## 7 Evaluation
### Example Illustration  
An illustrated example is presented to show how Input data sets obtained from the DOE sensors and expert preferences are used in the matching of emergency strategies with located emergency locations on a map. Let decision maker belonging to \(E^{S}=\{e_{1}^{S},e_{2}^{S},\dots,e_{5}^{S}\}\) be a set of experts for a selection of emergency strategies where \(q=\)5 is the number of experts that select one or several emergency strategies \(Q_{i}^{S}|(t=1,\dots,5)\) with data normalization in fuzzy weights \([0,1]\) and his/her preference \(\beta_{j}^{S}\) defined in a five-point scale (0: _oppose_, 0.25: _almost oppose_, 0.5: _have no preference_, 0.75: _almost agree_, 1: _agree_). In many decision problems related to the prioritisation of emergency service vehicles in emergency response scenarios an emergency service resource (i.e., a robot or vehicle) will identify potential targets, locations, and tasks; this requires multiple decision-making objectives. \(\left(Q_{j}\left(X\right)\right)\) is an objective function for five disaster strategies indicated for multiple objectives updated in the decision maker's knowledge-base. In steps 1 to 4 [of the proposed system--see Section 6] a decision maker can instruct an on-line emergency DSS on the automatic selection of the emergency management strategies in multiple decision-making objectives as follows: \(V_{1}\) (Ambulance), \(V_{2}\) (helicopter), \(V_{3}\) (fire engine), \(V_{4}\) (police car), and \(V_{5}\) (rapid fire truck). Assume that the objective function aims to minimise the impact of a fire in'real-time' emergency response to meet the five disaster strategies following objectives:  
1. \(\left(Q\left(1\right)\right)\): To rescue people in advance of a fire;
2. \(\left(Q\left(2\right)\right)\): To rescue people and move items to a place of safety;
3. \(\left(Q\left(3\right)\right)\): To protect items and infrastructure and maintain safety;
4. \(\left(Q\left(4\right)\right)\): To move items into safe storage;
5. \(\left(Q\left(5\right)\right)\): To rescue people in medical centres and hospitals together with emergency support. For instance, the value \(\left(Q_{j}\left(X\right)\right)\) is considered by using the linguistic variables as follows: _very high_, _high_, _very very low_, _very little low_, _medium high_, _very low_, _roundly high_,... In the preliminary experiment as shown in Step 5 [steps 5.1-5.7 of the proposed system--see Section 6]_Kansei_ evaluation models human sensibilities in the decision-support function. We have collected 10 _Kansei_ words belonging to an objective function as shown in in Table 1.  
A _Kansei_ disaster matrix is constructed using _Kansei_ words as shown in in Table 2.  
In _Kansei_ evaluation we us a semantic differential (SD) method with a five-point scale definition: (0: _little low_, 0.25: _low_, 0.5: _little high_, 0.75: _high_, 1: _very high_). The values for the preferences of decision makers to evaluate the objective function is depicted in Table 3. From steps in the calculations as discussed above, the degree of the objective function corresponding to the emergency rescue can be shown in Table 4.  
Apply the STC algorithm as shown in Equation (6):  
\[\frac{Min}{x}\sum_{j}w_{j}.Q_{j}\left(X\right)\ =\ Min\left[0.86756,\ 0.6525,\ 0.3087,\ 0.9687,\ 0.6876\right]=0.3087 \tag{6}\]  
In the proposed Hedge-Emergency-DSS model, multiple decision-making processes [for service resources (vehicles)] will find the optimal path (route) in the emergency zone (the DOE) based on vehicle priorities. As calculated by the formulas above, the illustration example the order of vehicles is expressed by: \(\left(V_{4}\ >\ V_{1}\ >\ V_{5}\ >\ V_{2}\ >\ V_{3}\right)\). The relevant example demonstrates that vehicles can find the optimal path (route) while maximising the coverage (of the DOE) and monitoring the priority of the emergency service vehicles in emergency response.