Towards understanding network topology and robustness of logistics systems

Advanced integration of logistics systems has been promoted for competitiveness and sustainability. Interconnection of transport operations increases complexity at a network level, which reduces the predictability of the response of the system to disruptions. However, our understanding of the behavior of such systems is still limited. In particular, the topology of the network, which changes as the systems are integrated, is an important factor that affects the performance of the entire system. Knowledge of such mechanisms would be useful in the design and evaluation of integrated logistics. Here, we developed a simple simulation framework for logistics networks that extracts the essence of the problem. We performed extensive numerical experiments for three scenarios that mimic changes in demand: (i) locally and temporally increased traffic demand, (ii) globally and temporally increased traffic demand, and (iii) permanent change in demand pattern, under various conditions on the type of route-finding algorithm, network structure, and transportation capacity. Adaptive route-finding algorithms were more effective in square lattice and random networks, which contained many bypass routes, than in hub-and-spoke networks. Furthermore, the square lattice and random networks were robust to the change in the demand. We suggest that such preferable properties are only present in networks with redundancy and that the bypass structure is an important criterion for designing network logistics. We also performed a realistic case study that mimics interregional truck transport in Japan and confirmed that our conclusions are applicable to a practical problem.