The design of a system is governed by multiple criteria, one being cost. If the benefit is fixed, the least cost design results in maximum benefit. However, the least cost approach normally results in a minimum capacity for a water supply network. A minimum cost model usually searches for the least cost solution (in pipe sizes), while satisfying the hydraulic constraints such as: required output pressures, maximum pipe flow rate and pipe flow velocities. The cost is a function of pipe diameters; therefore the optimization problem consists of finding a minimum cost solution by optimising pipe sizes to provide the minimum acceptable capacity. Multi-objective optimization However, according to the authors of the paper entitled, “Method for optimizing design and rehabilitation of water distribution systems”, “the least capacity is not a desirable solution to a sustainable water supply network in a long term, due to the uncertainty of the future demand”. It is preferable to provide extra pipe capacity to cope with unexpected demand growth and with water outages. The problem changes from a single objective optimization problem (minimizing cost), to a multi-objective optimization problem (minimizing cost and maximizing flow capacity). Weighted sum method To solve a multi-objective optimization problem, it is necessary to convert the problem into a single objective optimization problem, by using adjustments, such as a weighted sum of objectives, or an ε-constraint method. The weighted sum approach gives a certain weight to the different objectives, and then factors in all these weights to form a single objective function that can be solved by single factor optimization. This method is not entirely satisfactory, because the weights cannot be correctly chosen, so this approach cannot find the optimal solution for all the original objectives. The constraint method The second approach (the constraint method), chooses one of the objective functions as the single objective, and the other objective functions are treated as constraints with a limited value. However, the optimal solution depends on the pre-defined constraint limits. Sensitivity analysis The multiple objective optimization problems involve computing the tradeoff between the costs and benefits resulting in a set of solutions that can be used for sensitivity analysis and tested in different scenarios. But there is no single optimal solution that will satisfy the global optimality of both objectives. As both objectives are to some extent contradictory, it is not possible to improve one objective without sacrificing the other. It is necessary