scholarly journals Overview of Multi-Objective Optimization Approaches in Construction Project Management

2020 ◽  
Author(s):  
Ibraheem Alothaimeen ◽  
David Arditi
Keyword(s):  
Construction Projects ◽  
Construction Cost ◽  
Time Constraints ◽  
Quality Level ◽  
Construction Project Management ◽  
Multi Objective Optimization ◽  
Rating Systems ◽  
Multi Objective ◽  
The Many ◽  
Single Objective

The difficulties that are met in construction projects include budget issues, contractual time constraints, complying with sustainability rating systems, meeting local building codes, and achieving the desired quality level, to name but a few. Construction researchers have proposed and construction practitioners have used optimization strategies to meet various objectives over the years. They started out by optimizing one objective at a time (e.g., minimizing construction cost) while disregarding others. Because the objectives of construction projects often conflict with each other, single-objective optimization does not offer practical solutions as optimizing one objective would often adversely affect the other objectives that are not being optimized. They then experimented with multi-objective optimization. The many multi-objective optimization approaches that they used have their own advantages and drawbacks when used in some scenarios with different sets of objectives. In this chapter, a review is presented of 16 multi-objective optimization approaches used in 55 research studies performed in the construction industry and that were published in the period 2012–2016. The discussion highlights the strengths and weaknesses of these approaches when used in different scenarios.

Enhancement of Labyrinth Seals Efficiency by Means of a Multi-Objective Optimization Technique

2018 ◽  
Author(s):  
Mikhail Gritckevich ◽  
Kunyuan Zhou ◽  
Vincent Peltier ◽  
Markus Raben ◽  
Olga Galchenko
Keyword(s):  
Optimization Technique ◽  
Leakage Flow ◽  
Multi Objective Optimization ◽  
Labyrinth Seals ◽  
Engine Operation ◽  
Turbine Engine ◽  
Multi Objective ◽  
Single Objective ◽  
Ansys Workbench ◽  
Comprehensive Study

A comprehensive study of several labyrinth seals has been performed in the framework of both single-objective and multi-objective optimizations with the main focus on the effect of stator grooves formed due to the rubbing during gas turbine engine operation. For that purpose, the developed optimization workflow based on the DLR-AutoOpti optimizer and ANSYS-Workbench CAE environment has been employed to reduce the leakage flow and windage heating for several seals. The obtained results indicate that the seal designs obtained from optimizations without stator grooves have worse performance during the lifecycle than those with the stator grooves, justifying the importance of considering this effect for real engineering applications.

scholarly journals A two-step surrogate modeling strategy for single-objective and multi-objective optimization of radiofrequency circuits

2018 ◽  
Vol 23 (13) ◽  
pp. 4911-4925 ◽  
Author(s):  
F. Passos ◽  
R. González-Echevarría ◽  
E. Roca ◽  
R. Castro-López ◽  
F. V. Fernández
Keyword(s):  
Surrogate Modeling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Modeling Strategy ◽  
Single Objective

Multi-Objective Pricing Optimization for a High-Speed Rail Network Under Competition

2019 ◽  
Vol 2673 (7) ◽  
pp. 215-226 ◽  
Author(s):  
Huizhuo Cao ◽  
Xuemei Li ◽  
Vikrant Vaze ◽  
Xueyan Li
Keyword(s):  
High Speed ◽  
Programming Model ◽  
Pareto Frontier ◽  
Multiple Objectives ◽  
Multi Objective Optimization ◽  
High Speed Rail ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Single Objective

Multi-objective pricing of high-speed rail (HSR) passenger fares becomes a challenge when the HSR operator needs to deal with multiple conflicting objectives. Although many studies have tackled the challenge of calculating the optimal fares over railway networks, none of them focused on characterizing the trade-offs between multiple objectives under multi-modal competition. We formulate the multi-objective HSR fare optimization problem over a linear network by introducing the epsilon-constraint method within a bi-level programming model and develop an iterative algorithm to solve this model. This is the first HSR pricing study to use an epsilon-constraint methodology. We obtain two single-objective solutions and four multi-objective solutions and compare them on a variety of metrics. We also derive the Pareto frontier between the objectives of profit and passenger welfare to enable the operator to choose the best trade-off. Our results based on computational experiments with Beijing–Shanghai regional network provide several new insights. First, we find that small changes in fares can lead to a significant improvement in passenger welfare with no reduction in profitability under multi-objective optimization. Second, multi-objective optimization solutions show considerable improvements over the single-objective optimization solutions. Third, Pareto frontier enables decision-makers to make more informed decisions about choosing the best trade-offs. Overall, the explicit modeling of multiple objectives leads to better pricing solutions, which have the potential to guide pricing decisions for the HSR operators.

An Interactive Neural Network for Constrained Multi-Objective Optimization with Application to the Design of Digital Filters

2012 ◽  
Vol 433-440 ◽  
pp. 2808-2816
Author(s):  
Jian Jin Zheng ◽  
You Shen Xia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computational Complexity ◽  
Digital Filters ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Single Objective

This paper presents a new interactive neural network for solving constrained multi-objective optimization problems. The constrained multi-objective optimization problem is reformulated into two constrained single objective optimization problems and two neural networks are designed to obtain the optimal weight and the optimal solution of the two optimization problems respectively. The proposed algorithm has a low computational complexity and is easy to be implemented. Moreover, the proposed algorithm is well applied to the design of digital filters. Computed results illustrate the good performance of the proposed algorithm.

Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization

2010 ◽  
Vol 15 (9) ◽  
pp. 1749-1767 ◽  
Author(s):  
Hisao Ishibuchi ◽  
Yuji Sakane ◽  
Noritaka Tsukamoto ◽  
Yusuke Nojima
Keyword(s):  
Genetic Algorithms ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Neighborhood Structures ◽  
Single Objective

The Application of Entransy Dissipation Theory in Optimization Design of Heat Exchanger

2010 ◽  
Author(s):  
Jiangfeng Guo ◽  
Mengxun Li ◽  
Mingtian Xu ◽  
Lin Cheng
Keyword(s):  
Heat Conduction ◽  
Heat Exchanger ◽  
Working Fluid ◽  
Optimization Approach ◽  
Entransy Dissipation ◽  
Multi Objective Optimization ◽  
Fluid Friction ◽  
Multi Objective ◽  
Heat Exchanger Design ◽  
Single Objective

The heat conduction and fluid friction are two main detrimental irreversibilities in heat exchanger. According to the entransy dissipation theory, the entransy dissipation can be employed to quantify these two irreversibilities. In the present work, the optimization of heat exchanger design is investigated by applying the entransy dissipation theory and genetic algorithm. Firstly, by taking the total dimensionless entransy dissipation caused by heat conduction and fluid friction as objective function, a single-objective optimization approach to heat exchanger design is developed. However, it is found that the role played by the fluid friction is not fully taken into account in this approach when the working fluid of heat exchanger is liquid. In order to circumvent this problem, the non-dimensional entransy dissipations associated with heat conduction and fluid friction are taken as two separate objective functions and a multi-objective optimization approach to heat exchanger design is established. In comparison with the single-objective optimization approach, the multi-objective optimization approach demonstrates more advantages and flexibilities for heat exchanger design.

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