# A Cloud Based Disaster Management System
## 6 Optimized Rescue Plan Development  
Based on the wireless sensor network, our proposed system determines the locations of the targets that need to be visited by the robots, rescue team, and fighters. These targets can be victims' locations, fight locations or any zone that need urgent intervention. Moreover, an optimal intervention needs to be planned and executed in order to save victims and minimize loss. Therefore, in this section, we propose AHP-MTSP, a multi-objective optimization approach that helps to develop a rescue plan while minimizing several metrics of interest. Once the data was received from different sensors deployed in the environment. The system should prepare a training rescue plan to deal with the fire situation. The aim is to find an effective target locations assignment for the set of robots, such that each target is visited by only one robot at an optimal cost. Optimal cost includes the minimization of the total travelled distance that all the robots cover, minimization of the maximum tour length for all the robots, minimization of the overall mission time, minimization of the energy consumption, and balancing in the targets allotment. As mentioned in the architectures of Figures 1 and 2, the rescue team of robots and drones are connected to the cloud server through the Internet using cloud-based management system for controlling and monitoring robots, such as the Dronemap Planner system [61; 62; 63]. This enables the robots and drones to feed the cloud server with up to date information about the drones, including their locations, their internal status (battery level, altitudes, speed, accelerations,) which are used by the centralized agent server to dynamically develop the rescue plan using our proposed AHP-MTSP. The states of the robots and drones are exchanged using the MAVLink protocol [64] or the ROSLink protocol [65], which are messaging protocols that were designed to exchange messages between robots/drones and ground stations. It is imperative to note that multi-objective optimization problems take into account various conflicting objectives. This connotes that an effective resolution for one objective could be an ineffective resolution for another objective. Specifically, traditional optimization techniques do not offer answers that are effective for all objectives within a problem in focus. A multi-objective optimization problem can come about via a mathematical model characterized by a collection of p objective functions that should undergo simultaneous minimization or maximization. Officially, a multi-objective problem is definable as:  
\[ \begin{array}{l}\min/\max f_{1}(\text{X})\\ \min/\max f_{2}(\text{X})\\ \min/\max f_{3}(\text{X})\\ \ldots\\ \min/\max f_{p}(\text{X})\end{array}  \tag{1}\]  
where X represents the decision space. For the multi-objective MTSP, we considered a set of m robots \(\{\text{R}_{1},\ldots,\text{R}_{i}\}\) which are initially located at depots \(\{T_{1},\ldots,T_{i}\}\) The m robots should visit n targets \(\{T_{m+1}\cdots T_{m+n}\}\), where each robot should visit each target only once. Each robot Ri begins at its depot \(T_{i}\), then visits each of the \(ni\) designated targets \(\{T_{i1}\cdots T_{ini}\}\) sequentially then returns to its depot. The cost of travelling from target \(T_{i}\) to \(T_{j}\) is represented as C(\(T_{i}T_{j}\)), where cost may be Euclidean space, energy consumption, time spent, or anything else. Furthermore, it is possible to classify the objective functions into three groups. The first group focus on reducing the total costs for all the robots, such as reducing the total traveled distance or reducing the total energy consumption.