Two-echelon Inventory Model with Uncertainty Demand

Abstract

The objectives of this research are to develop a two-echelon inventory model with uncertain demand and to compare three meta heuristics method such as dragonfly algorithm (DA), particle swarm optimization (PSO) and bat algorithm (BA). Two products with same demand. and unequal demand are studied in various iteration, number of products in each packet,  purchasing price per unit, back-order cost per unit, shortage cost for each unit that is lost sale, constant cost per order, buyer’s constant product cost for each setup,  vendor’s holding cost per unit time per unit and  buyer’s holding cost per unit time per unit parameters. Minimum total inventory costs are investigated. The results show that PSO better than the other method when iteration less than 50, for iteration greater than or equal to 50, DA is superior and stable while PSO is stable when iteration more than 300. BA method is inferior for all cases.