Optimal scale and capacity integration in a port cluster under demand uncertainty
Optimal scale and capacity integration in a port cluster under demand uncertainty
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DOI：
10.1016/j.cie.2022.108733
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Liquan Guo;C. Jiang
中科院分区：
文献类型：
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作者：
Liquan Guo;C. Jiang
本文研究了需求不确定条件下港口群的能力整合问题，包括确定多阶段港口群中各港口的最优能力投资和退出决策。基于真实的期权理论，本文首先研究了不确定条件下无集成条件下单个无能力港口的最优投资和退出决策问题。其次，基于设施选址理论中的连续近似法，提出了港口群的最优规模，为现有港口群的能力整合提供了优化目标。第三，构建多阶段容量投资与退出模型，以最小化现有港口群下的社会福利与最优规模下的社会福利之间的差距为目标。此外，我们提出了一个基于动态规划的模式搜索算法（DP-GPS）来解决所提出的模型。最后，以中国海港为例，给出了单个港口的最优投资和退出决策。结果表明，当集水区的潜在海运需求大于某一阈值时，最优投资容量随潜在海运需求线性增加，而最优退出容量随潜在海运需求线性减小。最后，以辽宁港口群为例，对MCIEI模型和DP-GPS算法进行了验证。结果表明，在最优的能力整合方案中，锦州港和大连港应选择退出，营口港和丹东港应选择投资。我们的研究成果有助于指导港口集群中的港口整合，并进一步在沿海地区建立资源节约型港口。
We address the problem of capacity integration in a port cluster under demand uncertainty, which involves determining the optimal capacity investment and exit decisions for the ports in a port cluster over multiple stages. Based on real option theory, we first investigate the optimal investment and exit decisions for a single incapacitated port without integration under uncertainty. Second, we present the optimal scale of a port cluster based on the continuous approximation-based approach in facility location theory, which offers an optimization objective for capacity integration in an existing port cluster. Third, we build a multistage capacity investment and exit (MCIEI) model to minimize the gap between the social welfare under an existing port cluster and the social welfare at the optimal scale of a port cluster. In addition, we present a dynamic programming-based pattern search algorithm (DP-GPS) to solve the proposed model. Finally, the optimal investment and exit decisions for a single port are derived from the case of Chinese seaports. The results indicate that when the potential maritime demand of the catchment area is above a threshold, the optimal investment capacity increases linearly in the potential maritime demand while the optimal exit capacity decreases linearly in the potential maritime demand. Furthermore, the MCIEI model and the corresponding DP-GPS algorithm are verified with respect to the Liaoning port cluster in mainland China. The numerical results illustrate that the Jinzhou and Dalian Ports should adopt exit decisions in the optimal capacity integration scheme, while the Yingkou and Dandong Ports should opt to invest. Our research outcomes can help guide port integration in port clusters and further establish resource-saving ports in coastal regions.