Multimodal transport hubs (MTH) improve the operations between different transport modes, creating quasi-seamless connections between origin-destination locations. These hubs can fully use the advantages of various modes of transportation and avoid the limitations and cost pressures caused by a single mode. However, locating where to situate these hubs is a complex and critical process. Recent site location selection studies only assume that the hub is unstable under a single transportation mode, with uncertain demand and uncertain costs, which is very different from reality. This work approaches the hub selection problem following a particle swarm-simulated annealing algorithm constrained by transportation demand and cost. A case study was conducted in Northeast China, selecting six hubs responsible for about 93% of the cargo flow in the network. Our results suggest that selecting transportation demand and cost for a comprehensive analysis of hub location selection will optimize the hub layout and reduce the total system cost. The scale of freight efficiency would be improved compared to the existing transportation network.

In this paper, we evaluate the performance of a supply chains (SCs) under uncertainty with different components such as direct costs, operational costs, transaction expenses, order lead time, product flexibility and net profit. Data Envelopment Analysis (DEA) can be used for measuring the performance of supply chain problems. On the other hand, robust optimization approach is a powerful technique for handling problems faced with various environmental uncertainties. This paper combines these two concepts and proposes a method to evaluate SCs performance. The results of the proposed method, under any different environmental situation, show which ranking of SC’s performance is better in a network. The preliminary results of the implementation of a real-world case study indicates that the method could be successfully used for performance measurement.