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    Wyświetlanie 1-3 z 3
    Tytuł:
    Optimizing the Job Shop Scheduling Problem with a no Wait Constraint by Using the Jaya Algorithm Approach
    Autorzy:
    Bougloula, Aimade Eddine
    Powiązania:
    https://bibliotekanauki.pl/articles/24200517.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    scheduling
    optimization
    scheduling problem
    job shop
    no-wait problem
    Jaya algorithm
    Opis:
    This work is interested to optimize the job shop scheduling problem with a no wait constraint. This constraint occurs when two consecutive operations in a job must be processed without any waiting time either on or between machines. The no wait job shop scheduling problem is a combinatorial optimization problem. Therefore, the study presented here is focused on solving this problem by proposing strategy for making Jaya algorithm applicable for handling optimization of this type of problems and to find processing sequence that minimizes the makespan (Cmax). Several benchmarks are used to analyze the performance of this algorithm compared to the best-known solutions.
    Źródło:
    Management and Production Engineering Review; 2023, 14, 3; 148--155
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multi-criteria human resources planning optimisation using genetic algorithms enhanced with MCDA
    Autorzy:
    Jurczak, Marcin
    Miebs, Grzegorz
    Bachorz, Rafał A.
    Powiązania:
    https://bibliotekanauki.pl/articles/2204085.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    mathematical optimisation
    multi-criteria optimisation
    scheduling
    job shop problem
    MCDA
    Opis:
    The main objective of this paper is to present an example of the IT system implementation with advanced mathematical optimisation for job scheduling. The proposed genetic procedure leads to the Pareto front, and the application of the multiple criteria decision aiding (MCDA) approach allows extraction of the final solution. Definition of the key performance indicator (KPI), reflecting relevant features of the solutions, and the efficiency of the genetic procedure provide the Pareto front comprising the representative set of feasible solutions. The application of chosen MCDA, namely elimination et choix traduisant la réalité (ELECTRE) method, allows for the elicitation of the decision maker (DM) preferences and subsequently leads to the final solution. This solution fulfils all of the DM expectations and constitutes the best trade-off between considered KPIs. The proposed method is an efficient combination of genetic optimisation and the MCDA method.
    Źródło:
    Operations Research and Decisions; 2022, 32, 4; 57--74
    2081-8858
    2391-6060
    Pojawia się w:
    Operations Research and Decisions
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimization of Job Shop Scheduling Problem by Genetic Algorithms: Case Study
    Autorzy:
    Sahar, Habbadi
    Herrou, Brahim
    Sekkat, Souhail
    Powiązania:
    https://bibliotekanauki.pl/articles/24200523.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    optimization
    metaheuristics
    scheduling
    job shop scheduling problem
    genetic algorithms
    simulation
    Opis:
    The Job Shop scheduling problem is widely used in industry and has been the subject of study by several researchers with the aim of optimizing work sequences. This case study provides an overview of genetic algorithms, which have great potential for solving this type of combinatorial problem. The method will be applied manually during this study to understand the procedure and process of executing programs based on genetic algorithms. This problem requires strong decision analysis throughout the process due to the numerous choices and allocations of jobs to machines at specific times, in a specific order, and over a given duration. This operation is carried out at the operational level, and research must find an intelligent method to identify the best and most optimal combination. This article presents genetic algorithms in detail to explain their usage and to understand the compilation method of an intelligent program based on genetic algorithms. By the end of the article, the genetic algorithm method will have proven its performance in the search for the optimal solution to achieve the most optimal job sequence scenario.
    Źródło:
    Management and Production Engineering Review; 2023, 14, 3; 44--56
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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