scholarly journals Sewer Network Layout Selection and Hydraulic Design Using a Mathematical Optimization Framework

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3337
Author(s):  
Natalia Duque ◽  
Daniel Duque ◽  
Andrés Aguilar ◽  
Juan Saldarriaga
Keyword(s):  
Mathematical Optimization ◽  
Mixed Integer ◽  
Sewer System ◽  
Design Problems ◽  
Optimization Framework ◽  
Network Layout ◽  
Sewer Network ◽  
Hydraulic Design ◽  
Actual Design ◽  
Design Cost

This paper proposes an iterative mathematical optimization framework to solve the layout and hydraulic design problems of sewer networks. The layout selection model determines the flow rate and direction per pipe using mixed-integer programming, which results in a tree-like structured network. This network layout parametrizes a second model that determines hydraulic features including the diameter and the upstream and downstream invert elevations of pipes using a shortest path algorithm. These models are embedded in an iterative scheme that refines a cost function approximation for the first model upon learning the actual design cost from the second model. The framework was successfully tested on two sewer network benchmarks from the literature and a real sewer network located in Bogotá, Colombia, that is proposed as a new instance. For both benchmarks, the proposed methodology found a better solution with up to 42% cost reduction compared to the best methodologies reported in the literature. These are near-optimal solutions with respect to construction cost that satisfy all hydraulic and pipe connectivity constraints of a sewer system.

scholarly journals Layout Selection for an Optimal Sewer Network Design Based on Land Topography, Streets Network Topology, and Inflows

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2491
Author(s):  
Juan Saldarriaga ◽  
Jesús Zambrano ◽  
Juana Herrán ◽  
Pedro L. Iglesias-Rey
Keyword(s):  
Network Design ◽  
Objective Function ◽  
Network Topology ◽  
Drainage System ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Construction Costs ◽  
Sewer Network ◽  
Hydraulic Design ◽  
Design Cost

This paper proposes a methodology for the layout selection of an urban drainage system as an extension to the methodology for an optimal sewer network design proposed by Duque, Duque, Aguilar, & Saldarriaga. The layout selection approach proposed in this paper uses an objective function that takes into account all input data in the problem, such as: land topography, street network topology, and inflow to each manhole. Once the layout is selected, the network is optimally designed using dynamic programming. The problem of layout selection is solved as a mixed-integer programming problem and is divided into two steps. The first step tries to define an initial layout using the network topology and land topography as a criterion. This allows for an initial hydraulic design and an approximation of the sewer network’s construction costs. The second step uses the data obtained in the previous process to establish an approximation of the construction costs of each arc that can be part of the layout. This is in order to minimize the objective function of the layout selection problem so that the hydraulic design cost is also minimized. The methodology was successfully tested on three case studies: the Chicó sewer network proposed by Duque et al. and two sewer network benchmarks from the literature.

scholarly journals Feasible Sanitary Sewer Network Generation Using Graph Theory

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Mustafa Erkan Turan ◽  
Goksen Bacak-Turan ◽  
Tulin Cetin ◽  
Ersin Aslan
Keyword(s):  
Graph Theory ◽  
Design Optimization ◽  
Case Studies ◽  
Optimization Problem ◽  
System Optimization ◽  
Layout Optimization ◽  
Sewer System ◽  
Sanitary Sewer ◽  
Sewer Network ◽  
Hydraulic Design

A graph theory-based methodology is proposed for the sewer system optimization problem in this study. Sewer system optimization includes two subproblems: layout optimization and hydraulic design optimization, which can be solved independently or solved simultaneously. No matter which method is chosen for the solution of the optimization problem, a feasible layout that satisfies the restrictions of the sewer system must be obtained in any step of the solution. There are two different layout options encountered: the layouts containing all sewer links and the layouts not containing all sewer links. The method proposed in this study generates a feasible sewer layout that contains all sewer links and satisfies all restrictions of a sanitary sewer system by using graph theory without any additional strategies unlike other studies. The method is applied to two different case studies. The results of the case studies have shown that graph theory is well applicable to sewer system optimization and the methodology proposed based on it is capable of generating a feasible layout. This study is expected to stimulate the use of graph theory on similar studies.

