The Genetic Algorithms for Dynamic Order Volume with Randomly and Inconsecutively Demands
Article Sidebar
Main Article Content
Abstract
The problems of inventory management demand which has random variation of products as well as demand in each interval which have many opportunities in discrete value are the problems that quite difficult to find quantity order as demand have many alternatives. If we did not receive products immediately after being ordered, we had to plan the long-term condition for a while on order to get a suitable option. The solutions for this problem have many ways, this report will thus present two solutions. They are 1) Stochastic Integer Linear Programming (SILP) and 2) Genetic Algorithm (GA) by defining the stable products prices that do not vary by the time before testing with 24 model problems. As a consequence, both solutions provided the right answer for random demand dynamic ordering problem. However, the time to find the answers are of difference. SILP can provide more accurate answer compared with GA, but SILP is proper for small problems which less than 6 periods and GA proper for big problems which has complexity of less than 11 periods.
Article Details
References
[2] F. S. Hillier and G. J. Lieberman, Introduction to Operations Research, 9th ed., Raghothaman Srinivasan, United States: McGraw-Hill Higher Education, 2010, pp. 451–457.
[3] V. Rungreunganun, P. Sukchareonpong, and P. Charnsethikul, “Economic order quantity with discrete random time varying demands,” presented at the National Operations Research Conference, 2004, pp. 18–34.
[4] V. Rungreunganun, P. Sukchareonpong, and P. Charnsethikul, “Optimization based heuristics applied from markov decision process case study: silver jewelry factory,” presented at the National Operations Research Conference, 2005, pp. 147–162.
[5] H. M. Wagner and T. M. Whitin, “Dynamic version of economic lot size model,” Management Science, vol. 5, pp. 89–96, Oct 1958.
[6] X. Ding and C. Wang, “A novel algorithm of stochastic chance-constrained linear programming and its application,” Mathematical Problems in Engineering, vol. 2012, pp. 1–17, 2012.
[7] S. Cherdchoongam and V. Rungreunganum, “Forecasting the price of natural rubber in thailand using the ARIMA model,” KMUTNB Int J Appl Sci Technol, vol. 9, no. 4, pp. 271–277, 2016.
[8] S. Chotayakul, J. Pichitlamken, and P. Charnsethikul, “Capacitated dynamic lot size inventory model eith stochastic demand: A case study of an ATM network,” Ladkrabang Engineering Journal, vol. 2, pp. 49–54, Jun. 2014.
[9] N. Choosuk and A. Kengpol, “An application of forecasting models for the supply and demand management of cassava products,” KMUTNB Int J Appl Sci Technol, vol. 9, no. 3, pp. 175–187, 2016.
[10] G. S. Piperagkas, I. Konstantaras, K.Skouri, and K. E. Parsopoulos, “Solving the stochastic dynamic lot-sizing problem through natureinspired heuristics,” Computers & Operations Research, vol. 39, no. 7, pp. 1555–1565, Jul. 2012.
[11] H. Tempelmeier and T. Hilger, “Linear programming models for a stochastic dynamic capacitated lot sizing problem,” Computers & Operations Research, vol 59, pp. 119–125, Jul. 2015.