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Article
Publication date: 8 February 2016

Jun Sun, Lei Shu, Xianhao Song, Guangsheng Liu, Feng Xu, Enming Miao, Zhihao Xu, Zheng Zhang and Junwei Zhao

This paper aims to use the crankshaft-bearing system of a four-cylinder internal combustion engine as the studying object, and develop a multi-objective optimization

Abstract

Purpose

This paper aims to use the crankshaft-bearing system of a four-cylinder internal combustion engine as the studying object, and develop a multi-objective optimization design of the crankshaft-bearing. In the current optimization design of engine crankshaft-bearing, only the crankshaft-bearing was considered as the studying object. However, the corresponding relations of major structure dimensions exist between the crankshaft and the crankshaft-bearing in internal combustion engine, and there are the interaction effects between the crankshaft and the crankshaft-bearing during the operation of internal combustion engine.

Design/methodology/approach

The crankshaft mass and the total frictional power loss of crankshaft-bearing s are selected as the objective functions in the optimization design of crankshaft-bearing. The Particle Swarm Optimization algorithm based on the idea of decreasing strategy of inertia weight with the exponential type is used in the optimization calculation.

Findings

The total frictional power loss of crankshaft-bearing and the crankshaft mass are decreased, respectively, by 26.2 and 5.3 per cent by the multi-objective optimization design of crankshaft-bearing, which are more reasonable than the ones of single-objective optimization design in which only the crankshaft-bearing is considered as the studying object.

Originality/value

The crankshaft-bearing system of a four-cylinder internal combustion engine is taken as the studying object, and the multi-objective optimization design of crankshaft-bearing based on the crankshaft-bearing system is developed. The results of this paper are helpful to the design of the crankshaft-bearing for engine. There is universal significance to research the multi-objective optimization design of crankshaft-bearing based on the crankshaft-bearing system. The research method of the multi-objective optimization design of crankshaft-bearing based on the crankshaft-bearing system can be used to the optimization design of the bearing in the shaft-bearing system of ordinary machinery.

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Article
Publication date: 8 July 2020

Deniz Ustun, Serdar Carbas and Abdurrahim Toktas

In line with computational technological advances, obtaining optimal solutions for engineering problems has become attractive research topics in various disciplines and…

Abstract

Purpose

In line with computational technological advances, obtaining optimal solutions for engineering problems has become attractive research topics in various disciplines and real engineering systems having multiple objectives. Therefore, it is aimed to ensure that the multiple objectives are simultaneously optimized by considering them among the trade-offs. Furthermore, the practical means of solving those problems are principally concentrated on handling various complicated constraints. The purpose of this paper is to suggest an algorithm based on symbiotic organisms search (SOS), which mimics the symbiotic reciprocal influence scheme adopted by organisms to live on and breed within the ecosystem, for constrained multi-objective engineering design problems.

Design/methodology/approach

Though the general performance of SOS algorithm was previously well demonstrated for ordinary single objective optimization problems, its efficacy on multi-objective real engineering problems will be decisive about the performance. The SOS algorithm is, hence, implemented to obtain the optimal solutions of challengingly constrained multi-objective engineering design problems using the Pareto optimality concept.

Findings

Four well-known mixed constrained multi-objective engineering design problems and a real-world complex constrained multilayer dielectric filter design problem are tackled to demonstrate the precision and stability of the multi-objective SOS (MOSOS) algorithm. Also, the comparison of the obtained results with some other well-known metaheuristics illustrates the validity and robustness of the proposed algorithm.

Originality/value

The algorithmic performance of the MOSOS on the challengingly constrained multi-objective multidisciplinary engineering design problems with constraint-handling approach is successfully demonstrated with respect to the obtained outperforming final optimal designs.

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Article
Publication date: 4 December 2017

Wu Deng, Meng Sun, Huimin Zhao, Bo Li and Chunxiao Wang

This study aims to propose a new airport gate assignment method to effectively improve the comprehensive operation capacity and efficiency of hub airport. Gate assignment…

Abstract

Purpose

This study aims to propose a new airport gate assignment method to effectively improve the comprehensive operation capacity and efficiency of hub airport. Gate assignment is one of the most important tasks for airport ground operations, which assigns appropriate airport gates with high efficiency reasonable arrangement.

