Design of Wastewater Collection Networks Using Dynamic Programming Optimization Technique

Botrous, Akram; El-Hattab, I.; Dahab, Momen

doi:10.1061/40507(282)55

Cited by 9 publications

(3 citation statements)

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“…Haestad (2004) and Guo, Walters, and Savic (2008) reviewed a significant amount of research works in the field of sewer network design developed in the last 40 years. The methods such as enumeration approaches (Charalambous & Elimam, 1990;Desher & Davis, 1986;Miles & Heaney, 1988), linear programming (Dajani & Hasit, 1974;Elimam, Charalambous, & Ghobrial, 1989), nonlinear programming (Price, 1978;Swamee, 2001), dynamic programming (Botrous, El-Hattab, & Dahab, 2000;Diogo, Walters, Sousa, & Graveto, 2000;Gupta, Mehndiratta, & Khanna, 1983;Kulkarni & Khanna, 1985;Merrit & Bogan, 1973;Templeman & Walters, 1979;Walsh & Brown, 1973;Yen, Cheng, Jun, Voohees, & Wenzel, 1984) and evolutionary algorithms (EA) such as genetic algorithm (Afshar, Afshar, Marino, & Darbandi, 2006;Brand & Ostfeld, 2011;Haghighi & Bakhshipour, 2012;Heaney, Wright, Sample, Field, & Fan, 1999;Liang, Thompson, & Young, 2004), ant colony optimization algorithm (Afshar, 2007(Afshar, , 2010Moeini, 2017), particle swarm optimization (Izquierdo, Montalvo, Perez, & Fuertes, 2008;Navin & Mathur, 2016), simulated annealing (Karovic & Mays, 2014), and cellular automata (Afshar, Shahidi, Rohania, & Sargolzaei, 2011;Afshar, Zaheri, & Kim, 2016;Guo, 2005; have been proposed to solve sewer network design optimization problems. Mays and Tung (1992) pointed out some important limitations of the conventional optimization methods such as lin...…”

Section: Introductionmentioning

confidence: 99%

Hybridizing ant colony optimization algorithm with nonlinear programming method for effective optimal design of sewer networks

Moeini

Afshar

2019

Water Environment Research

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The ant colony optimization algorithm (ACOA) is hybridized with nonlinear programming (NLP) for the optimal design of sewer networks. The resulting problem is a highly constrained mixed integer nonlinear problem (MINLP) presenting a challenge even to the modern heuristic search methods. In the proposed hybrid method, The ACOA is used to determine pipe diameters while the NLP is used to determine the pipe slopes of the network by proposing two different formulations. In the first formulation, named ACOA‐NLP1, a penalty method is used to satisfy the problem constraints while in the second one, named ACOA‐NLP2, the velocity and flow depth constraints are expressed in terms of the slope constraints which are easily satisfied as box constraint of the NLP solver leading to a considerable reduction of the search space size. In addition, the assumption of minimum cover depth at the network inlets is used to calculate the nodal cover depths and the pump and drop heights at the network nodes, if required, leading to a complete solution. The total cost of the constructed solution is used as the objective function of the ACOA, guiding the ant toward minimum cost solutions. Proposed hybrid methods are used to solve three test examples, and the results are presented and compared with those produced by a conventional application of ACOA. The results indicate the effectiveness and efficiency of the proposed formulations and in particular the ACOA‐NLP2 to optimally solve the sewer network design optimization problems. Practitioner points ACOA is hybridized with NLP for the effective optimal design of sewer networks. Here, ACOA is used to determine pipe diameters and NLP is used to determine the network pipe slopes with predefined pipe diameters. In ACOA‐NLP1, a penalty method is used to enforce the problem constraints. In ACOA‐NLP2, velocity and flow depth constrains are expressed in terms of slope constraint.

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Section: Introductionmentioning

confidence: 99%

Hybridizing ant colony optimization algorithm with nonlinear programming method for effective optimal design of sewer networks

Moeini

Afshar

2019

Water Environment Research

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“…Most of the works on the optimal design of sewer network are, therefore, restricted to the optimal component sizing using different methods. For example, the methods such as enumeration and heuristic approaches (Desher and Davis, 1986;Miles and Heaney, 1988;Charalambous and Elimam, 1990), Linear Programming (Dajani and Hasit, 1974;Elimam et al, 1989;Swamee and Sharma, 2013), Nonlinear Programming (Price, 1978;Swamee, 2001), Dynamic Programming (Walsh andBrown, 1973, Templeman andWalters, 1979;Gupta et al, 1983;Yen et al, 1984;Kulkarni and Khanna, 1985;Botrous et al, 2000) and Evolutionary Algorithms (EAs) such as Genetic Algorithm (Heaney et al, 1999;Liang et al 2004, Afshar et al, 2006Haghighi and Bakhshipour, 2012), Ant Colony Optimization Algorithm (Afshar, 2007;Afshar, 2010), Particle swarm optimization (Izquierdo et al, 2008), Simulated Annealing (Karovic and Mays, 2014) and Cellular Automata (Guo, 2005;Guo et al, 2007b;Afshar et al, 2011) have been proposed for optimal sizing of sewer network.…”

