Optimal logistics scheduling with dynamic information in emergency response: case studies for humanitarian objectivesCao, J. (Avtor) Han, H. (Avtor) Wang, Y. J. (Avtor) Han, T. C. (Avtor) logistichumanitarian logisticsoptimizationmulti-objectivedynamic informationdelay costBenders decomposition algorithmmixed integer programmingant colony optimization algorithmgenetic algorithmThe mathematical model of infectious disease is a typical problem in mathematical modeling, and the common infectious disease models include the susceptible-infected (SI) model, the susceptible-infected-recovered model (SIR), the susceptible-infected-recovered-susceptible model (SIRS) and the susceptible-exposed-infected-recovered (SEIR) model. These models can be used to predict the impact of regional return to work after the epidemic. In this paper, we use the SEIR model to solve the dynamic medicine demand information in humanitarian relief phase. A multistage mixed integer programming model for the humanitarian logistics and transport resource is proposed. The objective functions of the model include delay cost and minimum running time in the time-space network. The model describes that how to distribute and deliver medicine resources from supply locations to demand locations with an efficient and lower-cost way through a transportation network. The linear programming problem is solved by the proposed Benders decomposition algorithm. Finally, we use two cases to calculate model and algorithm. The results of the case prove the validity of the model and algorithm.Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering20232026-02-18 12:25:11Članek v reviji97109slContent from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.