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    Wyświetlanie 1-4 z 4
    Tytuł:
    Stable scheduling of single machine with probabilistic parameters
    Autorzy:
    Bożejko, W.
    Rajba, P.
    Wodecki, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/200225.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    scheduling
    uncertain parameters
    tabu search
    stability
    planowanie
    niepewne parametry
    stabilność
    przeszukiwanie tabu
    Opis:
    We consider a stochastic variant of the single machine total weighted tardiness problem jobs parameters are independent random variables with normal or Erlang distributions. Since even deterministic problem is NP-hard, it is difficult to find global optimum for large instances in the reasonable run time. Therefore, we propose tabu search metaheuristics in this work. Computational experiments show that solutions obtained by the stochastic version of metaheuristics are more stable (i.e. resistant to data disturbance) than solutions generated by classic, deterministic version of the algorithm.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2017, 65, 2; 219-231
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Two meta-heuristic algorithms for scheduling on unrelated machines with the late work criterion
    Autorzy:
    Wang, Wen
    Chen, Xin
    Musial, Jędrzej
    Blazewicz, Jacek
    Powiązania:
    https://bibliotekanauki.pl/articles/330022.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    late work minimization
    unrelated machines
    tabu search
    genetic algorithm
    minimalizacja opóźnienia
    przeszukiwanie tabu
    algorytm genetyczny
    Opis:
    A scheduling problem in considered on unrelated machines with the goal of total late work minimization, in which the late work of a job means the late units executed after its due date. Due to the NP-hardness of the problem, we propose two meta-heuristic algorithms to solve it, namely, a tabu search (TS) and a genetic algorithm (GA), both of which are equipped with the techniques of initialization, iteration, as well as termination. The performances of the designed algorithms are verified through computational experiments, where we show that the GA can produce better solutions but with a higher time consumption. Moreover, we also analyze the influence of problem parameters on the performances of these metaheuristics.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2020, 30, 3; 573-584
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Research on optimization of unrelated parallel machine scheduling based on IG-TS algorithm
    Autorzy:
    Chi, Xinfu
    Liu, Shijing
    Li, Ce
    Powiązania:
    https://bibliotekanauki.pl/articles/2173693.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    warp knitting machine
    parallel machine scheduling
    iterative greedy algorithm
    tabu search
    osnowarka
    planowanie maszyn równoległych
    algorytm zachłanny iteracyjny
    przeszukiwanie tabu
    Opis:
    This issue is a typical NP-hard problem for an unrelated parallel machine scheduling problem with makespan minimization as the goal and no sequence-related preparation time. Based on the idea of tabu search (TS), this paper improves the iterative greedy algorithm (IG) and proposes an IG-TS algorithm with deconstruction, reconstruction, and neighborhood search operations as the main optimization process. This algorithm has the characteristics of the strong capability of global search and fast speed of convergence. The warp knitting workshop scheduling problem in the textile industry, which has the complex characteristics of a large scale, nonlinearity, uncertainty, and strong coupling, is a typical unrelated parallel machine scheduling problem. The IG-TS algorithm is applied to solve it, and three commonly used scheduling algorithms are set as a comparison, namely the GA-TS algorithm, ABC-TS algorithm, and PSO-TS algorithm. The outcome shows that the scheduling results of the IG-TS algorithm have the shortest manufacturing time and good robustness. In addition, the production comparison between the IG-TS algorithm scheduling scheme and the artificial experience scheduling scheme for the small-scale example problem shows that the IG-TS algorithm scheduling is slightly superior to the artificial experience scheduling in both planning and actual production. Experiments show that the IG-TS algorithm is feasible in warp knitting workshop scheduling problems, effectively realizing the reduction of energy and the increase in efficiency of a digital workshop in the textile industry.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 4; art. no. e141724
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Solving scheduling problems with integrated online sustainability observation using heuristic optimization
    Autorzy:
    Burduk, Anna
    Musiał, Kamil
    Balashov, Artem
    Batako, Andre
    Safonyk, Andrii
    Powiązania:
    https://bibliotekanauki.pl/articles/2173719.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    production scheduling
    sustainable development
    genetic algorithm
    meta-heuristics
    intelligent optimization methods of production systems
    tabu search
    harmonogramowanie produkcji
    zrównoważony rozwój
    algorytm genetyczny
    przeszukiwanie tabu
    metaheurystyki
    inteligentne metody optymalizacji systemów produkcyjnych
    Opis:
    The paper deals with the issue of production scheduling for various types of employees in a large manufacturing company where the decision-making process was based on a human factor and the foreman’s know-how, which was error-prone. Modern production processes are getting more and more complex. A company that wants to be competitive on the market must consider many factors. Relying only on human factors is not efficient at all. The presented work has the objective of developing a new employee scheduling system that might be considered a particular case of the job shop problem from the set of the employee scheduling problems. The Neuro-Tabu Search algorithm and the data gathered by manufacturing sensors and process controls are used to remotely inspect machine condition and sustainability as well as for preventive maintenance. They were used to build production schedules. The construction of the Neuro-Tabu Search algorithm combines the Tabu Search algorithm, one of the most effective methods of constructing heuristic algorithms for scheduling problems, and a self-organizing neural network that further improves the prohibition mechanism of the Tabu Search algorithm. Additionally, in the paper, sustainability with the use of Industry 4.0 is considered. That would make it possible to minimize the costs of employees’ work and the cost of the overall production process. Solving the optimization problem offered by Neuro-Tabu Search algorithm and real-time data shows a new way of production management.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 6; art. no. e143830
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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