Ten serwis zostanie wyłączony 2025-02-11.
Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2013 | R. 110, z. 1-M | 381--388
Tytuł artykułu

Research study of state-of-the-art algorithms for flexible job-shop scheduling problem

Wybrane pełne teksty z tego czasopisma
Warianty tytułu
PL
Przegląd współczesnych algorytmów harmonogramowania zadań z maszynami alternatywnymi
Języki publikacji
EN
Abstrakty
EN
The paper discusses various approaches used to solve flexible job-shop scheduling problem concentrating on formulations proposed in the last ten years. It mainly refers to the applied metaheuristic techniques which have been exploited in this research area. A comparison of presented approaches is attempted, some concluding insights are highlighted. Finally future research directions are suggested.
PL
W artykule opisano różne podejścia stosowane do rozwiązania problemu harmonogramowania zadań z maszynami alternatywnymi. Skoncentrowano się na opracowaniach opublikowanych w ostatnich dziesięciu latach. Głównie skupiono uwagę na podejściach wykorzystujących algorytmy metaheurystyczne. Dokonano próby porównania merytorycznego dostępnych w literaturze rozwiązań oraz wskazano kierunki dalszych prac.
Wydawca

Rocznik
Strony
381--388
Opis fizyczny
Bibliogr. 29 poz.
Twórcy
  • Institute of Production Engineering, Faculty of Mechanical Engineering, Cracow University of Technology
  • Institute of Production Engineering, Faculty of Mechanical Engineering, Cracow University of Technology
Bibliografia
  • [1] Lazar I., Review on solving the job-shop scheduling problem: recent development and trends, Transfer Inovacii, 23/2012, 55-60.
  • [2] Hsu T., Dupas R., Jolly D., Goncalves G., Evaluation of mutation heuristics for the solving of multiobjective flexible job-shop by an evolutionary algorithm, 2002 IEEE International Conference on Systems, Man and Cybernetics, Vol. 5, 2002.
  • [3] Das S.K., Nagendra P., Investigations into the impact of flexibility on manufacturing performance, International Journal of Production Research, Vol. 31, No. 10, 1993, 2337-2354.
  • [4] Nomden G., v.d.Zee D.J., Virtual cellular manufacturing: Configuring routing flexibility, International Journal of Production Economics, Vol. 112, 2008, 439-451.
  • [5] Pattanaik L.N., Jain P.K., Mehta N.K., Cell formation in the presence of reconfigurable machines, International Journal of Advanced Manufacturing Technology, Vol. 34, 2007, 335-345.
  • [6] Tsubone H., Horikawa M., A comparison between machine flexibility and routing flexibility, International Journal of Flexible Manufacturing Systems, Vol. 11, 1999, 83-101.
  • [7] Landers R.G., Min B.K., Koren Y., Reconfigurable machine tools, CIRP Annals – Manufacturing Technology, Vol. 50, 2001, 269-274.
  • [8] Özgüven C., Özbakir L., Yavuz Y., Mathematical models for job-shop scheduling problems with routing and process plan flexibility, Applied Mathematical Modelling, Vol. 34, 2010, 1539-1548.
  • [9] Stecke K.E., Raman N., FMS planning decision, operating flexibilities and system performance, IEEE Transactions on Engineering Management, Vol. 42, 1995, 82-90.
  • [10] Rossi A., Dini G., Flexible job-shop scheduling with routing flexibility and separable setup times using ant colony optimization method, Robotics and Computer-Integrated Manufacturing, Vol. 23, 2007, 503-516.
  • [11] Wang X.J., Zhang C.Y., Gao L., Li P.G., A survey and future trend of study on multi-objective scheduling, ICNC IEEE International Conference on Natural Computation, 2008, 382-391.
  • [12] Fattahi P., Mehrabad M S.,Jolai F., Mathematical modeling and heuristic approaches to flexible job shop scheduling problem, Journal of Intelligent Manufacturing, Vol. 18, 2007, 331-342.
  • [13] Bruker P., Schile R., Job shop scheduling with multi-purpose machine, Computing, Vol. 45, 1990, 369-375.
  • [14] Motaghedi-Iarijani A., Sabri-Iaghaie K. & Heydari M., Solving flexible job shop scheduling with multi objective approach, International Journal of Industry Engineering & Production Research, ISSN: 2008-4889, Vol. 21, 2010, 197-209.
  • [15] Demir Y., Kürnat İgleyen S., Evaluation of mathematical models for flexible job-shop scheduling problems, Applied Mathematical Modelling, Vol. 21, 2010, 197-209.
  • [16] Loukil T., Teghem J., Fortemps P., A multi-objective production scheduling case study solved by simulated annealing, European Journal of Operational Research, Vol. 179, 2007, 709-722.
  • [17] Xia W., Wu Z., An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems, Computers & Industrial Engineering, Vol. 48, 2005, 409-425.
  • [18] Fattahi P., Jolai F., Arkat J., Flexible job shop scheduling with overlapping in operations, Applied Mathematical Modelling, Vol. 33, 2009, 3076-3087.
  • [19] Dalfard V.M., Mohammadi G., Two meta-heuristic algorithms for solving multi-objective flexible job-shop scheduling with parallel machine and maintenance constraints, Computers and Mathematics with Applications, Vol. 64, 2012, 2111-2117.
  • [20] Brandimarte P., Routing and scheduling in a flexible job-shop by tabu search, Annals of Operations Research, Vol. 41, 1993, 157-183.
  • [21] Li J.Q., Pan Q., Liang Y.C., An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems, Computers & Industrial Engineering, Vol. 59, 2010, 647-662.
  • [22] Vilcot G., Billaut J.C., A tabu search and a genetic algorithm for solving a bicriteria general job shop scheduling problem, European Journal of Operational Research, Vol. 190, 2008, 398-411.
  • [23] Zhang Q., Manier H., Manier M.A., A genetic algorithm with tabu search procedure for flexible job-shop scheduling with transportation constraints and bounded processing times, Computers & Operations Research, Vol. 39, 2012, 1713-1723.
  • [24] Kacem I., Hammadi S., Borne P., Pareto-optimality approach for flexible job-shop scheduling problem: hybridization of evolutionary algorithms and fuzzy logic, Mathematics and Computers in Simulation, Vol. 60, 2002, 245-276.
  • [25] Pezzella F., Morganti G., Ciaschetti G., A genetic algorithm for the flexible job shop scheduling problem, Computers & Operations Research, Vol. 35, 2008, 3202-3212.
  • [26] Bagheri A., Zandieh M., Mahdavi I., Yazdani M., An artificial immune algorithm for the flexible job-shop scheduling problem, Future Generation Computer Systems, Vol. 26, 2010, 533-541.
  • [27] Xing L.N., Chen Y.W., Wang P., Zhao S., Xiong J., A knowledge-based ant colony optimization for flexible job-shop scheduling problems, Applied Soft Computing, Vol. 10, 2010, 888-896.
  • [28] Moslehi G., Mahnam M., A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search, International Journal of Production Economics, Vol. 129, 2011, 14-22.
  • [29] Baykasoglu A., Ozbakir L., Sonmez A.I., Using multiple objective tabu search and grammars to model and solve multi-objective flexible job-shop scheduling problems, Journal of Intelligent Manufacturing, Vol. 15(6), 2004, 777-785.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-53fdf91d-3260-4d27-b149-2f92d18ffe13
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.