Multiobjective Optimal Design of Sewerage Rehabilitation by Using the Nondominated Sorting Genetic Algorithm-II


Application of multiobjective optimization in sewerage rehabilitation management is not widespread due to the limitation of data collection and complex optimization process. Thus, a few researches in literature focused on sewerage rehabilitation optimization, and only considered two-objective optimization usually between the service life and the direct cost instead of a social cost. A sewerage rehabilitation multiobjective optimization decision support system (SRMOS) was developed for sewerage rehabilitation management in this study. The nondominated sorting genetic algorithm-II was used to design a set of Pareto surfaces with desirable rehabilitation effectiveness at the lowest cost by providing optimal plans comprising a construction method and substitute material. The SRMOS was applied to a real sewerage system to provide tradeoff solutions for three conflicting objectives, which are minimizing rehabilitation cost, maximizing pipe service, and minimizing traffic disruption. Compared with the experts' manual estimation, the plan derived from the SRMOS enables saving nearly 20% of the rehabilitation cost. The contours of the rehabilitation cost show the equivalent relation between the traffic disruption and service life of pipes. The results indicate that increasing the number of objectives can make up the drawback of cost hard to be quantified and can also facilitate deriving practical plans for reference in decision-making.