A framework for optimal reliability-based storm sewer network design in flat areas

2017 ◽  
Vol 44 (3) ◽  
pp. 139-150 ◽  
Author(s):  
Ali Moussavi ◽  
Hossein Mohammad Vali Samani ◽  
Ali Haghighi
Keyword(s):  
Performance Criterion ◽  
Adaptive Genetic Algorithm ◽  
Sewer System ◽  
Hydraulic Simulation ◽  
Storm Water Management Model ◽  
Sewer Network ◽  
Design Alternatives ◽  
Proposed Model ◽  
Design Cost ◽  
Storm Sewer

This paper presents a framework for the optimal design of a storm sewer network in flat areas where there is insufficient energy from gravity for runoff drainage. A reliability index based on nodal partial flooding is introduced as a performance criterion and utilizes the intrinsic storage capacity of the sewer network. The Storm Water Management Model is used for hydraulic simulation of the sewer system. This model is coupled with an adaptive genetic algorithm to obtain the least-cost design of the network. The model offers numerous design alternatives with various levels of reliability. The model was successfully applied to the Kianpars storm sewer network, a flat district of the city of Ahvaz in Iran. The proposed model is useful for managing the budget and technical limitations of sewer system design in flat areas as well as efficiently deriving an optimum trade-off between design cost and reliability.

Hydrogen Integration in Refinery Using MINLP Method

2012 ◽  
Vol 622-623 ◽  
pp. 720-725
Author(s):  
Mir Esmaeil Masoumi ◽  
Zahra Firooz Jahantighy
Keyword(s):  
Mathematical Optimization ◽  
Critical Issue ◽  
Mixed Integer ◽  
Total Annual Cost ◽  
Optimum Placement ◽  
Production Of Hydrogen ◽  
Hydrogen Network ◽  
Tehran Refinery ◽  
A Site ◽  
Low Sulfur

Hydrogen is an important utility in the production of clean fuels as low-sulfur gasoline and diesel. The combination of low-sulfur fuel specifications and reduced production of hydrogen in catalytic reformers make hydrogen management a critical issue. In this paper a systematic approach for the retrofit design of hydrogen networks in refineries was proposed. The methodology is based upon mathematical optimization of a superstructure and maximizing the amount of hydrogen recovered across a site. The techniques account fully for pressure constraints as well as the existing equipment. The optimum placement of new equipment such as compressors and purification units is also considered. Total annual cost and fresh hydrogen required by the refinery are employed as the optimizing objects. Equations obtained from superstructure method are solved with mixed-integer nonlinear programming of the general algebraic modeling system. In this work the Tehran refinery was considered as a case study. The results of optimization show that the 28% reduction was achieved in hydrogen production of north section and this is 35.7% for south section of refinery. Also adding the new hydrogen recovery unit in hydrogen network will cause 20% reduction in total costs of north and 31.2% in south sections.

A novel approach to discrete truss design problems using mixed integer neighborhood search

2018 ◽  
Vol 58 (6) ◽  
pp. 2411-2429 ◽  
Author(s):  
Mohammad Shahabsafa ◽  
Ali Mohammad-Nezhad ◽  
Tamás Terlaky ◽  
Luis Zuluaga ◽  
Sicheng He ◽  
...  
Keyword(s):  
Mixed Integer ◽  
Neighborhood Search ◽  
Design Problems ◽  
Truss Design ◽  
Novel Approach

Optimal Design Methodology of Point-to-Point Servo Systems for Minimization of Energy Consumption

1992 ◽  
Vol 206 (1) ◽  
pp. 35-43
Author(s):  
S W Kim ◽  
J S Park
Keyword(s):  
Design Methodology ◽  
Design Problems ◽  
Servo Systems ◽  
Speed Reduction ◽  
Point Motion ◽  
Actual Design ◽  
Profile Optimization ◽  
Point To Point ◽  
Selection Of

An optimum design methodology is presented for point-to-point motion control servo systems in which d.c. permanent magnetic motors are used as the main actuators. Emphasis is focused on establishing a series of comprehensive decision-making practices in dealing with three major design subjects: determination of the velocity profile, optimization of the speed reduction ratio, and selection of the motor. Finally, a practical design example is discussed to illustrate how the suggested design methodology may be applied to actual design problems.

Nonlinear Mixed Integer-Discrete-Continuous Programming and its Application to Engineering Design Problems

1989 ◽  
Author(s):  
J.-F. Fu ◽  
R. G. Fenton ◽  
W. L. Cleghorn
Keyword(s):  
Nonlinear Programming ◽  
Objective Function ◽  
Engineering Design ◽  
Optimization Algorithm ◽  
Mixed Integer ◽  
Continuous Variables ◽  
Design Problems ◽  
Continuous Programming ◽  
Engineering Design Problems ◽  
Standard Values

Abstract An algorithm for solving nonlinear programming problems containing integer, discrete and continuous variables is presented. Based on a commonly employed optimization algorithm, penalties on integer and/or discrete violations are imposed on the objective function to force the search to converge onto standard values. Examples are included to illustrate the practical use of this algorithm.

