## 5 Current Trends
emergency operation could also be observed, as can be seen in [64,95]. Interestingly, autonomous vehicles are also addressed, such as in [115] as well as ship routing in [102]. Meanwhile, several works addressed their work as an explicit DSS. For example, ref. [39,47,52] in supply and delivery operation as well as [114,123] in evacuation operations. These types of applications usually also utilise the aid of external information, such as [52,123], which applied the GIS, as well as [111], which aid evacuation through the BIM. Other research, such as in [102], make use of the geographical topography information to compute decreasing speed during the humanitarian operation. Some other works worth mentioning are [37], which addresses the fairness in terms of sharing relief aid among shelters. The importance of fairness is also highlighted in [38,51,90,106]. The importance of including livestock in an evacuation plan is highlighted in [121], while the mitigation phase of a disaster is addressed by [45] in restocking earthquake shelters. Ref. [55] synced the supervisory visit at a medical centre with the resupply of the pharmacy through PDVRP. Ref. [60] tackles the problem of dynamic route conditions via the incorporation of drones and land vehicles with the drone acting as the eye. Ref. [62] addressed the delivery process during drought, which is uncommon in the literature, with [72] addressing the blood donation problem. Meanwhile, a unique consideration is taken in [94], which addressed the possibility of an attack from desperate victims by constraining the delivery operation only via convoy. Ref. [112] addresses the flow of debris, which causes harm during evacuation, while [126,131] focuses on evacuating victims with disability. On the other hand, ref. [116] first addressed the flow of evacuees during evacuation before considering the routing problem and, finally, refs. [143,144] addressed both the connectivity network and the respective emergency vehicle. The application of ML as solution approach is truly lacking in this scope of research. A complete approach of ML could only be seen in [29], which incorporates MDP as part of the reinforcement learning approach. Ref. [140], on the other hand, introduced learning by maximising rewards and applied this to agent based modelling instead. The exact solution approach on the other hand shows a frequent application of the Dijkstra algorithm, CG method, A star algorithm, as well as many other unspecified commercial solvers from CPLEX, Gorubi, and LINGO, among others. It is interesting to note that all of the papers applied commercial solver instead of using the open source platform, such as GLPK, COIN-OR, and SCIP. Furthermore, it could be observed that the classified VRP that is solved by exact solution approach ranges from a simple problem, such as [32] with only one vehicle, to complex problem solving for multi-objectives, inter-modal, multi-trip, fuzzy environment, stochastic, dynamic problem, as well as a combination of the OR problem, such as LRP and ARP. The same could be said for the heuristic solution approaches in solving problems for various ranges of complexity. Among the well known heuristics applied in this scope of survey include Clarke and Wright heuristic, greedy heuristic, sweep heuristic, local search, and sequential insertion heuristic. Other heuristics that are not commonly applied in this scope include Best Time Flow Insertion heuristic and Lagrangian Relaxation based heuristic. Furthermore, a tailored heuristic could be seen in [124], with the cooperative multi-agent based algorithm heuristic. GA is commonly applied as a metaheuristic solution approach. Here, various versions of GA could be seen, such as NSGA-II, matrix encoding based GA, and parallel GA. Some metaheuristic are especially applied for solving multi-objectives such as NSGA-II and MOPSO. Furthermore, ACO is popularly adapted, especially for the case where swapping and mutation could not yield feasible solutions.