# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 5 Comparative Performance Analysis  
For the comparative analysis of the model performance, ideally the models are compared with each other by analysing performance parameters (in this case: total distribution time and total operational cost as plotted in Figures 4 and 5, respectively), but the relief item distribution conditions are not same in all these models. Therefore, the simple numerical quantitative performance measure is not the best suitable option to compare the models. To evaluate the model performance, the models are compared with their feasibility, their effectiveness in the distribution of relief items and how closely the models are with the real-case disaster environments. For the analysis, the best distribution sequence after the 70 generations is selected as a distribution plan as listed in Table 7. It can be observed that the distribution sequence is not uniform. Each model generates a different optimum distribution sequence based on available resources and disaster information. These models are an effective alternative to the generated distribution schedule under different circumstances depending on the information and resources available. The static limited vehicle scheduling model is the  
Figure 4: Distribution time plots of different dynamic RID Models. Figure 5: Operational cost plots of different dynamic RID Models. simplest way to make a distribution plan, but with the limited vehicle condition, it may not be an effective method to distribute to all the disaster regions. To overcome the limitation of the static distribution model, dynamic RID models have been applied. Four different dynamic models with different decision parameters have been applied. In the first dynamic RID model, a static resource information condition is used. Optimised distribution sequences, based on minimised distribution time and total operational cost, have been presented as the distribution sequence. The generation of dynamic distribution schedules maximises the use of vehicles and minimises the unmet demand. The model has a limitation as it does not have rationalised distribution in terms of covering disaster regions' priorities, though it has the advantage of simplicity from an implementation point of view. In the second dynamic RID model, dynamic distribution schedules are generated with fuzzified road conditions. Road conditions andtraffic conditions are used to get a fuzzified equivalent travel distance. With the dynamic update on the fuzzified travel distance, the shortest travel path has also been changed dynamically. The inclusion of a fuzzified distance matrix reflects a highly realistic travel route selection in a disaster environment. However, this model does not have rationalised distribution in terms of covering priorities, which appears as a limitation of this model. The fuzzy rules are generated based on expert opinions, which is also a limitation, as expert opinions may vary. In the third dynamic RID model, multiple priorities in terms of casualties and wait-time have been used. The priority has been calculated in each time slot with the updated information. Hence, the optimised sequences have been generated for the distribution of relief items covering multiple priorities. However, this model has a limitation in terms of static shortest path selection for vehicle transportation. The advantage of this model is that it covers heterogenous vehicles in the distribution plan along with the causality priority. In the fourth dynamic RID model, the strengths of all the discussed models are combined