Design/methodology/approach

In this paper, on the basis of analyzing the characteristics of airport gates and flights, an efficient multi-objective optimization model of airport gate assignment based on the objectives of the most balanced idle time, the shortest walking distances of passengers and the least number of flights at apron is constructed. Then an improved ant colony optimization (ICQACO) algorithm based on the ant colony collaborative strategy and pheromone update strategy is designed to solve the constructed model to fast realize the gate assignment and obtain a rational and effective gate assignment result for all flights in the different period.

Findings

In the designed ICQACO algorithm, the ant colony collaborative strategy is used to avoid the rapid convergence to the local optimal solution, and the pheromone update strategy is used to quickly increase the pheromone amount, eliminate the interference of the poor path and greatly accelerate the convergence speed.

Practical implications

The actual flight data from Guangzhou Baiyun airport of China is selected to verify the feasibility and effectiveness of the constructed multi-objective optimization model and the designed ICQACO algorithm. The experimental results show that the designed ICQACO algorithm can increase the pheromone amount, accelerate the convergence speed and avoid to fall into the local optimal solution. The constructed multi-objective optimization model can effectively improve the comprehensive operation capacity and efficiency. This study is a very meaningful work for airport gate assignment.

Originality/value

An efficient multi-objective optimization model for hub airport gate assignment problem is proposed in this paper. An improved ant colony optimization algorithm based on ant colony collaborative strategy and the pheromone update strategy is deeply studied to speed up the convergence and avoid to fall into the local optimal solution.

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Article
Publication date: 27 November 2018

Souhil Mouassa and Tarek Bouktir

In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a…

Abstract

Purpose

In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems. Multi-objective ORPD (MOORPD) aims to minimize simultaneously either the active power losses and voltage stability index, or the active power losses and the voltage deviation. The purpose of this paper is to propose multi-objective ant lion optimization (MOALO) algorithm to solve multi-objective ORPD problem considering large-scale power system in an effort to achieve a good performance with stable and secure operation of electric power systems.

Design/methodology/approach

A MOALO algorithm is presented and applied to solve the MOORPD problem. Fuzzy set theory was implemented to identify the best compromise solution from the set of the non-dominated solutions. A comparison with enhanced version of multi-objective particle swarm optimization (MOEPSO) algorithm and original (MOPSO) algorithm confirms the solutions. An in-depth analysis on the findings was conducted and the feasibility of solutions were fully verified and discussed.

Findings

Three test systems – the IEEE 30-bus, IEEE 57-bus and large-scale IEEE 300-bus – were used to examine the efficiency of the proposed algorithm. The findings obtained amply confirmed the superiority of the proposed approach over the multi-objective enhanced PSO and basic version of MOPSO. In addition to that, the algorithm is benefitted from good distributions of the non-dominated solutions and also guarantees the feasibility of solutions.

Originality/value

The proposed algorithm is applied to solve three versions of ORPD problem, active power losses, voltage deviation and voltage stability index, considering large -scale power system IEEE 300 bus.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 38 no. 1
Type: Research Article
ISSN: 0332-1649

Keywords

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Article
Publication date: 10 May 2019

Tarek Salama and Osama Moselhi

The purpose of this paper is to present a newly developed multi-objective optimization method for the time, cost and work interruptions for repetitive scheduling while…

Abstract

Purpose

The purpose of this paper is to present a newly developed multi-objective optimization method for the time, cost and work interruptions for repetitive scheduling while considering uncertainties associated with different input parameters.

Design/methodology/approach

The design of the developed method is based on integrating six modules: uncertainty and defuzzification module using fuzzy set theory, schedule calculations module using the integration of linear scheduling method (LSM) and critical chain project management (CCPM), cost calculations module that considers direct and indirect costs, delay penalty, and work interruptions cost, multi-objective optimization module using Evolver © 7.5.2 as a genetic algorithm (GA) software, module for identifying multiple critical sequences and schedule buffers, and reporting module.

Findings

For duration optimization that utilizes fuzzy inputs without interruptions or adding buffers, duration and cost generated by the developed method are found to be 90 and 99 percent of those reported in the literature, respectively. For cost optimization that utilizes fuzzy inputs without interruptions, project duration generated by the developed method is found to be 93 percent of that reported in the literature after adding buffers. The developed method accelerates the generation of optimum schedules.

