of the metaheuristic algorithms. This paper presents a comparative study between Metaheuristic algorithms: Genetic Algorithm, Tabu Search, and Simul ..."
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Abstract—Quadratic Assignment Problem (QAP) is an NPhard combinatorial optimization problem, therefore, solving the QAP requires applying one or more of the metaheuristic algorithms. This paper presents a comparative study between Metaheuristic algorithms: Genetic Algorithm, Tabu Search, and Simulated annealing for solving a reallife (QAP) and analyze their performance in terms of both runtime efficiency and solution quality. The results show that Genetic Algorithm has a better solution quality while Tabu Search has a faster execution time in comparison with other Metaheuristic algorithms for solving QAP.