A supplier selection & order allocation planning framework by integrating deep learning, principal component analysis, and optimization techniques - Islam, Samiul; Amin, Saman Hassanzadeh; Wardley, Leslie J. (Expert systems with applications, 2024)
In supply chain management, selection of suitable suppliers and allocating corresponding orders are two essential strategic decisions. Making these decisions is a complex process due to some uncertain parameters, such as future demand. This study proposes a three-stage solution framework to solve problems with supplier selection & order allocation planning. In Stage 1, a new modified relational deep learning forecasting technique is developed to forecast the demands of products. In this part, the efficiency of this modified technique is compared with two well-known forecasting techniques, namely Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and Light-Gradient Boosted Machine (LGBM). In Stage 2, a new hybrid principal component analysis method is used to generate suppliers’ weights. The results from Stages 1 and 2 are used in the multiple objectives optimization model which is developed in Stage 3. The hybrid method is used to derive a set of efficient solutions. The developed framework is discussed using a real dataset from the Canadian meat industry. The results of forecasting models show that the developed deep learning network can reduce the forecasting error by 55.42% when compared to the SARIMA method, and 13.1% when compared to the LGBM method. It is also observed that the consideration of inter-product correlation functions can change the selected suppliers and the corresponding orders.