Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Author: search.filters.author.Mahanti, AmbujSubject: search.filters.subject.Systems Integration

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    Manufacturing Cell Formation by State-Space Search
    (1993) Ghosh, Subrata; Mahanti, Ambuj; Nagi, Rakesh; Nau, Dana; ISR
    This paper addresses the problem of grouping machines in order to design cellular manufacturing cells, with an objective to minimize intercell flow. This problem is replaced to one of the major aims of group technology (GT): to decompose the manufacturing system into manufacturing cells that are as independent as possible.

    This problem is NP-hard. Thus, nonheuristic methods cannot address problems of typical industrial dimensions because they would require exorbitant amounts of computing time, while fast heuristic methods may suffer from sub-optimality.

    We present a branch-and-bound state-space search algorithm that attempts to overcome both these deficiencies. One of the major strengths of this algorithm is its efficient branching and search strategy. In addition, the algorithm employs the efficient Inter-Cell Traffic Minimization Method to provide good upper bounds, and computes lower bounds based on a relaxation of merging.

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    PRA: Massively Parallel Heuristic Search
    (1991) Evett, Matthew; Hendler, James A.; Mahanti, Ambuj; Nau, D.; ISR
    In this paper we describe a variant of A* search designed to run on the massively parallel, SIMD Connection Machine. The algorithm is designed to run in a limited memory by use of a retraction technique which allows nodes with poor heuristic values to be removed from the open list, until such time as they may need reexpansion, more promising paths having failed. Our algorithm, called PRA* (for Parallel Retraction A*), is designed to maximize use of the Connection Machine's memory and processors. In addition, the algorithm is guaranteed to return an optimal path when an admissable heuristic is used. Results comparing PRA* to Korf's IDA* for the fifteen-puzzle show significantly fewer node expansions for PRA*. In addition, empirical results show significant parallel speedups, indicative of the algorithm's design for high processor utilization.