Originality/value

Unlike methods reported in the literature, the proposed method is the first multi-objective optimization method that integrates LSM and the CCPM. This method considers uncertainties of productivity rates, quantities and availability of resources while utilizing multi-objective GA function to minimize project duration, cost and work interruptions simultaneously. Schedule buffers are assigned whether optimized schedule allows for interruptions or not. This method considers delay and work interruption penalties, and bonus payments.

Details

Engineering, Construction and Architectural Management, vol. 26 no. 7
Type: Research Article
ISSN: 0969-9988

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Book part
Publication date: 6 November 2013

Bartosz Sawik

This chapter presents the survey of selected linear and mixed integer programming multi-objective portfolio optimization. The definitions of selected percentile risk…

Abstract

This chapter presents the survey of selected linear and mixed integer programming multi-objective portfolio optimization. The definitions of selected percentile risk measures are presented. Some contrasts and similarities of the different types of portfolio formulations are drawn out. The survey of multi-criteria methods devoted to portfolio optimization such as weighting approach, lexicographic approach, and reference point method is also presented. This survey presents the nature of the multi-objective portfolio problems focuses on a compromise between the construction of objectives, constraints, and decision variables in a portfolio and the problem complexity of the implemented mathematical models. There is always a trade-off between computational time and the size of an input data, as well as the type of mathematical programming formulation with linear and/or mixed integer variables.

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Article
Publication date: 13 February 2020

Ho Pham Huy Anh and Cao Van Kien

The purpose of this paper is to propose an optimal energy management (OEM) method using intelligent optimization techniques applied to implement an optimally hybrid heat…

Abstract

Purpose

The purpose of this paper is to propose an optimal energy management (OEM) method using intelligent optimization techniques applied to implement an optimally hybrid heat and power isolated microgrid. The microgrid investigated combines renewable and conventional power generation.

Design/methodology/approach

Five bio-inspired optimization methods include an advanced proposed multi-objective particle swarm optimization (MOPSO) approach which is comparatively applied for OEM of the implemented microgrid with other bio-inspired optimization approaches via their comparative simulation results.

Findings

Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization methods. Moreover, the proposed MOPSO is successfully applied to perform 24-h OEM microgrid. The simulation results also display the merits of the real time optimization along with the arbitrary of users’ selection as to satisfy their power requirement.

Originality/value

This paper focuses on the OEM of a designed microgrid using a newly proposed modified MOPSO algorithm. Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization approaches.

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Article
Publication date: 16 April 2018

Fábio Monteiro Conde, Pedro Gonçalves Coelho, Rodrigo Paiva Tavares, Pedro Castro Camanho, José Miranda Guedes and Helder Carriço Rodrigues

This study aims to achieve a “pseudo-ductile” behaviour in the response of hybrid fibre reinforced composites under uniaxial traction by solving properly formulated…

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Abstract

Purpose

This study aims to achieve a “pseudo-ductile” behaviour in the response of hybrid fibre reinforced composites under uniaxial traction by solving properly formulated optimization problems.

Design/methodology/approach

The composite material model is based on the combination of different types of fibres (with different failure strains or strengths) embedded in a polymer matrix. The composite failure under tensile load is predicted by analytical models. An optimization problem formulation is proposed and a Genetic Algorithm is used. Multi-objective optimization problems balancing failure strength and ductility criteria are solved providing optimal mixtures of fibres whose properties may come either from a pre-defined list of materials, currently available in the market, or simply assuming their continuum variation within predefined bounds, in an attempt to attain unprecedented performance levels.

Findings

Optimal solutions of hybrid fibre reinforced composites exhibiting pseudo-ductile behaviour are presented. It is found that a fibre made from a material exhibiting relatively low stiffness combined with high strength is preferred for hybridization. Furthermore, the ratio of the average failure/critical strains between the low and high elongation fibres to be hybridized must be equal or greater than two.

Originality/value

Typically, a ductile failure is an inherent property of metals, that is, their typical response curve after the linear (elastic) region exhibits a yielding plateau still followed by an increase in stress till collapse. In stark contrast, composite materials exhibit (under some loading conditions) brittle failure that may limit their widespread usage. Therefore, a “pseudo-ductility” in composites is valued and targeted through optimization which is the main original contribution here.

