摘要：The outbreak of unexpected events such as floods and geological disasters often produces a large number of emergency material requirements, and when common logistics methods are often ineffective, emergency logistics unmanned aerial vehicles (UAVs) become an important means. How to rationally plan multiple UAVs to quickly complete the emergency logistics tasks in many disaster-stricken areas has become an urgent problem to be solved. In this paper, an optimization model is established with the goal of minimizing the task completion time and the penalty cost of advance/delay, and a discrete multi-objective teaching–learning-based optimization (DMOTLBO) algorithm is proposed. The Pareto frontier approximation problem is transformed into a set of single objective sub-problems by the decomposition mechanism of the algorithm, and each sub-problem is solved by the improved discrete TLBO algorithm. According to the characteristics of the problem, TLBO algorithm is improved by discretization, and an individual update method is constructed based on probability fusion of various mutation evolution operators. At the same time, variable neighborhood descent search is introduced to enhance the local search ability. Based on the multi-level comparative experiment, the improvement measures and effectiveness of DMOTLBO are verified. Finally, combined with specific case analysis, the practicability and efficiency of the DMOTLBO algorithm in solving the multi-objective emergency logistics task planning problem of multiple UAVs are further verified.
