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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Zmodyfikowana procedura rozmieszczenia pożywienia w algorytmie sztucznej kolonii pszczół w zastosowaniu do zarządzania łańcuchem dostaw
Języki publikacji
Abstrakty
The present study deals with the management of supply chain using an updated Artificial Bee Colony (ABC) algorithm named UABC. UABC employs a linear combination of Gaussian and Cauchy distributions to update the candidate food positions from the older ones in memory. Optimization of a supply chain model is an integer programming problem or a constrained integer-mixed problem, for which suitable modifications are done in the algorithm. Statistical analysis of the proposed variant when compared with three ABC based algorithms indicates its efficiency and validity.
Opisane w pracy badania dotyczą zarządzania łańcuchem dostaw przy zastosowaniu zmodyfikowanego algorytmu sztucznej kolonii pszczół (ang: Artificial Bee Colony - ABC), zwanego UABC (ang. Updated Artificial Bee Colony). Algorytm ten wykorzystuje liniową kombinację rozkładów Gaussa i Cauchy’ego w celu uaktualnienia pozycji rozmieszczenia pożywienia. Optymalizacja modelu łańcucha dostaw jest problemem programowania całkowitoliczbowego lub problemem programowania mieszanego z ograniczeniami. Opracowany algorytm UABC uwzględnia te informacje. Analiza statystyczna algorytmu UABC, w porównaniu z trzema innymi algorytmami opartymi na ABC, wskazuje na jego wydajność i poprawność.
Słowa kluczowe
Wydawca
Czasopismo
Rocznik
Tom
Strony
113--119
Opis fizyczny
Biliogr. 20 poz., rys.
Twórcy
autor
- Department of Applied Sciences and Engineering, Indian Institute of Technology, Roorkee, India
autor
- Department of Applied Sciences and Engineering, Indian Institute of Technology, Roorkee, India
Bibliografia
- Berning, G., Brandenburg, M., Gürsoy, K., Kussi, J.S., Mehta, V., Tölle, F.J., 2004, Integrating collaborative planning and supply chain optimization for the chemical process industry (I) – methodology, Computers and Chemical Engineering, 28, 913-927.
- Chellapilla, K., 1998, Combining Mutation Operators in Evolutionary Programming. IEEE Transactions on Evolutionary Computation, 2, 91–96.
- Almeder, C., Preusser M., Hartl, R.F., 2009, Simulation and optimization of supply chains: alternative or complementary approaches?, OR Spectrum, 31, 95–119.
- Bredstrom, D., Lundgren, J.T., Ronnqvist, M., Carlsson, D., Mason, A., 2004, Supply chain optimization in the pulp mill industry––IP models, column generation and novel constraint branches, European Journal of Operational Research, 156, 2-22.
- Goldberg, D.E., Deb, K., 1991, A comparison of selection schemes used in genetic algorithms, Foundations of Genetic Algorithms, eds, Rawlins, G.J.E., Morgan Kaufmann, San Mateo, 69-93.
- Hugos, M., 2003, Essentials of supply chain management, John Wiley & Sons, New Jersey.
- Jeong, B., Jung H.-S., Park, N.-K., 2002, A computerized causal forecasting system using genetic algorithms in supply chain management, The Journal of Systems and Software, 60, 223-237.
- Karaboga, D., 2005, An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Kayseri, Turkey: Erciyes University.
- Karaboga, D., Basturk, B., 2007, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of Global Optimization, 39, 171–459.
- Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N., 2012, A comprehensive survey: artificial bee colony (ABC) algorithm and applications, Artificial Intelligence Review, DOI 10.1007/s10462-012-9328-0.
- Mak, K.L., Wong, Y.S., 1995, Design of integrated productioninventory- distribution systems using genetic algorithm. Proc. Conf. Genetic Algorithms in Engineering Systems: Innovations and Applications, Glasgow, UK, 454-460.
- Michalewicz, Z., Schoenauer, M., 1996, Evolutionary algorithms for constrained parameter optimization problems, Evolutionary Computation, 4, 1–32.
- Shapiro, J.F., 2001, Modeling the Supply Chain, Thomson Learning, Duxbury.
- Sharma, T.K., Pant, M., Bansal, J.C., 2012, Artificial Bee Colony with Mean Mutation Operator for Better Exploitation, Proc. Conf. IEEE Congress on Evolutionary Computation, Brisbane, Australia, 1-7.
- Smirnov, A. V., Sheremetov, L. B., Chilov N., Cortes, J. R., 2004, Soft-computing technologies for configuration of cooperative supply chain, Applied Soft Computing, 4, 87-107.
- Stäblein, T., Baumgärtel, H., Wilke, J., 2007, The Supply Net Simulator SNS: An artificial intelligence apporach for highly efficient supply network simulation. Proc. Conf. Management logistischer Netzwerke, eds, Günther, H.O., Mattfeld, D.C., Suhl, L., 85–110.
- Subotic, M., 2011, Artificial bee colony algorithm with multiple onlookers for constrained optimization problems, Proc. Conf. European Computing Conference, Paris, 251-256.
- Syarif, A., Yun, Y, Gen, M., 2002, Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach, Computers & Industrial Engineering, 43, 299-314.
- Zhou, G., Min, H., Gen, M., 2002, The balanced allocation of customers to multiple distribution centers in the supply chain network: a genetic algorithm approach, Computers & Industrial Engineering, 43, 251-261.
- Zhu, G, Kwong, S., 2010, Gbest-guided artificial bee colony algorithm for numerical function optimization, Applied Mathematics and Computation, 217, 3166-3173.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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