## 1 Introduction  
Disaster logistics management, which involves planning and organizing the distribution of logistics to provide humanitarian assistance, is an important aspect of disaster management. It is crucial to minimize human suffering, property loss, and ensure a timely and appropriate response. However, disaster logistics management frequently faces a number of challenges. First, disaster relief logistics costs can reach up to 80% of total aid costs [1, 2], making logistics one of the most expensive components of disaster management [3]. Second, logistics management occurs frequently because aid distribution is not on target or evenly distributed [4]. This is due to the selection of unsuitable distribution center locations and distribution routes that have not reached the expected level of effectiveness. A dynamic facility location problem model for managing humanitarian aid logistics is required to overcome problems in the management of humanitarian aid in post-disaster areas. The problem of locating emergency facilities is the most fundamental humanitarian logistical problem. The problem of locating emergency facilities following a disaster is related to the location routing problem (LRP), which is used to optimize the location allocation of facilities and routes for humanitarian aid delivery. LRP problems are difficult combinatorial problems that are frequently categorized as NP-hard [5; 6]. LRP combines two kinds of problems: facility location problems and vehicle routing problems. The combination of these two issues makes LRP a difficult problem because it necessitates complex decision making when selecting the location of facilities and efficiently planning vehicle routes. Several models and approaches have been proposed to address the location facility problem in relief logistics and LRP management, including MOGWO, MOPSO, MOWCA, NSGA-II [7], hybrid genetics (genetics and NSGA-II) [8], a combination of the multi-objective simulated annealing algorithm (MOSA) and non-dominated sorting genetic algorithm II (NSGA II) [9], a combination of discrete particle swarm optimization (DPSO) and Harris Hawks optimization (HHO) [10], greedy algorithm [11; 12], tabu search [10; 13; 14; 15], mixed integer linear programming (MILP) [16], mixed integer non-linear programming (MINLP) [17], particle swarm optimization (PSO) [18], and e-constraints [19; 20; 21; 22]. Based on the research described, it is possible to conclude that the LRP can be solved using two approaches: the exact approach and the heuristic approach. Because the exact method takes a solution-finding approach by calculating every possible solution to produce the optimal answer, the exact algorithm takes a long time to complete to produce an optimal answer for large-scale problems. In the case of optimization problems, heuristics is a rule-of-thumb approach that allows for solutions that are not convergent or optimal [23]. As a result, the goal of this research is to create a model for dealing with LRP in the context of disaster relief logistics. Several researchers propose an artificial intelligence approach to optimize the objective function and address the solution problem in LRP [24]. Deep learning is a machine learning method that employs deep and complex artificial neural networks to extract patterns and features from data [25]. Several studies using the deep learning approach have made significant contributions to disaster management problem solving [26].