Optimal Scheduling of Water Network Repair Crews Considering Multiple Objectives

Abstract

Water main breaks disrupt water services and impact traffic flow along congested city roads. Dispatching water pipe repair crews needs to consider several factors that include: 1) the priority of repair site; 2) the suitability and efficiency of the construction crew in repairing a particular break type; and 3) the time required for crews to travel be­tween break sites. This paper presents a simulation-based multi-objective optimization model to schedule repair crews across water network break sites in an urban setting. Discrete-event simulation models for the water pipe repair process are developed to account for various repair methods. These models are subsequently integrated within a GA-based multi-objective optimization model that considers the following objectives: 1) minimizing the total repair time required to complete all breaks; 2) minimizing the total cost to complete the breaks; and 3) minimizing the cumulative impact of all breaks incident on road users and water customers. A case study for the water network on the City of Damietta, Egypt is used to demonstrate the capabilities of the model. Results show a 21% reduction in repair time and 50% reduction in user impact compared to heuristic crew allocation methods used by the water utility.

Multi‐objective optimization models for the renewal planning of multiple asset classes

“…Here non‐linear relaxation techniques are demonstrated to converge on local optima. Multi‐objective formulations for water main replacement planning are presented in Dandy and Engelhardt (2006), Kim et al (2004), and Osman et al (2017), the model formulations in these works aim to maximize risk reduction along with other considerations such as cost, hydraulic reliability, and traffic impacts.…”

Integration of discrete event simulation with other modeling techniques to simulate construction engineering and management: an overview

“…Applying discrete event simulation only Fabrication of products (Bohács et al, 2016;Brodetskaia et al, 2013;Cheng and Tran, 2016;Golzarpoor et al, 2017;Heravi and Firoozi, 2017;Hu et al, 2018;Hu and Mohamed, 2014;Khan et al, 2017;Lau et al, 2014;Sneha and Tezeswi, 2016; Planning and scheduling (Al-Emran et al, 2010;Alvanchi et al, 2012a;Alvanchi et al, 2012c;Beiert et al, 2010;Dallasega et al, 2019;Gonzalez and Echaveguren, 2012;Hassan et al, 2016;Jung et al, 2016;Lee et al, 2019;Lindhard et al, 2019;Osman et al, 2017;Sadeghi et al, 2015;Shin et al, 2011;Yuan et al, 2020;Zhang et al, 2012) Productivity (Afifi et al, 2020;Arashpour and Arashpour 2015;Arashpour et al, 2014;Aziz et al, 2017;Baniassad et al, 2018;Khanh and Kim, 2020;Kisi et al, 2017;Montaser and Moselhi, 2014) Construction processes (Akhavian and Behzadan, 2018;Akhavian and Behzadan, 2014;Athigakunagorn and Limsawasd 2020;Chen et al, 2020a;Corona-Suarez et al, 2014;Fayed and Azeldin, 2018;Feng et al, 2018a;Feng e...…”

Optimizing cluster selections for the replacement planning of water distribution systems

“…The methods presented by these authors allow for a systemwide analysis and prioritization of assets. Finally, examples of multi-objective formulations of the replacement planning problem are presented (Dandy & Engelhardt, 2006;Kim et al, 2004;Osman et al, 2017), which involve considerations such as cost minimization, hydraulic reliability, and impacts to road users and customers.…”