A consequence mitigation model for water networks subject to intentional physical attacks

This paper presents a mitigation model which can reduce or minimize adverse consequences when a water network suffers from significant water shortage resulting from physical destruction of critical facilities in water network. The model optimizes the water supply plan for water customers utilizing the information regarding the priority of water customers. Two different implementation engines based on Branch and Bound (BnB) and Genetic Algorithms (GA) are developed and evaluated using two representative water networks. BnB is a general search method for solving discrete and combinatorial optimization problem, which guarantees a global optimal solution while GAs are heuristic search algorithms in which the global optimality of the solution is not assured. A hydraulic network solver, EPANET2.0 is linked with these two optimization tools to test the hydraulic feasibility of candidate solutions. The performance of the two different engines is evaluated in terms of the quality of solutions and computational efficiency. The findings of this study indicate that the proposed optimization model can successfully reduce the consequences while meeting hydraulic constraints in water networks. In addition, as the size of the network increases, the use of the GA engine is preferred because it takes significantly less computation time than the BnB approach, while providing reliable levels of accuracy.