Sensor Placement in Municipal Water Networks with Temporal Integer Programming Models

Berry, Jonathan W.; Hart, William E.; Phillips, Cynthia A.; Uber, James G.; Watson, Jean‐Paul

doi:10.1061/(asce)0733-9496(2006)132:4(218)

Cited by 243 publications

(145 citation statements)

References 18 publications

(21 reference statements)

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“…In their study [22], authors utilize p-MP Mixed Integer Program (MIP) model to determine sensor locations in municipal water networks with the aim of minimizing impact of contamination in municipal water. They use the analytic model to react rapidly in case of emergencies such as accidental contamination or chemical terrorist attacks on municipal water networks, which are threatening cases on the public health.…”

Section: Literature Reviewmentioning

confidence: 99%

A multi-criteria assessment of the p-median, maximal coverage and p-center location models

2017

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Original scientific paper Location problems consider locating facilities with the objective of finding their best locations. In most real world problems, it is common that a demand node is required to be covered with multiple facilities in order to ensure a backup supply. The backup supply is necessary especially for public or emergency service location problems where a covered demand may not be serviced if it's designated facility is engaged serving other demands. In this study we consider three classic location models, i.e. p-median, maximal coverage and p-center, and compare their performances with respect to seven decision criteria under Q-coverage requirement. For this purpose, we generate multiple problem instances and solve each instance with the three models for different Q-values. Our numerical results reveal the pros and cons of each model to assist decision makers in determining the most promising model with respect to each assessment criteria. Keywords: backup coverage; facility location; maximal coverage; p-center; p-median Višekriterijska procjena modela lokacije p-srednje, maksimalne pokrivenosti i p-centralnoIzvorni znanstveni članak Problemi lokacije znače lociranje objekata s ciljem pronalaženja najbolje lokacije. Kod većine stvarnih problema uobičajeno je da zahtijevani čvor zadovoljava višestruke uvjete kako bi osigurao rezervnu opskrbu. Ta je rezervna opskrba posebno potrebna kod javnih ili problema lokacije hitne službe kada se pokrivena potražnja ne može osigurati ako je njezin za to određeni objekt zauzet pružanjem usluge drugim zahtijevima. U ovom radu razmatramo tri klasična modela lokacije, t.j. p-srednje, maksimalne pokrivenosti i p-centralno, te uspoređujemo njihovo funkcioniranje u odnosu na sedam kriterija za donošenje odluke u okviru zahtjeva Q-pokrivenosti (Q-coverage). U tu svrhu generiramo slučajeve višestrukih problema i svaki slučaj rješavamo s tri modela za različite Q vrijednosti. Naši numerički rezultati otkrivaju argumente za i protiv svakog modela koji pomažu u donošenju odluke o određivanju modela koji bi najviše odgovarao u odnosu na svaki od kriterija koji se uzimaju u obzir pri donošenju procjene.

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Section: Literature Reviewmentioning

confidence: 99%

A multi-criteria assessment of the p-median, maximal coverage and p-center location models

2017

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“…[6,7] introduced an MIP(mixed integer programming) solution for the objective to minimize the expected fraction of population exposed to a contamination. The objective of [8,9] is to ensure that the expected impact of a contamination event is within a pre-specified level, and [8] introduced a formulation based on set cover and solved the problem using genetic algorithm while [9] use a MIP based solution. In order to achieve the objective defined in [10], [11][12][13][14][15][16][17][18][19][20][21] adopted multi-objective optimization by different methods such as heuristic, predator-prey model or local search method and [22] used the submodular property to achieve an approximation guarantee.…”

Section: Related Workmentioning

confidence: 99%

“…Note that sensor placement optimization can be also reduced to integer linear programming (ILP), which could be solved by the state-of-the-art mixed integer programming (MIP) solver such as ILOG's AMPL/CPLEX9.1 MIP solver that was widely used in the previous works [9]. However, in general, these solvers are less efficient and can not scale up to large water distribution networks.…”

