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Outline
Demand uncertainty and inland port congestion pose significant challenges to the efficient operation of port collection and distribution systems. This paper proposes a bi-level programming model that simultaneously account for demand uncertainty and inland port congestion. The upper-level model optimizes inland port location and capacity configuration by minimizing construction costs and congestion costs, while the lower-level model optimizes transport paths by minimizing transportation costs, transfer costs, carbon emission costs, and time costs. A Genetic Algorithm is employed to solve the model, with empirical analysis based on data from the Liaoning port cluster and its northeastern hinterland. The results show that higher-grade inland ports effectively absorb demand fluctuations and reduce congestion costs, while reducing carbon emissions by increasing the share of railway transport. A maximum regret value of θ=0.25 achieves the optimal balance between total cost and stability of the Liaoning network. Additionally, a carbon tax of ￥0.5/kg achieves the best emission reduction and congestion control outcomes.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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