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    Wyświetlanie 1-3 z 3
    Tytuł:
    Meta-heuristic approach based on genetic and greedy algorithms to solve flexible job-shop scheduling problem
    Autorzy:
    Rezaeipanah, Amin
    Sarhangnia, Fariba
    Abdollahi, Mohammad Javad
    Powiązania:
    https://bibliotekanauki.pl/articles/2097966.pdf
    Data publikacji:
    2021
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    job-shop scheduling
    meta-heuristic method
    genetic algorithm
    greedy algorithm
    jobs priority
    Opis:
    Job-shop scheduling systems are one of the applications of group technology in industry, the purpose of which is to take advantage of the physical or operational similarities of products in their various aspects of construction and design. Additionally, these systems are identified as cellular manufacturing systems (CMS). In this paper, a meta-heuristic method that is based on combining genetic and greedy algorithms has been used in order to optimize and evaluate the performance criteria of the flexible job-shop scheduling problem. In order to improve the efficiency of the genetic algorithm, the initial population is generated by the greedy algorithm, and several elitist operators are used to improve the solutions. The greedy algorithm that is used to improve the generation of the initial population prioritizes the cells and the job in each cell and, thus, offers quality solutions. The proposed algorithm is tested over the P-FJSP dataset and compared with the state-of-the-art techniques of this literature. To evaluate the performance of the diversity, spacing, quality, and run-time criteria were used in a multi-objective function. The results of the simulation indicate the better performance of the proposed method as compared to the NRGA and NSGA-II methods.
    Źródło:
    Computer Science; 2021, 22 (4); 463--488
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Contribution Overview to the Evaluation and Development of Spare Parts Management Models: Meta-Heuristic and Probabilistic Methods
    Autorzy:
    Bounou, Oumaima
    El Barkany, Abdellah
    El Biyaali, Ahmed
    Powiązania:
    https://bibliotekanauki.pl/articles/1841431.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    spare parts
    maintenance
    inventory management
    probabilistic methods
    meta heuristic method
    risk management
    performance
    Opis:
    The presence of the spare parts stock is a necessity to ensure the continuity of services. The supply of spare parts is a special case of the global supply chain. The main objective of our research is to propose a global spare parts management approach which allows decision makers to determine the essential points in stock management. Thus, it is important for the stock manager to evaluate the system considered from time to time based on performance indicators. Some of these indicators are presented in the form of a dashboard. The presentation of this chapter chronologically traces the progress of our research work. In the first part, we present the work related to the forecast of spare parts needs through parametric and statistical methods as well as a Bayesian modelling of demand forecasting. To measure the appreciation of the supply of spare parts inventory, the second part focuses on work related to the evaluation of the performance of the spare parts system. Thus, we concretize the link between the management of spare parts and maintenance in the third part, more precisely, in the performance evaluation of the joint -management of spare parts and maintenance, in order to visualize the influence of parameters on the system. In the last section of this chapter, we will present the metaheuristic methods and their use in the management of spare parts and maintenance and make an analysis on work done in the literature.
    Źródło:
    Management and Production Engineering Review; 2021, 12, 1; 24-37
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Metaheuristic optimization of marginal risk constrained long - short portfolios
    Autorzy:
    Vijayalakshmi Pai, G. A.
    Michel, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/91858.pdf
    Data publikacji:
    2012
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    metaheuristic
    optimization
    portfolio optimization
    marginal risk
    quadratic programming
    meta heuristic method
    data envelopment analysis
    Opis:
    The problem of portfolio optimization with its twin objectives of maximizing expected portfolio return and minimizing portfolio risk renders itself difficult for direct solving using traditional methods when constraints reflective of investor preferences, risk management and market conditions are imposed on the underlying mathematical model. Marginal risk that represents the risk contributed by an asset to the total portfolio risk is an important criterion during portfolio selection and risk management. However, the inclusion of the constraint turns the problem model into a notorious non-convex quadratic constrained quadratic programming problem that seeks acceptable solutions using metaheuristic methods. In this work, two metaheuristic methods, viz., Evolution Strategy with Hall of Fame and Differential Evolution (rand/1/bin) with Hall of Fame have been evolved to solve the complex problem and compare the quality of the solutions obtained. The experimental studies have been undertaken on the Bombay Stock Exchange (BSE200) data set for the period March 1999-March 2009. The efficiency of the portfolios obtained by the two metaheuristic methods have been analyzed using Data Envelopment Analysis.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 3; 259-274
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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