# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 4 Computational Experiments
### Dynamic RID Scheduling
#### 4.2.4 Dynamic Distribution Model with Fuzzy Travel Distance, Multi-Priority and Sliding Time Window  
Several distribution models have been applied to generate the distribution schedule [67; 68; 69; 70; 71; 72]. However, the major limitation of these distribution models has been the use of static information. However, disaster scenarios are dynamic, which leads to including past, current and future updates in the distribution plan [48]. Some dynamic models have been implemented for short-term demand, covering the uncertainty of demand across the disaster regions [70] and multiple stochastic periods [41], but these models are based on the estimation of the future update based on present data. The use of estimation may reduce the effectiveness of the distribution task as the actual data may be different than the estimated data. The dynamic model is only effective if regular timely updates on resources, transportation fleet and vehicle routing routes are implemented in the model [73]. An effective relief item distribution plan must be determined by considering all possible demand scenarios [74]. Relief demand, available resources and route information changes dynamically, therefore, the initial distribution plan needs to be optimised with the change in information [67]. The inclusion of simultaneous factors for disaster relief operations assists in implementing an efficient distribution plan [75]. Considering these limitations and needs, a combined dynamic RID model with fuzzy distance and multi-priority with sliding time window is applied together in the proposed model, which reflects the closest modelling of the real-time disaster environments where the decision-makers must integrate the disaster region priority and road conditions for vehicle transportation along with the changing information of each time-slot. To address the need for multiple factors in a single model, this proposed model covers the disaster conditions relief item distribution in the optimum possible way. The dynamic RID model with fuzzy travel distance, multi-priority and sliding time window generates an optimised distribution schedule at each time slot based on the information update. The optimised distribution schedule at time slot 0 is generated with the available information. With the information update at each time slot, the distribution schedule generated at the previous time slot is re-optimised. For the computational experiment, five time slots are selected for the relief item distribution. Initial relief demand is assumed as of the Chi-Chi case study and additional demand that appears in the next time slots are synthesised as listed in Table 4. In this table, time slot 0 lists the initial demand, and time slot 1 to time slot 4 lists the additional demand that appears across all the demand regions. Vehicle availability of each type at each time slot is synthesised as listed in Table 5 (T0-T4). The optimum demand sequence is found after optimising the demand sequence for 70 generations. The optimum demand sequence at Time Slot 0 shows some of the disaster regions being served at Time Slot 0 (marked with bold font) whereas other demand regions in the sequence (marked with normal font) receive relief items at another time slot, such as 1, 2 and so on. The number of disaster regions being served at time slots 1, 2 and others depends on the number of available vehicles at that particular time slot. **Best Plan Sequence (Time Slot 0): 27, 17, 11, 23, 9, 21, 16, 19, 12, 13, 18, 6, 4, 14, 26, 2, 1, 3, 25, 20, 7, 28, 10,**  
The planned distribution sequence at Time Slot 0 might not be the optimum distribution sequence at Time Slots 1, 2 and so on as the demand, priority, distance matrix and available vehicles change in each time slot. Thus, re-optimisation of the distribution schedule is applied based on the available updated resource information, the priorities of each disaster region, available vehicles and route condition. The distance matrix is updated with recent information on the road's conditions.