scholarly journals Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making

2019 ◽  
Vol 33 ◽  
pp. 1418-1426 ◽  
Author(s):  
Sina Aghaei ◽  
Mohammad Javad Azizi ◽  
Phebe Vayanos
Keyword(s):  
Decision Making ◽  
Decision Trees ◽  
Mixed Integer ◽  
Training Dataset ◽  
Integer Optimization ◽  
Disparate Impact ◽  
Optimization Framework ◽  
Disparate Treatment ◽  
Misclassification Rates ◽  
Automated Tools

In recent years, automated data-driven decision-making systems have enjoyed a tremendous success in a variety of fields (e.g., to make product recommendations, or to guide the production of entertainment). More recently, these algorithms are increasingly being used to assist socially sensitive decisionmaking (e.g., to decide who to admit into a degree program or to prioritize individuals for public housing). Yet, these automated tools may result in discriminative decision-making in the sense that they may treat individuals unfairly or unequally based on membership to a category or a minority, resulting in disparate treatment or disparate impact and violating both moral and ethical standards. This may happen when the training dataset is itself biased (e.g., if individuals belonging to a particular group have historically been discriminated upon). However, it may also happen when the training dataset is unbiased, if the errors made by the system affect individuals belonging to a category or minority differently (e.g., if misclassification rates for Blacks are higher than for Whites). In this paper, we unify the definitions of unfairness across classification and regression. We propose a versatile mixed-integer optimization framework for learning optimal and fair decision trees and variants thereof to prevent disparate treatment and/or disparate impact as appropriate. This translates to a flexible schema for designing fair and interpretable policies suitable for socially sensitive decision-making. We conduct extensive computational studies that show that our framework improves the state-of-the-art in the field (which typically relies on heuristics) to yield non-discriminative decisions at lower cost to overall accuracy.

scholarly journals Centralized Ambulance Diversion Policy Using Rolling-Horizon Optimization Framework to Minimize Patient Tardiness

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 266
Author(s):  
Sohye Baek ◽  
Young Hoon Lee ◽  
Seong Hyeon Park
Keyword(s):  
Programming Model ◽  
Mixed Integer ◽  
Time Interval ◽  
Rolling Horizon ◽  
Decentralized Optimization ◽  
Hospital System ◽  
Ambulance Diversion ◽  
Optimization Framework ◽  
The Impact ◽  
Rolling Horizon Optimization

Ambulance diversion (AD) is a common method for reducing crowdedness of emergency departments by diverting ambulance-transported patients to a neighboring hospital. In a multi-hospital system, the AD of one hospital increases the neighboring hospital’s congestion. This should be carefully considered for minimizing patients’ tardiness in the entire multi-hospital system. Therefore, this paper proposes a centralized AD policy based on a rolling-horizon optimization framework. It is an iterative methodology for coping with uncertainty, which first solves the centralized optimization model formulated as a mixed-integer linear programming model at each discretized time, and then moves forward for the time interval reflecting the realized uncertainty. Furthermore, the decentralized optimization, decentralized priority, and No-AD models are presented for practical application, which can also show the impact of using the following three factors: centralization, mathematical model, and AD strategy. The numerical experiments conducted based on the historical data of Seoul, South Korea, for 2017, show that the centralized AD policy outperforms the other three policies by 30%, 37%, and 44%, respectively, and that all three factors contribute to reducing patients’ tardiness. The proposed policy yields an efficient centralized AD management strategy, which can improve the local healthcare system with active coordination between hospitals.

scholarly journals Mathematical Optimization Model for Truck Scheduling in a Distribution Center with a Mixed Service-Mode Dock Area

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Gustavo Correa Issi ◽  
Rodrigo Linfati ◽  
John Willmer Escobar
Keyword(s):  
Mathematical Optimization ◽  
Distribution Center ◽  
Mixed Integer ◽  
Cross Docking ◽  
Truck Scheduling ◽  
Logistics Strategy ◽  
Service Mode ◽  
Reducing Costs ◽  
And Storage

Cross-docking is a logistics strategy in which products arriving at a distribution center are unloaded from inbound trucks and sorted for transfer directly to outbound trucks, reducing costs and storage and product handling times. This paper addresses a cross-docking problem by designing a mixed-integer linear programming (MILP) model to determine a schedule for inbound and outbound trucks in a mixed service-mode dock area that minimizes the time from when the first inbound truck arrives until the last outbound truck departs (makespan). The model is developed using AMPL software with the CPLEX and Gurobi solvers, which provide results for different instances, most of these with actual shift data from an integrated distribution center of a multinational food company located in Concepción, Chile. The results obtained from the case study are notable and show the effectiveness of the proposed mathematical model.

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