Optimal Design of Regional Wastewater Systems: A Fixed-Charge Network Flow Model

Jarvis, John J.; Rardin, Ronald L.; Unger, Volker; Moore, Richard W.; Schimpeler, Charles C

doi:10.1287/opre.26.4.538

Cited by 41 publications

(4 citation statements)

References 3 publications

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“…Daskin [11] provides in‐depth coverage of the area and an extensive list of application papers is available in Eiselt et al [12]. Network design applications arise in telecommunications [14, 27], including related location problems [15], regional wastewater system design [21], and electrical smartgrid data network design, including equipment placement, described in Barr et al [9].…”

Section: Problem Definition and Backgroundmentioning

confidence: 99%

An extreme‐point tabu‐search algorithm for fixed‐charge network problems

et al. 2021

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We propose a new algorithm for fixed‐charge network flow problems based on ghost image (GI) processes as proposed in Glover (1994) and adapted to fixed‐charge transportation problems in Glover et al. (2005). Our GI algorithm iteratively modifies an idealized representation of the problem embodied in a parametric GI, enabling all steps to be performed with a primal network flow algorithm operating on the parametric GI. Computational testing is carried out on well‐known problems from the literature plus a new set of large‐scale fixed‐charge transportation and transshipment network instances. We also provide comparisons against CPLEX 12.8 and demonstrate that the new GI algorithm with tabu search (TS) is effective on large problem instances, finding solutions with statistically equivalent objective values at least 700 times faster. The attractive outcomes produced by the current GI/TS implementation provide a significant advance in our ability to solve fixed‐cost network problems efficiently and invites its use for larger instances from a variety of application domains.

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Section: Problem Definition and Backgroundmentioning

confidence: 99%

An extreme‐point tabu‐search algorithm for fixed‐charge network problems

et al. 2021

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“…Especially, in [2], a siting model is introduced in order to locate waste water treatment facilities, and the concave cost of a treatment plant is approximated by a fixed-charge cost and one straight-line segment. Similarly, in [3], the regional waste water system is modeled as a fixed-charge network flow problem where the concave cost functions are practically linearized by using piecewise linearization. Estimating the system load in terms of population units at some target year in the future, the authors solve the linearized model for a specific region.…”

Section: Introductionmentioning

confidence: 99%

A Linearized Mathematical Formulation for Combined Centralized and Distributed Waste Water Treatment Network Design

2022

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Waste water treatment (WWT) is a very important issue affecting both the environment and public health in the twenty-first century. The increasing earth’s population together with the growing urbanism leads to the need of redesigning effective WWT. In this paper, the problem of optimal Waste Water Treatment Network Design (WWTND) is addressed. To this end, various parameters affecting the problem have been taken into consideration, such as the distance between the residential areas and the treatment plants, estimations for future population of towns and costs of expanding existing network or building a new one. The last parameter of cost has a lot of components (pipeline cost, treatment plant cost, etc.), all of which are non-linear functions depending on the amount of waste water produced and treated within the network. The authors have developed a mathematical model for the solution of WWTND problem and have applied piecewise linearization in order to deal with the non-linear terms. The developed model has been implemented on an area in Luxemburg, for which data were collected. The results prove the model’s validity and usefulness, while its solution is computationally affordable.

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“…Many mathematical models have been developed to obtain least-costly designs for regional wastewater treatment systems [e.g., Converse, 1972;Joeres et al, 1974;Rossman and Liebman, 1974;Whitlatch and ReVelle, 1976;Chiang and Lauria, 1977;Rossman, 1978;Jarvis et al, 1978]. A method which places more emphasis on generating alternative plans, rather than obtaining an 'optimal' plan, has also been presented [Brill and Nakamura, 1978].…”

Section: Introductionmentioning

confidence: 99%

Generation and evaluation of alternative plans for regional wastewater systems: An imputed value method

Nakamura¹,

Brill²

1979

Water Resources Research

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A branch‐and‐bound method for use in planning regional wastewater treatment systems is extended to include a methodology for generating and evaluating alternative plans. Since many planning issues are related to a specific configuration of regional facilities, economic trade‐offs among alternative plans are evaluated using imputed values of individual facilities or groups of them. The imputed values, or bounds on them, are calculated from information contained in the branch‐and‐bound tree using an ‘imputed value incidence matrix.’ The general approach, which can be applied to other public facility location problems, is designed to aid an analyst in gaining more insights into the problem under study than obtainable from conventional optimization methods. The procedure is designed for use within a larger planning process where the steps are to generate, to evaluate, and to elaborate on alternative plans. The method is illustrated using the regional wastewater system for DuPage County, Illinois.

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Optimal Design of Regional Wastewater Systems: A Fixed-Charge Network Flow Model

Cited by 41 publications

References 3 publications

An extreme‐point tabu‐search algorithm for fixed‐charge network problems

An extreme‐point tabu‐search algorithm for fixed‐charge network problems

A Linearized Mathematical Formulation for Combined Centralized and Distributed Waste Water Treatment Network Design

Generation and evaluation of alternative plans for regional wastewater systems: An imputed value method

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