Shanhua Wu

With the surge in global maritime trade of new energy vehicles (NEVs), the roll-on/roll-off (Ro-Ro) shipping market faces a severe supply–demand imbalance, pushing shipping rates to persistently high levels. To tackle this challenge, NEV manufacturers and other automakers have begun establishing their own Ro-Ro fleets, creating an urgent need for optimized operational scheduling of these proprietary fleets. Against this context, this study focuses on optimizing the operational scheduling of automakers’ self-owned Ro-Ro fleets. Under the premise of deterministic automobile export transportation demands, a mixed-integer programming model is developed to minimize total fleet operational costs, with decision variables covering vessel port call sequence/selection, port loading and unloading quantities, and voyage speeds. A genetic algorithm is designed to solve the model, and the effectiveness of the proposed approach is validated through a real-world case study. The results demonstrate that the optimization method generates clear, actionable scheduling schemes for self-owned Ro-Ro fleets, effectively helping automakers refine their maritime logistics strategies for proprietary fleets. This study contributes to the field by focusing on automaker-owned Ro-Ro fleets and filling the research gap in cargo-owner-centric scheduling, providing a practical tool for automakers’ overseas logistics operations.

In order to enhance consumers’ experience of online shopping and to reduce their unnecessary car trips for offline shopping, a new mode, namely, establishing the virtual-shopping-experience store, is proposed in this paper. A bi-level programming model is then built with the aim of optimizing the location of the virtual-shopping-experience stores. The upper-level submodel is utilized to optimize the location of the experience stores, as well as the selection of virtual-reality (VR) devices purchased by the stores, by maximizing the social welfare generated from reducing the car trips for offline shopping after the establishment of the virtual-shopping-experience stores. The lower-level submodel is a binary Logit model, one which calculates the probability of consumers’ choices between online and offline shopping according to the locations of the experience stores output by the upper-level submodel. A genetic algorithm is adopted to solve the model. To validate the accuracy of the model, as well as that of the algorithm, case studies are carried out based on the real data collected in Dalian and Ningbo (two cities in China). The case study result demonstrates that the establishment of virtual-shopping-experience stores would contribute to reducing the frequency of car trips for offline shopping, as well as the distance of car trips for offline shopping and the time spent in car trips for offline shopping.

Focusing on bottleneck of express package delivery in rural regions, this study proposes a new potential solution, namely, Collaboration of Delivery and Sale (CDS), which integrates the express package delivery with the offline mobile sales. In order to figure out how to implement the CDS mode, the demand for offline mobile shopping is predicted based on the fitted probability distribution of customers’ arrival time, and on this basis, the vehicle routing optimisation model is developed with the objective of maximising profit of CDS service providers. The methodology is applied in three rural towns with different sizes in China. The case-study result demonstrates that adopting the CDS mode is able to greatly improve the profit of CDS service providers, and thus to resolve the problem of high delivery cost in rural regions with sparse delivery demand, and to facilitate the sustainable development of express package delivery service in remote rural regions.

In recent years, the port integration in China has been carried out vigorously, and has basically covered all coastal provinces from south to north and some areas along the Yangtze River. In order to quantify the practical effects of the port integration, this paper conducts research investigating whether or not the port integration would be able to realize the coordinated development between the port cluster and its hinterland economic system more smoothly. This paper carries out the research by building the improved coupling coordination degree model for evaluating the coupling coordination degree between the port cluster and its hinterland economic system, and further by applying the improved model to the empirical analysis of three port clusters with different integration patterns. The empirical results reveal that: (1) the port integration may lead to a “Painful Period” for the coordinated development relationship between the port cluster and its hinterland economic system in the short term, especially at the beginning of the integration, and the coupling coordination degree of the whole system would fall into a drop temporally; (2) among different port clusters with different port integration purposes and patterns, there are evident differences in the change of the coupling coordination degree of the whole system at the beginning of the integration; and (3) in the long run, the port integration would be able to effectively improve the coupling coordination degree of the port cluster and its hinterland economic system, and thus the whole system can obtain a better relationship of coordinated development.

This paper integrates empty container reposition with container shipping network design considering the container life stages. A mixed-integer linear programming model is built to determine the liner shipping network, the initial launch ports of new containers, the ports for scrapping obsolete containers, the empty container reposition scheme and the containers of different life stages being used for a specific voyage. A case study is done by taking liner shipping among China, Japan and South Korea, Southeast Asia, Europe and the US as an example. The results show that over 93% of new containers are put into use in China while 63% of old containers are scrapped in Europe or the US, and 73% of containers used for packing cargoes from China to the US are those at the Decline Stage or the Exit Stage.