Optimization modeling for management of large combined sewer networks

This study develops a decision-making framework for large combined sewer management to assist asset managers in decision-making regarding sewer maintenance and rehabilitation plans under constraints of limited access to sewer condition data. Traditionally, municipalities have addressed this issue with a crisis-based approach. This study contributes to infrastructure management efforts in developing a management system based on life cycle cost analysis. The framework involves the application of probabilistic dynamic programming in conjunction with a Markov chains model to analyze the life cycle cost of sewer systems. The study attempts to predict future structural conditions using a Markov chains based model and integrates the prediction into the analysis. The output of the analysis provides the decision maker with optimal maintenance/rehabilitation techniques and the optimal times of application. The role of simulation has also been explored to obtain the variabilities of total cost when uncertainties prevail. By knowing the expected costs as well as their variabilities, asset managers can obtain a deeper understanding of the life cycle costs of sewer infrastructure. Municipalities are often faced with budgetary restraints. This frequently forces asset managers to defer rehabilitation. Prioritization criteria and budget allocation have been discussed to assist decision-makers in developing prioritized lists of rehabilitation projects constrained by limited budgets. The long-term network conditions and the effects of delayed rehabilitation under different budget scenarios have been explored. While the actual network condition may not be recognized until sometime in the future, the ability to estimate future conditions is useful for asset managers in making better decisions based on present circumstances as well as future considerations. This research has focused on large combined sewers and the framework has been validated using a large combined sewer network in the City of Indianapolis. Similar decision-making processes can be applied to other types of sewers with some modification. This study is a step towards an integrated sewer management system which involves long-term considerations