A model-experience-driven method for the planning of refined product primary logistics

Logistics planning is regarded as the most complex part of supply chain management for refined products. A vital knowledge gap still exists in understanding the trade-offs between the economy and the practicability of logistics schemes. Focus on this issue, this paper proposes a model-experience-driven method for the planning of refined product primary logistics. The method couples three sub-modules: (1) use coordinator's preference information and convex function interpolation to construct satisfaction indicator; (2) set up a multi-objective model for logistics coordination and optimization considering supply adjustment and secondary delivery; (3) adopt the augmented e-constraint method to obtain the Pareto solutions and balance the economy and satisfaction indicators. The method is verified by a small-scale system, where the satisfaction degree increases by 77% while the logistics cost remains unchanged. The method is also successfully applied to a large-scale system with 29 refineries and 196 market depots, where Pareto logistics schemes are obtained and the supply-demand imbalance is greatly eased. The proposed method can help provide theoretical guidance for real-world logistics planning.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

Logistics planning is the most complex part of supply chain management for refined products. This paper proposes a model-experience-driven method that includes three sub-modules to address the trade-offs between economy and practicability in logistics schemes. The method is verified and successfully applied to small and large-scale systems, improving satisfaction degree and easing supply-demand imbalance.