Data-Driven Two-Stage Robust Optimization Allocation and Loading for Salt Lake Chemical Enterprise Products Under Demand Uncertainty

Abstract

Most enterprises rely on railway transportation to deliver their products to customers, particularly in the salt lake chemical industry. Notably, allocating products to freight spaces and their assembly on transport vehicles are critical pre-transportation processes. However, due to demand fluctuations from changing product orders and unforeseen railway scheduling delays, manually adjusted allocation and loading may lead to excessive loading and unloading distances and times, ultimately increasing transportation costs for enterprises. To address these issues, this paper proposes a data-driven two-stage robust optimization (TSRO) framework embedding with the gated stacked temporal autoencoder clustering based on the attention mechanism (GSTAC-AM), which aims to overcome demand uncertainty and enhance the efficiency of freight allocation and loading. Specifically, GSTAC-AM is developed to help predict the deviation level of demand uncertainty and mitigate the impact of potential outliers. Then, a robust counterpart model is formulated to ensure computational tractability. In addition, a multi-stage hybrid heuristic algorithm is designed to handle the large scale and complexity inherent in the freight space allocation and loading processes. Finally, the effectiveness and applicability of the proposed framework are validated through a real case study conducted in a large salt lake chemical enterprise.