Search Results - (( generating permutation optimization algorithm ) OR ( java implication drops algorithm ))

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    Optimization of job scheduling in a machine shop using genetic algorithm by Adhikari, A., Biswas, C.K., Adhikari, N.

    Published 2002
    “…A modified version of GA known as string GA has been used to get the near optimal cycle time for permutation analysis. An experiment has been carried out with 2 iv 5 resolution to find the significance of five parameters of GA, namely population size, maximum generation, probability of crossing, probability of mutation and crossover operators. …”
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    Article
  3. 3

    Optimization of job scheduling in a machine shop using genetic algorithm by Adhikari, A., Biswas, C.K., Adhikari, N.

    Published 2002
    “…A modified version of GA known as string GA has been used to get the near optimal cycle time for permutation analysis. An experiment has been carried out with 2 iv 5 resolution to find the significance of five parameters of GA, namely population size, maximum generation, probability of crossing, probability of mutation and crossover operators. …”
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    Article
  4. 4

    Hybrid-discrete multi-objective particle swarm optimization for multi-objective job-shop scheduling by Anuar, Nurul Izah

    Published 2022
    “…Many real-world production scheduling problems involve the simultaneous optimization of multiple conflicting objectives that are challenging to solve without the aid of powerful optimization techniques. …”
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    Thesis
  5. 5

    Solving assembly sequence planning using angle modulated simulated kalman filter by Ainizar, Mustapa, Zulkifli, Md. Yusof, Asrul, Adam, Badaruddin, Muhammad, Zuwairie, Ibrahim

    Published 2018
    “…This paper presents an implementation of Simulated Kalman Filter (SKF) algorithm for optimizing an Assembly Sequence Planning (ASP) problem. …”
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  6. 6

    Simulated Annealing Based Strategy for Test Redundancy Reduction by Kamal Z., Zamli, Mohd Hafiz, Mohd Hassin, Al-Kazemi, Basem, Naseer, Atif

    Published 2014
    “…Unlike existing works which rely on the Greedy approaches, our work adopts the random sequence permutation and optimization algorithm based on Simulated Annealing with systematic merging technique. …”
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