Search Results - (( java implication based algorithm ) OR ( programming trade effect algorithm ))

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    A bacteria foraging algorithm for solving integrated multi-period cell formation and subcontracting production planning in a dynamic cellular manufacturing system by Tang, Sai Hong, Nouri, Hossein, Motlagh, Omid Reza Esmaeili

    Published 2011
    “…This research aims to apply this emerging optimisation algorithm to develop a mixed-integer programming model for designing cellular manufacturing systems (CMSs), and production planning in dynamic environments. …”
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    Hybrid meta-heuristic algorithm for solving multi-objective aggregate production planning in fuzzy environment by Kalaf, Kalaf, Bayda Atiya

    Published 2017
    “…For these proposed approaches, this study adopted a hybridization of a fuzzy programming, modify simulated annealing, and simplex downhill (SD) algorithm called Fuzzy-MSASD to resolve multiple objective linear programming APP problems in a fuzzy environment. …”
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    Development of a fuzzy multi-objective mathematical model for hazardous waste location-routing problem by Hassani, Omid Boyer

    Published 2014
    “…Using fuzzy parametric programing proved that the waste quantity uncertainty has effect on the objectives function values, the optimum number of facilities and location of facilities. …”
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    Obfuscatable aggregatable signcryption scheme with PadSteg for unattended devices in IoT systems by Uwadiegwu, Inyama Victor

    Published 2019
    “…In other to achieve the security enhancement, efficiency and reduce communication overheads by using small security parameter and the effect of such a security complexity trade-off. Motivated by this issue, we propose Signcryption Obfuscatable and Steganography-PadSteg (SOS) algorithm as a solution. …”
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    Implementation of Hybrid Indexing, Clustering and Classification Methods to Enhance Rural Development Programme in South Sulawesi by Muhammad, Faisal

    Published 2024
    “…This shows that the accuracy status indicates a high percentage of correct predictions, and then the F1 Score of 0.9 indicates a well-balanced trade-off between precision and recall, demonstrating the model's overall effectiveness. …”
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