Optimizing a vendor managed inventory (VMI) supply chain for perishable products by considering discount: Two calibrated meta-heuristic algorithms

• Published in: Computers & industrial engineering 2017-01, Vol.103, p.227-241
• Main Authors: Akbari Kaasgari, Maryam, Imani, Din Mohammad, Mahmoodjanloo, Mehdi
• Format: Article
• Language: English
• Subjects: Algorithms; Costs; Discount; Discounts; Genetic algorithm; Inventory management; Mathematical models; Particle swarm optimization; Perishable supply chain; Stockpiling; Supply chains; Swarm intelligence; Vendor managed inventory
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2016.11.013

•The discount is considering as depend on product lifetime.•The discount is considering in VMI model.•The perishable product is considering in VMI model. Vendor Managed Inventory (VMI) is one of the inventory management strategies that reduce costs, increase responsiveness and improve collaboration between the members of supply chain. Although the VMI can reduce response time and deterioration in perishable supply chain (PSC), but there is a few reports on using VMI for PSC. In this paper VMI strategy is used for managing the inventory of perishable product at two-level supply chain with single vendor and multiple retailers. After passing a specific time of product lifetime that called the critical time, the product would be perished by a probability distribution function. It is probable that the inventory of product is not sold after the critical time, therefore the management system will use discount to stimulate demand. Then a proposed model is formulated as a nonlinear programming model. The objective function of the proposed model is minimizing the total cost of supply chain including the cost of fixed ordering, holding, discount, and deterioration whereas replenishment cycles and order size for retailers and also production time needed to supply inventory of each retailer can be determined through the proposed model. Since the model is a NP-hard problem, a Genetic algorithm (GA) and a Particle Swarm Optimization (PSO) algorithm are developed for solving it appropriately and the results are presented that PSO algorithm has a better performance for solving of the proposed model in this paper. Taguchi method is an applied to calibrate the parameters of the algorithms into provide reliable solution. Finally, the conclusion and further research are presented.