## 1 Introduction  
Following increased events of disasters in the past decades, the urgent need for effective Disaster Management (DM) has been the focus in various academic fields. Being the centre of this topic, humanitarian operations are the key to alleviating suffering when disasters strike. This includes Humanitarian Logistics (HL) as the key in handling post-disaster distribution operations, ensuring that the critical aids are efficiently and effectively delivered to the victims [1]. Aside from HL, humanitarian operations, such as evacuation and rescue operations, are of equal importance within the scope of DM. Humanitarian operation, according to the UN Office for the Coordination of Humanitarian Affairs (OCHA), is operations conducted in order to alleviate human suffering, focusing on civilians, due to extenuating circumstances where service support is often time lacking or non-existence [2]. However, existing theoretical approaches for optimization methods in humanitarian operations rarely account for the real situation of the event, which often includes the disruption of critical facilities, such as the transportation network consisting of the roads and highways. Moreover, chaotic situations could arise due to an uncertain volume of demands and critical conditions that victims may suffer from, such as being trapped in a damaged building or that the road may become congested due to unplanned panic evacuation. This gives rise to various theoretical approaches within the field of Operations Research (OR) in addressing the humanitarian operations in such challenging settings. One of the prominent scopes within the field of OR is the Vehicle Routing Problem (VRP), where the effort to address the humanitarian operations could be traced back as early as 1988 with the work of [3]. Despite almost three decades of research, deterministic problems continue to dominate this field of research, despite the advancement of solution approaches, such as the exact solution, heuristic and metaheuristic solution approaches as well as various hybrid solution approaches. However, the deterministic problem is not practical for problems that involve uncertain parameters and periodic changes of parameters as one would perceive during the time of disaster. Such a real-world problem demands that the dynamic and stochastic elements of the problem be addressed [4]. Nonetheless, limited literature addressed the rich VRP (RVRP) for the humanitarian operations that, often, would include the stochastic and dynamic elements as well as other attributes, such as multi-objective, multi-trip operations, split deliveries, and time windows, among others. However, it is challenging to apply exact solution approaches for RVRP that best describes the humanitarian effort operations. This stems from the fact that even the basic VRP is an NP-hard problem, especially for practical problem instances. The same problem is faced by Machine Learning (ML) that is based on the dynamic programming approach that suffers the three curses of dimensionality [5]. At this point, the computation for the exact solution approach or dynamic programming to obtain one true optimal solution would become prohibitive and, eventually, a trade-off needs to be made between the quality of solution and computation ability. For such cases, most researchers and practitioners would turn towards heuristic or metaheuristic solution approaches, where a near-optimal solution could be obtained within a reasonable computation time. Instead of iteratively searching for all potential solutions and evaluating them, as would be done in exact solution approach and dynamic programming, heuristic and metaheuristic work by attempting to avoid evaluating all of the potential solutions in the solution pool while using a controlled randomised solution selection and try to reach a near-optimal value by iteratively improving the current potential solution until no improvement is detected within a reasonable computation time.