Artificial neural network model for cost optimization in a dual-source multi-destination outbound system

Abstract

Cost optimization is one of the most important issues in distribution operations of any manufacturing system. Most real life problems are non-deterministic polynomial-time hard, and solving such problems are quite challenging. Managing Dual Source multi-destination Inventory system is extensively more difficult than managing a single source multi-destination inventory structure. Undesirably, most managers rely on traditional method while making allocation decision. There is need for efficient and robust computational algorithm. This study emphasizes the importance of creative algorithm, artificial neural network (ANN) in decision-making. ANN model was applied to a double-source multi-destination system in a paint manufacturing company. The accuracy of the model was evaluated using mean square error and correlation coefficient (®values for actual and predicted standards. ANN Feed-Forward Back-Propagation learning with sigmoid transfer function [3–10–1–1] was considered using 74% of available data for training and 26% for testing and validation. The result showed that the proposed method (ANN) outperforms the classical method in use. Approximately 17% of the current operational cost was saved using the soft computing technique.

Keywords

• artificial neural network
• transhipment
• dual source
• multi-destinations