A new supply chain distribution network design for two classes of customers using transfer recurrent neural network

Supply chain management integrates planning and controlling of materials, information, and finances in a process which begins from suppliers and ends with customers. Optimal planning decisions made in such a distribution network usually include transportation, facilities location, and inventory. This study presents a new approach for considering customers’ differentiation in an integrated location-allocation and inventory control model using transfer recurrent neural network (RNN). In this study, a location and allocation problem is integrated with inventory control decisions considering two classes of strategic and non-strategic customers. For the first time, a novel transfer RNN is applied to estimate parameters in order to reach to a near optimal solution. The proposed mathematical model is multi-product, single-period, multi-transportation mode, and with multilevel capacity warehouses with two classes of customers based on a critical level policy. The transfer RNN approach is used to transfer knowledge from a similar domain to the problem domain in this study. The performance result is compared with the condition when no transfer learning approach is applied. The exact calculation method is demonstrated for small scale instances while hybrid meta-heuristic algorithms (Genetic and Simulated Annealing) developed for real size samples. Finally, a sensitivity analysis is carried out for different instances to evaluate the effect of different indexes on the running time and total cost value of the objective function.