Application of multiobjective optimization to scheduling capacity expansion of urban water resource systems

Abstract

Significant population increase in urban areas is likely to result in a deterioration of drought security and level of service provided by urban water resource systems. One way to cope with this is to optimally schedule the expansion of system resources. However, the high capital costs and environmental impacts associated with expanding or building major water infrastructure warrant the investigation of scheduling system operational options such as reservoir operating rules, demand reduction policies, and drought contingency plans, as a way of delaying or avoiding the expansion of water supply infrastructure. Traditionally, minimizing cost has been considered the primary objective in scheduling capacity expansion problems. In this paper, we consider some of the drawbacks of this approach. It is shown that there is no guarantee that the social burden of coping with drought emergencies is shared equitably across planning stages. In addition, it is shown that previous approaches do not adequately exploit the benefits of joint optimization of operational and infrastructure options and do not adequately address the need for the high level of drought security expected for urban systems. To address these shortcomings, a new multiobjective optimization approach to scheduling capacity expansion in an urban water resource system is presented and illustrated in a case study involving the bulk water supply system for Canberra. The results show that the multiobjective approach can address the temporal equity issue of sharing the burden of drought emergencies and that joint optimization of operational and infrastructure options can provide solutions superior to those just involving infrastructure options.

Stochastic capacity expansion models for airport facilities

“…In the context of expected long-term demand growth, the core of this strategic planning process is intended to determine the optimal timing and level of capacity acquisition or ''expansion''. This decision process is crucial in a wide array of practical applications, such as telecommunications (Gendreau et al, 2006), manufacturing (Ahmed et al, 2003), oil industry (MirHassani and Noori, 2011), network design (Pimentel et al, 2013), electricity generation (Parpas and Webster, 2014) and urban water resource systems (Mortazavi-Naeini et al, 2014).…”

“…For instance, the concept of delay seems absent in urban water supply systems; therefore, all costs are linear as in Mortazavi-Naeini et al (2014). More importantly, the modeling philosophy of ''do not introduce nonlinearities unless necessary'' leads to the omission or simplification of nonlinear costs.…”

“…As a key issue in many business and engineering fields, capacity expansion has been well studied and documented in the literature, such as in the oil industry (MirHassani and Noori, 2011), network design (Pimentel et al, 2013), electricity generation (Parpas and Webster, 2014) and urban water resource systems (Mortazavi-Naeini et al, 2014). While applications in other areas shed light on the airport capacity expansion problem, an essential factor should be addressed: airport facilities are subject to congestion, like other common transportation facilities, such as highway links and ports.…”

Scenario driven optimal sequencing under deep uncertainty

“…Examples include the sequencing of urban water supply augmentation sources and infrastructure (Beh et al, 2014;Kang and Lansey, 2014;Mortazavi-Naeini et al, 2014;Ray et al, 2013), the scheduling of pumps and rehabilitation activities in water distribution systems (Kleiner et al, 1998;Dandy and Engelhardt, 2001;Dandy and Engelhardt, 2006;Savić et al, 2011;Zheng and Zecchin, 2014), the scheduling of wastewater discharges (Murillo et al, 2011), the scheduling of mining production activities (Badiozamani and Askari-Nasab, 2014), the scheduling of forest management activities (Sharples et al, 2009;Simon and Etienne, 2010;Zhang and Barten, 2009), the scheduling of irrigation water (Ge et al, 2013;Merot and Bergez, 2010), the scheduling of crop management activities (Lautenbach et al, 2013;Ripoche et al, 2011), the scheduling of environmental flows in rivers (Szemis et al, 2013;Szemis et al, 2012) and determining the optimal schedule of investments of conservation funding (Bode et al, 2008;Wilson et al, 2006).…”

Solving Water Resource Scheduling Problem through an Improved Artificial Fish Swarm Algorithm

“…Comair et al proposed GIS-based system to guide water resources management [9]. Mortazavi et al established multi-objective optimization model to schedule urban water resource [10]. Nouiri optimized the water resource management by using genetic algorithm [11].…”