Multimodal transport is a process of effectively moving cargoes in a single container by combining land transport (road or rail) and maritime or river transport (vessel or barge) in one transport chain. However, cold chain logistics (CCL), as a special while major kind of cargo delivery, has not been incorporated with this beneficial combination. In order to realize efficient delivery of cold chain foods (CCF), in this study, the characteristics of multimodal and CCL are analyzed and integrated to select the optimal logistics path. In establishing the path-selection model, customer satisfaction is introduced, which is reflected by arrival punctuality and the quality of CCF. An improved particle swarm optimization algorithm (IPSO) is introduced to address the model and is proven to retain a fast convergence rate and achieve outstanding solving accuracy through the experimental study. Sensitivity analysis is also conducted to present the impact of railway speed and cost variation on path selection. Results show that compared with highway transport, railway transport is preferable to the medium and long distance. The influence of railway speed improvement is more striking than cost reduction in motivating decision makers to choose railway transport mode in logistics operations.

This paper proposed a framework to integrate metro for urban logistics delivery in order to avert the conflicts with ground transportation, reduce delivery cost, transport distance and delivery time of vehicles. Integrating metro-based Underground Logistics System (ULS) with ground delivery vehicles (GDVs), an urban logistics delivery path optimization model based on a single metro line was built to improve delivery efficiency. In handling this model, total transport cost was taken as the objective function by considering the capacity limitations of GDVs and soft customer time window (CTW), vehicle routing problem with Time Windows (VRPTW) was also taken into account. By means of the saving algorithm (CW algorithm) and the improved Tabu Search algorithm (CWTS), our established model was solved to optimize the delivery routes under metro-based ULS. Through the case analysis of a Logistics Transport Enterprise (LTE) in Nanjing, it was shown that our proposed algorithm consistently outperformed Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) in taking less computation time and obtaining better results. Our proposed delivery mode also had a great advantage compared to the traditional truck delivery (TD) alone in terms of saving cost and guaranteeing the quality of service (QoS). The sensitivity analysis of the capacity offered an optimal GDV capacity setting to reduce total delivery cost.

This paper develops a location optimization method about the metro-based underground logistics system (MULS) to transport freight by metro during off-peak periods. First, we make qualitative and quantitative feasibility analysis of the MULS through questionnaires and field surveys in metro stations. The analysis result shows that more than 85.22% of interviewees support logistics delivery by the metro. An improved p-median model is developed, which considers four influencing factors. The shortest path algorithm is used to minimize the transport costs while the costs of the remaining factors are calculated using the collected data. Then, the Voronoi diagram is adopted to optimize the location of candidate metro stations and redraw the logistics service scope by adding weighted terms. Finally, the Nanjing metro is chosen as a case study to validate the effectiveness of the developed method. The optimization result shows the total cost of the logistics delivery is reduced by 33. 27% suggesting that the method can be used to reduce logistics costs and improve delivery efficiency in urban areas.

As an important supplement of conventional fixed-route public transit, flexible transit services draw more attention in low-demand areas recently. Demand-responsive connector is one of those flexible transit services and has already been operated as feeder transit in some cities. This article concentrates on the vehicle routing operation of the demand-responsive connector system with on-demand stations. A two-stage routing model is proposed to minimize the system cost, considering both the service provider and riders, and two solution algorithms are proposed and compared for the routing model. A simulation experiment based on a real-life case study in Nanjing city is conducted to demonstrate the applicability of the proposed vehicle routing model. The results demonstrate that our vehicle routing modeling and optimized algorithm can confidently handle the daily operation of demand-responsive connector services with on-demand stations.