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Research And Implementation Of Equipments Cannibalization And Shipping Allocating System In Distributed Environment

Posted on:2006-04-25Degree:MasterType:Thesis
Country:ChinaCandidate:P XuFull Text:PDF
GTID:2178360185463453Subject:Computer Science and Technology
Abstract/Summary:PDF Full Text Request
Under the background of equipments cannibalization and shipping allocating process in distributed environment, this paper emphasizes to study three key problems: the integration of e relation databases, materials cannibalization and allocating ships problem.With the help of integration tool, InforEAI, the paper puts forward a hierarchy of relation databases' integration. The concept of data mirror layer, with which original different databases firstly are converted to a standard database is introduced, and then, by trigger mechanic complicated online updating can be realized. A meta-trigger table is created to fulfill user-customization because in reality business conversion functions are changeable and unforeseeable.In the cannibalization model, a kind of single target shortest path problem(STSP) is put forward. And then an efficient algorithm to solve it is given, which is proved in the theory of computing complexity. Aiming at dissimilarity what the urgent circumstance and the otherwise pay attention to, a time-priority model and a cost sensitive one are built and solved respectively. Under the circumstance of some of the transporting network jamming up, the paper gives again an improvement in the BDSTSP algorithm, which can save the computation under such condition.The shipping model mainly considers the relation between units, materials and ships. By using relation matrix a non-linear hybrid knapsack model is given, and a heuristic genetic algorithm is designed to solve it. In this algorithm, an efficient coding is adopted, which can not only save the storage but also are propitious to subsequent genetic operators. Combining some chromosomes from greedy strategy into initial pop is to attain global result quickly. Experiments and comparisons show that the algorithm receives better result at the problem scaling greatly.
Keywords/Search Tags:Data Integration, Linear and Non-linear programming, Range Analysis, Single Target Shortest Path, Heuristic Genetic Algorithm
PDF Full Text Request
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