Repair scheduling and routing and logistics support are interdependent and critical for post-disaster distribution system restoration (PDSR), which is also influenced by the geographical characteristics of outage area. Hence, we develop a co-optimization model for the PDSR with logistics support and geographical characteristics. A hybrid improved bacterial colony chemotaxis algorithm is proposed to solve the model, in which A* algorithm is employed to route repair crews and material delivery in the transportation network considering geographical characteristics, and an improved bacterial colony chemotaxis algorithm is proposed to determine the repair scheduling and material allocation in the distribution system. Different scale of distribution system instances with different damage levels and different geographical characteristics are used to demonstrate the effectiveness of the proposed methodology.

The outbreak of an epidemic disease may cause a large number of infections and a slightly higher death rate. In response to epidemic disease, both patient transfer and relief distribution are significant to reduce corresponding damage. This study proposes a two-stage multi-objective stochastic model (TMS-PTRD) considering pre-pandemic preparedness measures and post-pandemic relief operations. The proposed model considers the following four objectives: the total number of untreated infected patients, the total transfer time, the overall cost, and the equity distribution of relief supplies. Before an outbreak, the locations of temporary relief distribution centers (TRDCs) and the inventory levels of established TRDCs should be determined. After an outbreak, the locations of temporary hospitals (THs), the locations of designated hospitals (DHs), the transfer plans for patients, and the relief distribution should be determined. To solve the TMS-PTRD model, we address an improved preference-inspired co-evolutionary algorithm named the PICEA-g-AKNN algorithm, which is embedded with a novel similarity distance and three different tailored evolutionary strategies. A real-world case study of Hunan of China and 18 test instances are randomly generated to evaluate the TMS-PTRD model. The finding shows that the PICEA-g-AKNN algorithm is better than some most widely used multi-objective algorithms.