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Algorytmy immunologiczne w szeregowaniu zadań produkcyjnych

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
EN
Immune algorithms in production jobs scheduling
Języki publikacji
PL
Abstrakty
EN
It is important to shorten time of new production order at-tending, time of production process and to improve productivity and to execute the total production programme. Scheduling problem is one of the major issues on the quick response to continuously changing customers' demands. In the paper, the Multi Objective Immune Algorithm (MOIA) for scheduling problems is proposed. The goal of MOIA is to generate a Pareto curve that enables the decision-maker to make explicit trade-offs between conflicting objective functions. The decision-maker can use two, three or four criteria in optimisation process: makespan minimization; total tardiness minimization; total flow time minimization; total idle time of machines minimization. The decision-maker is released from his obligation to have knowledge about used criteria and their interactions in process of objective functions' weights defining. MOIA reaches one most suitable solution from Pareto-optimal solutions set. In order to find best searching strategy of best schedule various researches were made. MOIA has been used for solving flow shop, job shop and open job shop scheduling problems. The production system can deal with sequence dependent setup times or normal setup times, and production batch flow can be parallel or combination of the series flow and parallel flow. MOIA reaches compromise solutions for contradictive criteria of scheduling problems in short period of time.
Rocznik
Strony
47--56
Opis fizyczny
Bibliogr. 23 poz.
Twórcy
autor
autor
  • Instytut Automatyzacji Procesów Technologicznych i Zintegrowanych Systemów Wytwarzania, Politechnika Śląska (032)237 16 01 (032)237 16 57, bozena.skolud@polsl.pl
Bibliografia
  • [1] Alisantoso D., Khoo L. P., Jiang P. Y.: An immune algorithm approach to the scheduling of a flexible PCB flowshop, Int J Adv ManufTechnol 2003,22, pp. 819-827.
  • [2] Arroyo J.E.C.: Genetic local search for multi-objective flowshop scheduling problems. "European Journal of Operational Research" 2005, Vol. 167, pp. 717-738.
  • [3] Bertel S.: A genetic algorithm for an industrial multi-processor flow shop scheduling problem with recirculation, "European Journal of Operational Research" 159,651-662.
  • [4] Bilkay O., Anlagan O., Kilic S.E: Job shop scheduling using fuzzy logic, Int J Adv Manuf Technol 2004, no. 23, pp. 606-619.
  • [5] Brans J. P., Vincke P. H.: A preference ranking organisation method, "Management Science", Vol. 31, No. 6 (1985), pp. 647-656.
  • [6] Cai X., Li K. N.: A genetic algorithm for scheduling staff of mixed skills under multi-criteria, "European Journal of Operational Research", no. 125 (2000), pp. 359-369.
  • [7] Cochran J. K., Horng S-M, Fowler J. W.: A multi-population genetic algorithm to some multi-objective scheduling problems for parallel machines, "Computers & Operational research" 2003, Vol. 30, pp. 1097-1102.
  • [8] Gajpal Y., Pajendran Ch.: An ant-colony optimization algorithm for minimizing the completion-time variance of jobs in flowshops, "International Journal of Production Economics" 101 (2006), pp. 259-272.
  • [9] Gupta K.: Simulation based multiobjective schedule optimization in semiconductor manufacturing, Proceedings of the 2002 Winter Simulation Conference.
  • [10] Kurz M., E., Askin R., G.: Scheduling flexible flow lines with sequence-dependent setup times, "European Journal of Operational Research", no. 159 (2004), pp. 66-82.
  • [11] Kis T.: A review ofexact solution methods for the non-preemptive multiprocessor flowshop problem, "European Journal of Operational Research", no. 164 (2005), pp. 592-608.
  • [12] Lova A.: A multicriteria heuristic method to improve resource allocation in multiproject scheduling, "European Journal of Operational Research" no. 127, pp. 408-424.
  • [13] Low Ch., Yip Y., Wu T-H.: Modelling andheuristics of FMS scheduling with multiple objectives, "Computers and Operations Research", no. 33 (2006), pp. 674-694.
  • [14] Mattfeld D. C: An efficient genetic algorithm for job shop scheduling with tardiness objectives, "European Journal of Operational Research", no. 155 (2004), pp. 616-630.
  • [15] Montusiewicz J.: Ewolucyjna analiza wielokryterialna w zagadnieniach technicznych, Instytut Podstawowych Problemów Techniki PAN, Warszawa 2004.
  • [16] Ponnambalam S. G., Ramkumar V., Jawahar N.: A TSP-GA multi-objective algorithm for flowshop scheduling, Int J Adv Manuf Technol 2004, Vol. 23, pp. 909-915.
  • [17] Ruiz-Torres A. J.: Generating Pareto schedules with outsource and internal parallel resources, "International Journal of Production Economics", no. 103 (2006), pp. 810-825.
  • [18] Skołud B.: Planowanie wieloasortymentowej produkcji rytmicznej. Zeszyty naukowe Politechniki Śląskiej. Mechanika z. 136, Gliwice 2000.
  • [19] Skolud B., Wosik I.: Multi-objective genetic and immune algorithms for butch scheduling problem with dependent setups. Recent developments in artificial intelligence methods, Gliwice 2007, s. 185-196.
  • [20] Skołud B., Wosik I.: Logika rozmyta w przeszukiwaniu wielokryterialnej przestrzeni rozwiązań dla problemu szeregowania zadań, "Metody i techniki zarządzania w Inżynierii Produkcji", rocznik II, Bielsko-Biała 2008, s 173-182.
  • [21] Skołud B., Wosik I.: An immune algorithm and fuzzy logic for multicriteria optimization in the area of scheduling (w druku).
  • [22] Wosik I.: The multi-objective immune algorithm fine tuning using fuzzy logic. 5lh International PhD Conference on Mechanical Engineering, Pilsen, s. 309-312.
  • [23] Wróblewski K..: Podstawy sterowania przepływem produkcji, WNT, Warszawa 1993.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-LOD9-0005-0006
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