Abstract
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.
Highlights
Emergencies such as floods and geological disasters often generate a large number of emergency needs for emergency supplies
The research on unmanned aerial vehicles (UAVs) task planning is mostly transformed into combinatorial optimization problems, and the traditional solutions are deterministic method based on mixed integer programming (MILP), task allocation method based on market mechanism and dynamic network flow optimization method
In this paper, aiming at the task planning of UAV emergency material delivery, a mathematical 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
Summary
Emergencies such as floods and geological disasters often generate a large number of emergency needs for emergency supplies. How to rationally plan multiple UAVs in the base to quickly complete the emergency logistics tasks in many disaster-stricken areas has become an urgent problem to be solved. The rapid development of intelligent optimization algorithms in recent years provides a new way to solve UAVs task planning problems, among which population-based algorithms are common. Combining mathematical programming methods with intelligent algorithms is a new idea to solve emergency UAVs multi-objective task allocation problems. TLBO algorithm, is designed to solve the multi-objective task planning and scheduling problem of emergency UAVs. The TLBO algorithm is an efficient and intelligent optimization algorithm proposed by Rao and other scholars. Through a series of simulation experiments, the feasibility and efficiency of DMOTLBO algorithm are verified
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