Section: Definition 2 (Evaluation Function)mentioning

confidence: 99%

Incremental Sensor Placement Optimization on Water Network

Huang

et al. 2013

Advanced Information Systems Engineering

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Abstract. Sensor placement on water networks is critical for the detection of accidental or intentional contamination event. With the development and expansion of cities, the public water distribution systems in cities are continuously growing. As a result, the current sensor placement will lose its effectiveness in detecting contamination event. Hence, in many real applications, we need to solve the incremental sensor placement (ISP) problem. We expect to find a sensor placement solution that reuses existing sensor deployments as much as possible to reduce cost, while ensuring the effectiveness of contamination detection. In this paper, we propose scenario-cover model to formalize ISP and prove that ISP is NPhard and propose our greedy approaches with provable quality bound. Extensive experiments show the effectiveness, robustness and efficiency of the proposed solutions.

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“…Most studied has been the influence of uncertainty in the nature of potential contamination events; Davis et al (2013) provide a recent review of work in this area. Studies also have considered the influence of uncertainty in water demand (e.g., Berry et al, 2006;Comboul and Ghanem, 2012;Cozzolino et a., 2006Cozzolino et a., , 2011Mukherjee et al, 2017;Ostfeld and Salomons, 2005a, b;Shastri and Diwekar, 2006), and population density (Rico-Ramirez et al, 2007;Davis et al, 2013). Davis et al (2013) also considered the influence 15 of uncertainty in the rate of contaminant decay in a network following injection and uncertainty in the nature of the exposure model used to assess the consequences of a contamination event.…”

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confidence: 99%

“…Development of CWS designs is discussed in Davis et al (2013). TEVA-SPOT optimizes sensor placement using a heuristic approach (Berry et al, 2006). Designs were developed for the original and the three skeletonized network models for each WDS for three sensor set sizes (5, 10, and 25 sensors) and for five different dose levels ranging from 10 -4 to 1 mg. A total of 120 designs were developed for each network (two objectives, four network models, three sensor set sizes, and five dose levels).…”

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confidence: 99%

The effect of a loss of model structural detail due to network skeletonization on contamination warning system design: case studies

Davis¹,

Janke²

2018

Preprint

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Abstract.The effect of limitations in the structural detail available in a network model on contamination warning system (CWS) design was examined in case studies using the original and skeletonized network models for two water distribution systems (WDSs).The skeletonized models were used as proxies for incomplete network models. CWS designs were developed by optimizing sensor placements for worst-case and mean-case contamination events. Designs developed using the skeletonized network 5 models were transplanted into the original network model for evaluation. CWS performance was defined as the number of people who ingest more than some quantity of a contaminant in tap water before the CWS detects the presence of contamination. Lack of structural detail in a network model can result in CWS designs that (1) provide considerably less protection against worst-case contamination events than that obtained when a more complete network model is available and (2) yield substantial underestimates of the consequences associated with a contamination event. Nevertheless, CWSs developed using 10 skeletonized network models can provide useful reductions in consequences for contaminants whose effects are not localized near the injection location. Mean-case designs can yield worst-case performances similar to those for worst-case designs when there is uncertainty in the network model. Improvements in network models for WDSs have the potential to yield significant improvements in CWS designs as well as more realistic evaluations of those designs. Although such improvements would be expected to yield improved CWS performance, the expected improvements in CWS performance have not been quanti-15 fied previously. The results presented here should be useful to those responsible for the design or implementation of CWSs, particularly managers and engineers in water utilities, and encourage the development of improved network models.

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Sensor Placement in Municipal Water Networks with Temporal Integer Programming Models

Cited by 243 publications

References 18 publications

A multi-criteria assessment of the p-median, maximal coverage and p-center location models

A multi-criteria assessment of the p-median, maximal coverage and p-center location models

Incremental Sensor Placement Optimization on Water Network

The effect of a loss of model structural detail due to network skeletonization on contamination warning system design: case studies

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