Yunqiang Yin

To address uncertain demand during the pandemic, hospitals have utilized supply chain strategies by coordinating their available medical service capacity laterally. In this coordination, hospitals either request medical service capacity from external sources or mobilize part of their own capacity to support other hospitals, which influences the latter’s capacity decisions. Optimizing these lateral coordination strategies and addressing challenges such as oversupply and inequity are critical issues in collaborative hospital responses. This paper examines how hospitals adjust their medical service capacity and transshipment capacity through proactive and reactive policies under the leveraging scheme of the Integrated County Healthcare Consortium (ICHC). We find that ICHC strategy decisions are influenced not only by demand size and the gap between hospitals but also by demand fluctuations. Additionally, even if hospitals engage in lateral coordination, their roles — either as a receiver or a source — are affected by both demand uncertainty and the ICHC’s leveraging scheme. Regarding equity, we identify a hump-shaped relationship between the leveraging scheme of the ICHC, the quantity of service capacity transshipped, and equity. We propose a more balanced solution where the ICHC sets a scheme that allows the receiving hospital to dominate coordination decisions, thus benefiting both economic outcomes and equity.

The U-shaped yard is an important part of the U-shaped automated container terminal (U-ACT), which consists of a set of blocks used for storing containers, I-lanes for automated guided vehicles (AGVs) travel, and U-lanes for external trucks (ETs) travel. Double-side cantilever yard cranes (DCYCs) perform stacking and unstacking operations for containers transported by AGVs and ETs. Managing and coordinating the operations of DCYC, AGVs, and ETs, not only improves the operation efficiency of U-ACTs but also helps to promote the development of green ports. This paper addresses the problem of scheduling DCYCs and path planning for AGVs and ETs in the U-ACT. To achieve this, we establish a bi-objective mixed integer programming model to minimize both the makespan and the energy consumption. The model considers conflicts between two DCYCs within each block, ensures workload balance for these DCYCs, optimizes parking slots for AGVs and ETs, and schedules appropriate entry times for AGVs and ETs into the yard to reduce conflicts. To solve this model, we develop an improved multi-objective particle swarm optimization (IMOPSO) algorithm, where a globally optimal heuristic search mechanism and several conflict avoidance strategies to plan conflict-free spatiotemporal paths are introduced to accelerate the convergence of the algorithm. Numerical experiments demonstrate the superiority of the IMOPSO approach in terms of multiple metrics, confirming the effectiveness of the optimized vehicle entry timing strategy, which improves efficiency by 7.33% and yields energy savings by 11.72%. These findings clearly highlight that our model and solution approach can effectively enhance the operational efficiency of DCYCs, AGVs, and ETs, contributing to the overall improvement of container terminal operations.

We investigate the impact of Typhoon Lekima, a source of environmental uncertainty, on delivery performance, utilizing data from RiRiShun Logistics. We apply a staggered difference‐in‐differences model across low‐complexity and high‐complexity supply chains. The analysis uncovers distinct dynamics throughout the typhoon's forecast, active, and recovery phases. Notably, the active phase experiences less disruption in delivery performance compared to the forecast phase, owing to RiRiShun's wait‐and‐see policy. This approach leads to order accumulation but subsequently enables more effective resource allocation. In the recovery phase, high‐complexity supply chains demonstrate significant improvements, surpassing pre‐disaster performance levels. The study further emphasizes the critical role of two response mechanisms in managing high‐complexity supply chains. Increasing throughput efficiencies at destination centers and implementing transfer centers prove effective in enhancing delivery performance during both the forecast and active phases. However, the simultaneous application of these response mechanisms during the forecast phase unexpectedly reduces performance.