Towards understanding network topology and robustness of logistics systems

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