Section: Introductionmentioning

confidence: 99%

Extension of the Hybrid Ant Colony Optimization Algorithm for Layout and Size Optimization of Sewer Networks

Moeini¹,

Afshar²

2018

J ENV INFORM

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In this paper, the incremental solution building capability of Ant Colony Optimization Algorithm (ACOA) is exploited using a Tree Growing Algorithm (TGA) augmented with the efficiency of Nonlinear Programming (NLP) methods leading to a hybrid ACOA-TGA-NLP algorithm for the effective layout and pipe size optimization of pumped/gravitational sewer networks. Solution of layout and pipe size optimization of sewer network requires the determination of pipe locations, pipe diameters, average pipe cover depths, drop and pump heights minimizing the total cost of the sewer network subject to operational constraints. The resulting problem is a highly constrained Mixed-Integer Nonlinear Programming (MINLP) problem presenting a challenge even to the modern heuristic search methods. In the proposed method, the TGA is used to construct feasible tree-like layouts out of the base layout defined for the sewer network, the ACOA is used to optimally determine the pipe diameters of the constructed layout, and finally NLP is used to determine the pipe slopes from which the remaining characteristics of the network such as pump/drop locations and heights are determined. In the NLP stage of the model, the velocity and flow depth constraints are expressed in terms of the slope constraints which are easily enforced as box constraint of the NLP solver leading to a considerable reduction of the search space size. The proposed hybrid ACOA-TGA-NLP has two significant advantages over other available methods. First, this method can be used for both pumped and gravitational sewer networks. Second, the computational effort is significantly reduced compared to alternative methods. Another method is also proposed here in which the layout of the network is determined by an ad-hoc method based on engineering judgment while the component design of the network is carried out by ACOA-NLP method as defined above. Proposed hybrid methods are used to solve a benchmark example from the literature and a hypothetical test example and the results are presented and compared with those produced by the existing methods such as SPST-DDDP, SDM-DDDP and GA-DDDP. The results indicate the efficiency and effectiveness of the proposed methods and in particular the ACOA-TGA-NLP method. In fact, the optimal solution of ACOA-TGA-NLP is 149, 64.1, 22.2 and 13.6% cheaper than those of ACOA-NLP, SPST-DDDP, SDM-DDDP and GA-DDDP methods, respectively, for the benchmark text example. Furthermore, ACOA-TGA-NLP yields a solution 80% cheaper than that of ACOA-NLP method for hypothetical test example.

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“…Haestad [2] and Guo et al [3] have reviewed the research studies of this eld over the last 40 years. In general, traditional methods [4], LP [5][6][7], NLP [8,9], DP [10][11][12][13][14][15][16], metaheuristic [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32], and hybrid methods [33][34][35][36][37][38] have been used in this eld.…”

Section: Introductionmentioning

confidence: 99%

Arc based ant colony optimization algorithm for solving sewer network design optimization problem

Moeini

2017

Scientia Iranica

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Abstract. In this paper, Arc Based Ant Colony Optimization Algorithm (ABACOA) is used to solve sewer network design optimization problem with proposing two di erent formulations. In both of the proposed formulations, i.e. UABAC and CABAC, the cover depths of sewer network nodes are taken as decision variables of the problem. The constrained version of ABACOA (CABAC) is also proposed in the second formulation to optimally determine the cover depths of the sewer network nodes. The constrained version of ABACOA is proposed here to satisfy slope constraint explicitly leading to reduction of search space of the problem, which is compared with that by the unconstrained arc based ACOA (UABAC). The ABACOA has two signi cant advantages of e cient implementation of the exploration and exploitation features along with an easy and straightforward de nition of the heuristic information for the ants over the alternative usual point based formulation. Two benchmark test examples are solved here using the proposed formulations, and the results are presented and compared with those obtained by alternative point-based formulation and other existing methods. The results show the superiority of the proposed ABACOA formulation, especially the constrained version of it, to optimally solve the sewer network design optimization.

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Design of Wastewater Collection Networks Using Dynamic Programming Optimization Technique

Cited by 9 publications

References 0 publications

Hybridizing ant colony optimization algorithm with nonlinear programming method for effective optimal design of sewer networks

Hybridizing ant colony optimization algorithm with nonlinear programming method for effective optimal design of sewer networks

Extension of the Hybrid Ant Colony Optimization Algorithm for Layout and Size Optimization of Sewer Networks

Arc based ant colony optimization algorithm for solving sewer network design optimization problem

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