Details

Engineering Computations, vol. 35 no. 2
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 5 January 2015

Giovanni Droandi and Giuseppe Gibertini

The purpose of this paper is to present the aerodynamic blade design of a tiltwing aircraft with a multi-objective optimization procedure. The aerodynamic design of…

Abstract

Purpose

The purpose of this paper is to present the aerodynamic blade design of a tiltwing aircraft with a multi-objective optimization procedure. The aerodynamic design of tiltrotor blades is a very challenging task in the project of this type of aircraft.

Design/methodology/approach

Tiltrotor blades have to give good performance both in helicopter and aeroplane modes. According to the design parameters (the chords, the twists and the airfoils along the blade), as the optimization objectives are different from one operating condition to another, the blade is the result of a multi-objective constrained optimization based on a controlled elitist genetic algorithm founded on the NSGA-II algorithm. The optimization process uses a BEMT solver to compute rotor performance. To avoid negative effects due to compressibility losses in aeroplane mode, the blade shape has been refined following the normal Mach number criterion.

Findings

It has been found that the optimized rotor blade gives good performance both in terms of figure of merit and propulsive efficiency if compared with experimental data of existing rotor (ERICA tiltrotor) and propeller (NACA high-speed propeller).

Practical implications

The optimization procedure described in this paper for the design of tiltrotor blades can be efficiently used for the aerodynamic design of helicopter rotors and aircraft propellers of all typology.

Originality/value

In this work, advanced methodologies have been used for the aerodynamics design of a proprotor optimized for an aircraft which belongs to the innovative typology of high-performance tiltwing tiltrotor aircraft.

Details

Aircraft Engineering and Aerospace Technology: An International Journal, vol. 87 no. 1
Type: Research Article
ISSN: 0002-2667

Keywords

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Article
Publication date: 25 January 2021

Hafed Touahar, Nouara Ouazraoui, Nor El Houda Khanfri, Mourad Korichi, Bilal Bachi and Houcem Eddine Boukrouma

The main objective of safety instrumented systems (SISs) is to maintain a safe condition of a facility if hazardous events occur. However, in some cases, SIS's can be…

Abstract

Purpose

The main objective of safety instrumented systems (SISs) is to maintain a safe condition of a facility if hazardous events occur. However, in some cases, SIS's can be activated prematurely, these activations are characterized in terms of frequency by a Spurious Trip Rate (STR) and their occurrence leads to significant technical, economic and even environmental losses. This work aims to propose an approach to optimize the performances of the SIS by a multi-objective genetic algorithm. The optimization of SIS performances is performed using the multi-objective genetic algorithm by minimizing their probability of failure on demand PFDavg, Spurious Trip Rate (STR) and Life Cycle Costs (LCCavg). A set of constraints related to maintenance costs have been established. These constraints imply specific maintenance strategies which improve the SIS performances and minimize the technical, economic and environmental risks related to spurious shutdowns. Validation of such an approach is applied to an Emergency Shutdown (ESD) of the blower section of an industrial facility (RGTE- In Amenas).

Design/methodology/approach

The optimization of SIS performances is performed using the multi-objective genetic algorithm by minimizing their probability of failure on demand PFDavg, Spurious Trip Rate (STR) and Life Cycle Costs (LCCavg). A set of constraints related to maintenance costs have been established. These constraints imply specific maintenance strategies which improve the SIS performances and minimize the technical, economic and environmental risks related to spurious shutdowns. Validation of such an approach is applied to an Emergency Shutdown (ESD) of the blower section of an industrial facility (RGTE- In Amenas).

Findings

A case study concerning a safety instrumented system implemented in the RGTE facility has shown the great applicability of the proposed approach and the results are encouraging. The results show that the selection of a good maintenance strategy allows a very significant minimization of the PFDavg, the frequency of spurious trips and Life Cycle Costs of SIS.

Originality/value

The maintenance strategy defined by the system designer can be modified and improved during the operational phase, in particular safety systems. It constitutes one of the least expensive investment strategies for improving SIS performances. It has allowed a considerable minimization of the SIS life cycle costs; PFDavg and the frequency of spurious trips.

Details

International Journal of Quality & Reliability Management, vol. 38 no. 8
Type: Research Article
ISSN: 0265-671X

Keywords

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