An application of metaheuristic optimization algorithms for solving the flexible job-shop scheduling problem

  • Aleksandar Stanković Faculty of Mechanical Engineering, University of Niš, Serbia
  • Goran Petrović Faculty of Mechanical Engineering, University of Niš, Serbia
  • Žarko Ćojbašić Faculty of Mechanical Engineering, University of Niš, Serbia
  • Danijel Marković Faculty of Mechanical Engineering, University of Niš, Serbia
Keywords: Scheduling, Flexible job-shop, Genetic algorithm, Tabu Search, Ant Colony Optimization, Local search.

Abstract

Abstract. The Flexible Job Planning (FJSP) problem is another planning and scheduling problem. It is a continuation of the classic problem of scheduling jobs, where each operation can be performed on different machines, while the processing time depends on the machine being used. FJSP is a difficult NP problem that consists of two sub-problems, scheduling problems and scheduling operations. The paper presents a model for solving FJSP based on meta-heuristic algorithms: Genetic algorithm (GA), Tabu search (TS) and Ant colony optimization (ACO). The efficiency of the approach in solving the aforementioned problem is reflected in the flexible search of space and the choice of dominant solutions. The results of the computation are graphically represented on the Gantt chart.

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Published
2020-09-24
How to Cite
Stanković, A., Petrović, G., Ćojbašić, Žarko, & Marković, D. (2020). An application of metaheuristic optimization algorithms for solving the flexible job-shop scheduling problem . Operational Research in Engineering Sciences: Theory and Applications, 3(3), 13-28. Retrieved from https://oresta.rabek.org/index.php/oresta/article/view/57