Qi Liao

The energy transition is driving batch transport systems toward multi-product, high-frequency operations, with more batches and smaller batch volumes. However, strong couplings among material flows, operational decisions and temporal constraints make large-scale scheduling increasingly difficult, often rendering direct optimization computationally infeasible within practical engineering timeframes. This challenge is particularly pronounced in large-scale multi-product pipeline systems. To address this, this study proposes a multi-stage engineering framework that enables feasible and scalable scheduling of large-scale, batch-centric transport systems. The framework integrates two complementary mechanisms: a coarse-to-fine feasibility identification strategy that progressively narrows the admissible operational space, and a divide-and-conquer temporal decomposition strategy that partitions long scheduling horizons into tractable sub-horizons. The proposed framework is validated using two distinct industrial-scale pipeline systems. In a six-product benchmark, the framework achieves zero or negligible optimality gaps. For a complex branching network, it obtains feasible, high-quality schedules within the engineering time limit, whereas direct optimization fails to achieve a gap below 3%. Temporal decomposition reduces active binary variables and improves solution quality by over 50%. The framework provides a generalizable approach for large-scale batch transport and emerging multi-carrier energy infrastructures.

The global energy landscape is undergoing a transformative shift, driven by the urgent need to address climate change, enhance energy efficiency, and integrate renewable energy sources