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Liczba wyników
2009 | nr 85 Advanced Information Technologies for Management - AITM 2009 | 170--178
Tytuł artykułu

A Novel Intelligent Method for Task Scheduling in Industrial Cluster

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper the author proposes a new genetic algorithm (NGA) for a scheduling problem in a local supply network (in other words industrial cluster - IC). The new genetic algorithm enables not only a manufacturing scheduling in IC. Additionally, NGA aids planners in transport orders planning. New genetic algorithm employs two steps to encode the scheduling problem in IC. In the first step, each chromosome type A represents a potential optimal solution of a problem being optimized. Chromosome type A is a set of 4-positions genes. The value of the first position represents the job, the value of the second position the operation number, the next value the resource number or the order transport, and the last value the factory number or the source of the transport order. The second step is to copy the first and the second position from the gene of the chromosome A into the gene of the chromosome B, and to translate the last two positions from the gene of the chromosome A into one position of the gene of the chromosome B. The cases study shows that proposed by the author new genetic algorithm is effective in solving the scheduling problems in local supply networks. (original abstract)
Twórcy
  • Warsaw School of Economics, Poland
Bibliografia
  • Arroyo J.E.C., Armentano V.A. (2005), Genetic local search for multi-objective How shop scheduling problems, European Journal of Operational Research, Vol. 167, pp. 717-738.
  • Chan F.T.S., Chung S.I I., 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 1. Representation, Computers and Industrial Engineering, Vol. 4, 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.
  • 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. 1, pp. 10-28.
  • Ławrynowicz A. (2007), Production planning and control with outsourcing using artificial intelligence, International Journal Services and Operations Management, Vol. 2, 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. 4, 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.
  • 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.
  • Ying-Hua C., Young-Chang 11. (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 C.D., Ioannou G. (2009), A hybrid evolutionary algorithm for the job shop scheduling problem, Journal of the Operational Research Society, Vol. 2, pp. 221-235.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000169337076

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