1 A Multi-Product Multi-Period Inventory Control Problem under Inflation and Discount: A Parameter-Tuned Particle Swarm Optimization Algorithm Seyed Mohsen Mousavi, M.Sc. Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia, Email: mousavi.mohsen8@gmail.com Vahid Hajipour, M.Sc. Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran, Phone: +98 281 3665275, Fax: +98 281 3665277, Email: v.hajipour@qiau.ac.ir Seyed Taghi Akhavan Niaki 1 , Ph.D. Department of Industrial Engineering, Sharif University of Technology, P.O. Box 11155-0414 Azadi Ave, Tehran 1458889694 Iran, Email: Niaki@Sharif.edu Najmeh Aalikar, M.Sc. Department of Science and Technology, Universiti Kebangsaan Malaysia,43600, Bangi, Selangor, Malaysia Email: najme.alikar@gmail.com Abstract In this paper, a seasonal multi-product multi-period inventory control problem is modeled in which the inventory costs are obtained under inflation and all-unit discount policy. Furthermore, the products are delivered in boxes of known number of items and in case of shortage, a fraction of demand is considered backorder and a fraction lost sale. Besides, the total storage space and total available budget are limited. The objective is to find the optimal number of boxes of the products in different periods to minimize the total inventory cost (including ordering, holding, shortage, and purchasing costs). Since the integer nonlinear model of the problem is hard to solve using exact methods, a particle swarm optimization (PSO) algorithm is proposed to find a near optimal solution. Since there is no benchmark available in the literature to justify and validate the results, a genetic algorithm (GA) is presented as well. In order to compare the performances of the two algorithms in terms of the objective function and the required CPU time, they are first tuned using the Taguchi approach, in which a metric called “smaller is better” is used to model the response variable. Then, some numerical examples are provided to demonstrate the application and to validate the results obtained. The results show that while both algorithms have statistically similar performances, PSO tends to be the better algorithm in almost all problems. Keywords: Multiproduct inventory control; Inflation; Discounts; Shortages; Particle swarm optimization; Genetic algorithm; Taguchi method 1 Corresponding author