A two-stage stochastic programming approach for enhancing seismic resilience of water pipe networks
•We propose a two-stage stochastic mixed integer nonlinear program (MINLP).•We propose piecewise linear functions to approximate the nonlinearity in MINLP.•We formulate a mixed integer linear program (MILP) to approximate the MINLP.•We introduce a sequential heuristic algorithm.•We show that the seq...
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| Vydáno v: | Computers & industrial engineering Ročník 207; s. 111266 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.09.2025
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| ISSN: | 0360-8352 |
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| Abstract | •We propose a two-stage stochastic mixed integer nonlinear program (MINLP).•We propose piecewise linear functions to approximate the nonlinearity in MINLP.•We formulate a mixed integer linear program (MILP) to approximate the MINLP.•We introduce a sequential heuristic algorithm.•We show that the sequential algorithm yields a solution within 2 % of optimality.
Earthquakes are sudden and inevitable disasters that can cause enormous losses and suffering, and having accessible water is critically important for earthquake victims. To address this challenge, utility managers do preventive procedures on water pipes periodically to withstand future earthquake damage. The existing seismic vulnerability models usually consider simple methods to find the pipes to rehabilitate with highest priority. In this research, we develop an optimization approach to determine which water pipes to rehabilitate subject to a limited budget to achieve a network with highest post-disaster serviceability. We propose a two-stage stochastic mixed integer nonlinear program (MINLP). The MINLP model cannot be solved by commercial optimization software, like BARON, even for problems with a very small number of scenarios. Consequently, we propose piecewise linear functions (PLF) to approximate the nonlinearity in the MINLP. Therefore, we formulate a mixed integer linear program (MILP) to approximate the MINLP. The optimization of the MILP is still challenging to solve, so we introduce a sequential heuristic algorithm to mitigate this computational challenge and find bounds for the approximated optimal solution. We tested this method on multiple water pipe networks based on a standard network from the literature, and we show that the sequential algorithm yields a solution within 2 % of optimality. |
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| AbstractList | •We propose a two-stage stochastic mixed integer nonlinear program (MINLP).•We propose piecewise linear functions to approximate the nonlinearity in MINLP.•We formulate a mixed integer linear program (MILP) to approximate the MINLP.•We introduce a sequential heuristic algorithm.•We show that the sequential algorithm yields a solution within 2 % of optimality.
Earthquakes are sudden and inevitable disasters that can cause enormous losses and suffering, and having accessible water is critically important for earthquake victims. To address this challenge, utility managers do preventive procedures on water pipes periodically to withstand future earthquake damage. The existing seismic vulnerability models usually consider simple methods to find the pipes to rehabilitate with highest priority. In this research, we develop an optimization approach to determine which water pipes to rehabilitate subject to a limited budget to achieve a network with highest post-disaster serviceability. We propose a two-stage stochastic mixed integer nonlinear program (MINLP). The MINLP model cannot be solved by commercial optimization software, like BARON, even for problems with a very small number of scenarios. Consequently, we propose piecewise linear functions (PLF) to approximate the nonlinearity in the MINLP. Therefore, we formulate a mixed integer linear program (MILP) to approximate the MINLP. The optimization of the MILP is still challenging to solve, so we introduce a sequential heuristic algorithm to mitigate this computational challenge and find bounds for the approximated optimal solution. We tested this method on multiple water pipe networks based on a standard network from the literature, and we show that the sequential algorithm yields a solution within 2 % of optimality. |
| ArticleNumber | 111266 |
| Author | Shahandashti, Mohsen Rosenberger, Jay Michael Kareem, Uthman Abiola Pudasaini, Binaya Boskabadi, Azam |
| Author_xml | – sequence: 1 givenname: Azam orcidid: 0000-0002-4856-0504 surname: Boskabadi fullname: Boskabadi, Azam email: azam.boskabadi@wsu.edu organization: Department of Finance and Management Sciences, Washington State University, Washington, USA – sequence: 2 givenname: Uthman Abiola surname: Kareem fullname: Kareem, Uthman Abiola email: uak8880@mavs.uta.edu organization: Industrial, Manufacturing, and Systems Engineering Department, University of Texas at Arlington, TX, USA – sequence: 3 givenname: Jay Michael orcidid: 0000-0003-4038-1402 surname: Rosenberger fullname: Rosenberger, Jay Michael email: jrosenbe@uta.edu organization: Industrial, Manufacturing, and Systems Engineering Department, University of Texas at Arlington, TX, USA – sequence: 4 givenname: Mohsen orcidid: 0000-0002-2373-7596 surname: Shahandashti fullname: Shahandashti, Mohsen email: mohsen@uta.edu organization: Civil Engineering Department, University of Texas at Arlington, TX, USA – sequence: 5 givenname: Binaya surname: Pudasaini fullname: Pudasaini, Binaya email: binaya.pudasaini@mavs.uta.edu organization: Bridge EIT and HDR, Oregon, USA |
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| Cites_doi | 10.1016/j.proeng.2018.01.058 10.1016/j.cie.2016.07.014 10.1016/j.ejor.2017.06.058 10.1193/030413EQS062M 10.1016/j.cor.2016.08.006 10.21236/ADA594171 10.1061/(ASCE)IR.1943-4774.0001429 10.1061/(ASCE)CO.1943-7862.0001856 10.1007/s11269-017-1694-6 10.1016/j.egypro.2017.03.1055 10.1016/j.cie.2016.07.030 10.1016/S0167-6377(98)00054-6 10.1016/j.knosys.2018.01.012 10.1016/j.resconrec.2016.03.015 10.1029/2006WR005316 10.1061/(ASCE)NH.1527-6996.0000328 10.1007/s11269-013-0488-8 10.1016/j.compchemeng.2019.05.017 10.1016/j.cor.2018.03.007 10.1016/j.cor.2017.09.019 10.1586/eri.11.155 10.1007/s11704-016-5195-1 10.1016/j.cie.2016.10.022 10.1016/j.ejor.2018.01.022 10.1061/9780784484302.010 10.1016/j.egypro.2017.03.1109 |
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| Keywords | Mixed integer nonlinear programming (MINLP) Network optimization Linear approximation Stochastic programming |
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| Title | A two-stage stochastic programming approach for enhancing seismic resilience of water pipe networks |
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