vendor managed inventory of a supply chain under stochastic demands

in this research, an integrated inventory problem is formulated for a single-vendor multiple-retailer supply chain that works according to the vendor managed inventory policy. the model is derived based on the economic order quantity in which shortages with penalty costs at the retailers` level is permitted. as predicting customer demand is the most important problem in inventory systems and there are difficulties to estimate it, a probabilistic demand is considered to model the problem. in addition, all retailers are assumed to share a unique number of replenishments where their demands during lead-time follow a uniform distribution. moreover, there is a vendor-related budget constraint dedicated to each retailer. the aim is to determine the near optimal or optimal order quantity of the retailers, the order points, and the number of replenishments so that the total inventory cost of the system is minimized. the proposed model is an integer nonlinear programming problem (nilp); hence, a meta-heuristic namely genetic algorithm (ga) is employed to solve it. as there is no benchmark available in the literature to validate the results obtained, another meta-heuristic called firefly algorithm (fa) is used for validation and verification. to achieve better solutions, the parameters of both meta-heuristics are calibrated using the taguchi method. several numerical examples are solved at the end to demonstrate the applicability of the proposed methodology and to compare the performance of the solution approaches.