Multi‐objective optimization models for the renewal planning of multiple asset classes. Issue 2 (25th April 2023)

• Record Type: Journal Article
• Authors: Chen, Thomas Ying‐Jeh; Wang, Eric; Pasch, Nicole; Ganjidoost, Amin
• Is Part Of: AWWA water science. Volume 5:Issue 2(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-04-25
• Subjects: asset management -- drinking water systems -- optimization -- risk analysis; Water -- Analysis -- Periodicals; Water-supply engineering -- Periodicals; Water -- Social aspects -- Periodicals; Water -- Purification -- Periodicals; Water-supply engineering; Water -- Social aspects; Water -- Analysis; Electronic journals; Periodicals; 628.162
• DOI: 10.1002/aws2.1336
• Languages: English
• ISSNs: 2577-8161

Abstract

Abstract: Managing the aging infrastructure of water distribution systems presents a challenge for many utilities. With various asset types competing for limited dollars, designing an effective asset management program is a resource allocation problem. Mobilization of equipment and crew is a significant cost (typically 2%–10%) within any capital improvement program. Therefore, selecting projects that target multiple asset classes together can reduce mobilization and help utilities stretch their budgets further. This research presents a process for modeling the joint renewal planning of multiple asset classes. The problem is framed as a dual‐objective optimization, where the selection of project areas aims to maximize lead service line removal and water meter changeout together. A case study from a Midwest utility is presented, and empirical data suggests the dual‐objective approach effectively reduces duplicate interventions in the same regions. Equity considerations are also examined, where constraints are added to enforce system‐wide project selection. Results show that the sensitivity of the objective toward equity is dependent on the underlying spatial distribution of the target asset itself, where uneven spread of the target asset leads to greater negative impact on model performance.