On the value of shipment consolidation and machine learning techniques for the optimal design of a multimodal logistics network

Abstract

We study a multimodal logistics network for a multi-echelon supply chain (SC) with multiple products, considering economic and environmental sustainability and shipment consolidation (ShC). The SC logistics network is modelled as a Mixed Integer Linear Program (MILP) and then tested on randomly generated but realistic test instances. The effects of ShC in SC network design on economic and environmental costs are analyzed, showing that consolidation decreases the SC cost, especially when the distance between the shipper and receiver is significant. Moreover, machine learning (ML) approaches for predicting stochastic parameters using historical data are evaluated compared to the more traditional stochastic programming approaches over multiple prediction periods. The three ML models utilized; namely, Attention CNN-LSTM, Attention ConvLSTM and an ensemble of both models using Support Vector Regression, performed significantly better than the stochastic programming approaches considered (simple recourse and chance-constrained) in all scenarios. The numerical examples show that the MILP models using the predictions from the ML algorithms provide the highest value of the stochastic solution and the lowest expected value of perfect information. This study makes a case for the continued integration of ML prediction methodologies into stochastic optimization modelling in the setting of sustainable SC logistics design problems.