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2010 | 16 | nr 104 Data Mining and Business Intelligence | 148--165
Tytuł artykułu

A Novel Intelligent Method to Support Operations Management in Clusters

Warianty tytułu
Nowatorska inteligentna metoda wspomagająca zarządzanie operacyjne w klastrach
Języki publikacji
EN
Abstrakty
W artykule autorka proponuje inteligentną metodę wspomagającą zintegrowane podejmowanie decyzji operacyjnych w nowych strukturach biznesu. Podejście koncentruje się na interakcjach pomiędzy różnymi firmami klastra, na poziomie zarządzania operacyjnego, w celu ulepszenia procesów wytwarzania. Autorka zaproponowała nowatorską metodę do zespołowego harmonogramowania w klastrze przemysłowym. Do tego celu jest rozbudowywany algorytm genetyczny zaproponowany przez autorkę w 2008 r. Nowy algorytm genetyczny do zespołowego harmonogramowania jest oparty na kodowaniu operacji, gdzie każdy chromosom jest zbiorem 4-pozycyjnych genów. Zaproponowana metoda jest weryfikowana eksperymentalnie. Zaprezentowana w artykule analiza pokazuje, że nowy algorytm genetyczny zaproponowany przez autorkę może być wykorzystany do udoskonalenia szczegółowego harmonogramowania w klastrach. Ponadto zaproponowany algorytm genetyczny może wspomagać planistów w planowaniu zleceń transportu. To podejście może być zastosowane w dynamicznym środowisku, w którym ponowne harmonogramowanie jest inicjowane przez nieoczekiwane zmiany.(abstrakt oryginalny)
EN
In this paper, the author proposed a novel intelligent method to support an integration of operating decision making in new structures of business. This approach focuses on interactions between the various firms within a cluster at operations management level in order to improve manufacturing processes. The author proposed a novel intelligent method for a collective scheduling in an industrial cluster. For this purpose, the genetic algorithm proposed by the author in previous work by Ławrynowicz [2008] is developed. The new genetic algorithm for the collective scheduling is based on operation codes, where each chromosome is a set of 4-positions genes. The proposed method is verification on some experiments. The analysis presented in the article shows that the new genetic algorithm proposed by the author can be used to improve a detailed scheduling in the cluster. Moreover, the proposed genetic algorithm may aid planners in transport orders planning. It can be applied in a dynamic setting when rescheduling is initiated by unexpected changes.(original abstract)
Twórcy
  • Warsaw School of Economics, Poland
Bibliografia
  • Arroyo J.B.C., Armentano V.A. (2005), Genetic local search for multi-objective flow shop scheduling problems, European Journal of Operational Research, Vol. 167, pp. 717-738.
  • Buckley P.J. (2009), The rise of the Japanese multinational enterprise: Then and now, Asia Pacific Business Review, Vol. 15, pp. 309-320.
  • Chan F.T.S., Chung S.H., Chan P.L.Y. (2005), An adaptive genetic algorithm with dominated genes for distributed scheduling problems, Expert System with Applications, Vol. 29, pp. 364-371.
  • Chen K.J., Ji P. (2007), A genetic algorithm for dynamic advanced planning and scheduling (DAPS) with frozen interval, Expert Systems with Applications, Vol. 33, pp. 1004-1010.
  • Cheng R., Gen M., Tsujimura Y. (1996), A tutorial survey of job-shop scheduling problems using genetic algorithms. Part I: Representation, Computers and Industrial Engineering, Vol. 30, pp. 983--997.
  • Franca P.M., Gupta J.N.D., Mendes A.S., Moscato P., Veltink K.J. (2005), Evolutionary algorithms for scheduling a flowshop manufacturing cell with sequence dependent family setups, Computers & Industrial Engineering, Vol. 48, pp. 491-506.
  • Gao J., Gen M., Sun L., Zhao X. (2007), A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems, Computers & Industrial Engineering, Vol. 53, pp. 149-162.
  • Goldberg D., Lingle R. (1985), Alleles, loci and the traveling salesman problem, [in:] Proceedings of the First International Conference on Genetic Algorithms, Ed. J.J. Grefenstette, Lawrence Erl-baum, Associates, Hillsdale, NJ, pp. 154-159.
  • Jia H.Z., Fuh J.Y.H., Nee A.Y.C., Zhang Y.F. (2007), Integration of genetic algorithm and Gantt chart for job shop scheduling in distributed manufacturing systems, Computers & Industrial Engineering, Vol. 53, pp. 313-320.
  • Kobbacy K.A.H., Vadera S., Rasmy M.H. (2007), AI and OR in management of operations: History and trends, Journal of the Operational Research Society, Vol. 58, pp. 10-28.
  • Ławrynowicz A. (2006), Hybrid approach with an expert system and a genetic algorithm to production management in the supply net, Special Issue: Intelligent Systems in Operations Management, Intelligent Systems in Accounting, Finance and Management, Vol. 14, pp. 59-76.
  • Ławrynowicz A. (2007), Production planning and control with outsourcing using artificial intelligence, International Journal Services and Operations Management, Vol. 3, pp. 193-209.
  • Ławrynowicz A. (2008), Integration of production planning and scheduling using an export system and a genetic algorithm, Journal of the Operational Research Society, Vol. 59, pp. 455-463.
  • Ławrynowicz A. (2009), A new genetic algorithm for job shop scheduling in supply networks, [in.] Proceedings of the Fourth European Conference on Intelligent Management Systems in Operations, Eds. K.A.H. Kobbacy, S. Vadera, University of Salford and The OR Society, Greater Manchester, pp. 101-110.
  • Moon C, Sen Y, Yun Y, Gen M. (2006), Adaptive genetic algorithm for advanced planning in manufacturing supply chain, Journal of Intelligent Manufacturing, Vol. 17, pp. 509-522.
  • Morya K.K., Dwivedi H. (2009), Aligning interests of SMEs and a focal firm (MNE) in a global supply chain setup, The Icfai University Journal of Supply Chain Management, Vol. VI, pp. 49-59.
  • Niu K.H., (2009), The involvement of firms in industrial clusters: A conceptual analysis, International Journal of Management, Vol. 26, pp. 445-455.
  • Ruiz R., Maroto C. (2006), A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility, European Journal of Operational Research, Vol. 169, pp. 781-800.
  • Sheremetov L., Rocha-Mier L. (2008), Supply chain network optimization based on collective intelligence and agent technologies, Human Systems Management, Vol. 27, pp. 31-47.
  • Ying-Hua C, Young-Chang H. (2008), Dynamic programming decision path encoding of genetic algorithms for production allocation problems, Computers & Industrial Engineering, Vol. 54, pp. 53-65.
  • Zobolas G.I., Tarantilis CD., Ioannou G. (2009), A hybrid evolutionary algorithm for the job shop scheduling problem, Journal of the Operational Research Society, Vol. 60, pp. 221-235.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000169070908

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