Fuzzy constraint handling technique used with genetic algorithms to optimize order quantity
DOI:
https://doi.org/10.15587/1729-4061.2014.27915Keywords:
genetic algorithms, penalty functions, ordering goods, credit, deficit, natural loss, incoming inspection, lackAbstract
The paper presents the problem of determining the optimal volume of ordering goods with the deferred delivery cost payment taking into account input control errors, the time factor when making financial calculations and inflation. Solving this task is difficult non-trivial process that requires applying evolutionary optimization methods that do not depend on the choice of the starting point and do not need additional constrains on the objective function characteristics.
The most efficient way to find the optimum of constrained problems using evolutionary algorithms is applying adaptive and problem-oriented penalty functions. However, the main problem that accompanies their use is the solution quality sensitivity to the choice of the individual parameters of penalty elements, calculation methods of which are not always known.
The paper proposes using fuzzy penalty functions, the main idea of which is to replace the constraints as inequalities by a set of fuzzy variables. The algorithm for implementing the method in full search space of possible solutions was explored. The aspects of software implementation of the technology were examined. The experimental verification of the method was performed, and the results of a comparative analysis of the dynamic and adaptive penalty functions were given.
References
Ryghykov, Yu. I. (2001). Theory of waiting line and inventory management, StP: Piter, 384.
Jaber, M. Y. (2009). Inventory management: non-classical view, Boca Raton: CRC Press Taylor Francis Group, 228.
Chung, K. J., Liao, J. J. (2004). Lot-sizing decisions under trade credit depending on the ordering quantity. Computers & Operation Research, 31 (6), 909–928. http://dx.doi.org/10.1016/s0305-0548(03)00043-1
Shah, N. H., Raykundaliya, N. (2009). Optimal inventory policies for Weibull deterioration under trade credit in declining market. Indus Journal of Managment & Social Sciences, 3 (2), 11–20.
Tripathy, C. K., Pradhan, L. M. (2012). An EOQ model for three parameter Weibull deterioration with permissible delay in payments and associated salvage value. International Journal of Industrial Engineering Computations, 3 (2), 115-122. http://dx.doi.org/10.5267/j.ijiec.2011.07.004
Hou, K. L., Lin, L. C. (2009). A cash flow oriented EOQ model with deteriorating items under permissible delay in payments. Journal of Applied Sciences, 9 (9), 1791–1794. http://dx.doi.org/10.3923/jas.2009.1791.1794
Hsu, J.-T., Hsu, J.-T. (2013). Two EPQ models with imperfect production processes, inspection errors, planned backorders, and sales returns. Computers & Industrial Engineering, 64 (1), 389–402. http://dx.doi.org/10.1016/j.cie.2012.10.005
Yegorova, O. (2014). Fuzzy expected value model with inspection errors and two level of trade credit in one replenishment cycle. Information Models and Analyses, 3 (1), 37–52.
Subbotin, S. O. (2008). Representation and processing of knowledge in artificial intelligence and design support systems, Zaporizhia: ZNTY, 341.
Weise, T. (2009). Global optimization algorithms – Theory and application, http://it-weise.de/projects/book.pdf/ (Last accessed: 06.10.2014).
Brownlee, J. (2011). Clever algorithms: Nature-inspired programming recipes, Melbourne: LuLu, 436.
Kramer, O. (2010). A review of constraint-handling techniques for evolution strategies. Applied computational Intelligence and Soft Computing, 2010, 1–11. http://dx.doi.org/10.1155/2010/185063
Coello Coello, C. A. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: А survey of the state of the art. Computer Methods in Applied Mechanics and Engineering, 191 (11-12), 1245–1287. http://dx.doi.org/10.1016/s0045-7825(01)00323-1
Goldberg, D. (1989). Genetic algorithms in search, optimization and machine learning, Addison-Wesley, Reading, MA.
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Copyright (c) 2014 Ольга В’ячеславівна Єгорова, Ігор Олегович Пасішний
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