چكيده به لاتين
Supply chain management is a set of approaches that integrate suppliers, manufacturers, vendors, and retailers. As a result the right product can be produced and distributed at the right amount right time and place. Inventory management is one of the main elements in the supply chain. Vendor inventory management is one of the agile supply chain strategies that reduces the response time of customer demand through synchronization between supply chain members.
In this study, a two-level closed loop supply chain and a single product for perishable goods, including a vendor and several retailers are investigated. A product after a certain time period of its lifetime, called a critical time, begins to rot due to probability distribution function. The product may not be sold after reaching critical times. For this purpose, the system management will apply a price discount to stimulate costumer demand. However it is likely even after the price reduction some of the product will not be sold and will be completely wasted. Considering the importance of the corrosive environmental effects of the product and the resulting biological concerns, management system collects and, if possible recycles the perished products. The model under study is a nonlinear planning model that minimizes the total function of the system's total cost including cost of holding, fixed ordering, price reduction, deterioration, collection and recycling, as well as the replenishment cycle and retail order size and the time needed to generate each customer's supply through the proposed model. Since the model is a NP-hard problem, after validating and solving it with Lingo software (LINGO 17.0X64) in small dimensions, the genetic algorithm and particle swarm optimization algorithm (MATLAB R2014.a) were developed to solve it in large dimensions. The results showed that the particle swarm optimization algorithm is more suitable for solving the model.
Keywords: Closed Loop Supply Chain, Vendor Managed Inventory, Prishable Products, Algorithm GA, Algorithm PSO
