Abstract
Inventory control is the science-based art of controlling the amount of inventory (or stock) held, in various forms. Inventory control techniques are very important components and the most organizations can substantially reduce their costs associated with the flow of materials. This paper presents biological swarm intelligence in general, and in particularly two models: particle swarm optimization and firefly algorithm for modelling on inventory control in production system. The aim of this research is to create models to minimize production cost according to price of items and inventory keeping cost.
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References
Lewis, C.: Demand Forecasting and Inventory Control: A Computer Aided Learning Approach. Wiley, New York (1998)
Bartmann, D., Beckmann, M.J.: Inventory Control: Models and Methods. Springer, Heidelberg (1992)
Simić, D., Simić, S.: Evolutionary approach in inventory routing problem. Lecture Notes in Computer Science, vol. 7903, pp. 395–403. Springer (2013)
Simić, D., Simić, S.: Hybrid artificial intelligence approaches on vehicle routing problem in logistics distribution. In: Hybrid Artificial Intelligence Systems. LNCS, vol. 7208, pp. 208–220. Springer, Heidelberg (2012)
Simić, D., Svirčević, V., Simić, S.: A hybrid evolutionary model for supplier assessment and selection in inbound logistics. J. Appl. Log. 13(2, Part A), 138–147 (2015)
Simić, D., Kovačević, I., Svirčević, V., Simić, S.: 50 years of fuzzy set theory and models for supplier assessment and selection: a literature review. J. Appl. Log. 24(part A), 85–96 (2017)
Simić, D., Kovačević, I., Svirčević, V., Simić, S.: Hybrid firefly model in routing heterogeneous fleet of vehicles in logistics distribution. Log. J. IGPL 23(3), 521–532 (2015)
Keynes, J.M.: The General Theory of Employment, Interest, and Money (reprint edition). Macmillan and Co., London (1949)
Samanta, B., Al-Araimi, S.A.: An inventory control model using fuzzy logic. Int. J. Prod. Econ. 73(3), 217–226 (2001)
Madamidola, O.A, Daramola, O.A Akintola, K.G.: Web – based intelligent inventory management system. Int. J. Trend Sci. Res. Dev. 1(4), 164–173 (2017)
Šustrová, T.: A suitable artificial intelligence model for inventory level optimization. Trends Econ. Manag. 25(1), 48–55 (2016)
Zhivitskaya, H., Safronava, T.: Fuzzy model for inventory control under uncertainty. Central Eur. Researchers J. 1(2), 10–13 (2015)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE International Conference on Evolutionary Computation, pp. 69–73 (1998)
https://www.mathworks.com/matlabcentral/fileexchange/53142-inventory-control-using-pso-in-matlab. Accessed 8 Mar 2018
Niknam, T., Amiri, B.: An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Appl. Soft Comput. 10(1), 183–197 (2010)
Mahadevana, K., Kannan, P.S.: Comprehensive learning particle swarm optimization for reactive power dispatch. Appl. Soft Comput. 10(2), 641–652 (2010)
Pedersen, E.M.H., Chipperfeld, A.J.: Simplifying particle swarm optimization. Appl. Soft Comput. 10(2), 618–628 (2010)
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Simić, D., Ilin, V., Simić, S.D., Simić, S. (2019). Swarm Intelligence Methods on Inventory Management. In: Graña, M., et al. International Joint Conference SOCO’18-CISIS’18-ICEUTE’18. SOCO’18-CISIS’18-ICEUTE’18 2018. Advances in Intelligent Systems and Computing, vol 771. Springer, Cham. https://doi.org/10.1007/978-3-319-94120-2_41
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DOI: https://doi.org/10.1007/978-3-319-94120-2_41
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