# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 2 Related Works
### Transportation Aspects in RID  
The road status after a disaster is one of the major components to be addressed in the RID model. Because of the changed conditions of the road after the disaster, the distribution of relief items to the demand regions in the affected area must be based on real situations [27]. The transportation routes have a direct impact on the travel time and, hence, on the relief item distribution time. The change in transportation conditions has hampered the relief distribution task in post-disaster relief management [28]. This study has shown that the damage on the routes needs to be analysed to find the best alternative effective routes for transportation. The congestions on the transportation routes have serious consequences on the transportation time of the relief items to the victims as the vehicle's travel time is directly related to the road traffic congestion. A severe problem that usually occurs after a disaster is the destruction of some parts of the transportation network. As a result, some roads and links may not be accessible, which directly affects the transportation route. To analyse this, a scenario-based method has been used to cover the uncertainty and dynamic road conditions with heterogeneous fleet vehicles [29]. However, this model does not cover the vehicle selection criteria, which is crucial in an efficient distribution model. The changing road conditions highlights the need for a distribution model that covers the dynamic road conditions and the selection of appropriate vehicles for each time period to get the optimal distribution plan.  
In disaster environments, often, there is more than one means of transportation and they vary in type, cost, capacity and speed. Decision-makers need to plan the distribution system with a heterogeneous fleet of vehicles with varying cost, capacity and speed [30; 31]. These varying parameters of any vehicles need to be considered for the vehicle's transportation planning. In disaster scenarios, it is often required to determine the optimal combination of vehicles that will generate efficient ways to distribute relief items. Simultaneous optimisation with the vehicle composition and routing is required to fulfil the demands [32; 33]. The objectives are more often set as minimisation of travel time, associated operational cost and maximum utilisation of the loading capacity of the vehicles. Finding the optimum transportation routes are also useful for the relief distribution in disaster environments as the optimum transportation routes had been advantageous in terms of minimisation of travel time and associated operational cost. Different approaches had been applied over the years to generate optimised routing schedules [34; 35]. A constructive heuristic approach with a local search [36] was applied for vehicle routing where a demand sequence was generated by constructing a distribution schedule one-by-one, such that the highest priority demand appeared first. The dynamic vehicle routing was incorporated with real-time information to generate an optimised transportation schedule. The real-time information-based routing system had an optimum result in comparison to the routing based on static information [37]. Applying dynamic vehicle routing and distribution schedule total operational cost decreased. A hybrid genetic algorithm-based search [38; 39] was applied that combined the local neighbour search with genetic algorithms to explore the improved vehicle routing schedule. Minimisation of time and cost and maximisation of the utilisation of vehicle capacity and early response time have been commonly used for the vehicle selection task. However, all the objective functions have been not applied in a single model.