Multiobjective analytical evolutionary algorithm for train stowage planning problem of steel industry

The train stowage planning problem (TSPP) of the steel industry aims to select steel coils and allocate them to trains cost-effectively. It is a key component in the transportation of steel products. This study focuses on a multiobjective train stowage planning problem (MoTSPP) that maximises both the loading efficiency of the crane and the loading rate of the train. The MoTSPP also considers operation constraints related to steel coils, train wagons, and stowage modes in real-life railway transportation. An integer programming model is established to mathematically describe this problem. To obtain an efficient solution, a multiobjective analytical evolutionary algorithm (MAEA) that combines evolutionary algorithm (EA) with machine learning (ML) is presented. The EA part is a multiobjective differential evolution that introduces guided evolution and parameter adaptation to produce promising individuals and parameters, respectively. ML part adopts clustering algorithm and surrogate model to accelerate the search. Extensive comparisons and insight analyses are conducted from various perspectives to demonstrate the effectiveness and efficiency of the MAEA for solving the MoTSPP.

Automated summary

This study focuses on a multiobjective train stowage planning problem (MoTSPP) that aims to maximize loading efficiency and loading rate of the train. A multiobjective analytical evolutionary algorithm (MAEA) that combines evolutionary algorithm (EA) with machine learning (ML) is presented to obtain efficient solutions. The effectiveness and efficiency of the MAEA are demonstrated through extensive comparisons and insight analyses.