Search results

1 – 10 of 626
To view the access options for this content please click here
Article
Publication date: 1 February 2002

Mun‐Bo Shim, Myung‐Won Suh, Tomonari Furukawa, Genki Yagawa and Shinobu Yoshimura

In an attempt to solve multiobjective optimization problems, many traditional methods scalarize an objective vector into a single objective by a weight vector. In these…

Abstract

In an attempt to solve multiobjective optimization problems, many traditional methods scalarize an objective vector into a single objective by a weight vector. In these cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands a user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto‐optimal points, instead of a single point. In this paper, Pareto‐based Continuous Evolutionary Algorithms for Multiobjective Optimization problems having continuous search space are introduced. These algorithms are based on Continuous Evolutionary Algorithms, which were developed by the authors to solve single‐objective optimization problems with a continuous function and continuous search space efficiently. For multiobjective optimization, a progressive reproduction operator and a niche‐formation method for fitness sharing and a storing process for elitism are implemented in the algorithm. The operator and the niche formulation allow the solution set to be distributed widely over the Pareto‐optimal tradeoff surface. Finally, the validity of this method has been demonstrated through some numerical examples.

Details

Engineering Computations, vol. 19 no. 1
Type: Research Article
ISSN: 0264-4401

Keywords

To view the access options for this content please click here
Article
Publication date: 1 February 1986

MASATOSHI SAKAWA and HITOSHI YANO

This paper presents an interactive fuzzy satisfying method by assuming that the decision maker (DM) has fuzzy goals for each of the objective functions in multiobjective…

Abstract

This paper presents an interactive fuzzy satisfying method by assuming that the decision maker (DM) has fuzzy goals for each of the objective functions in multiobjective nonlinear programming problems. The fuzzy goals of the DM are quantified by eliciting the corresponding membership functions through the interaction with the DM. After determining the membership functions for each of the objective functions, in order to generate a candidate for the satisficing solution which is also a Pareto optimal, the DM selects an appropriate standing membership function and specifies his/her aspiration levels of achievement of the other membership functions, called constraint membership values. For the DM's constraint membership values, the corresponding constraint problem is solved and the DM is supplied with the Pareto optima] solution together with the trade‐off rates between a standing membership function and each of the other membership functions. Then by considering the current values of the membership functions as well as the trade‐off rates, the DM acts on this solution by updating his/her constraint membership values. In this way, the satisficing solution for the DM can be derived efficiently from among a Pareto optimal solution set by updating his/her constraint membership values. On the basis of the proposed method, a time‐sharing computer program is written and an application to regional planning is demonstrated along with the corresponding computer outputs.

Details

Kybernetes, vol. 15 no. 2
Type: Research Article
ISSN: 0368-492X

To view the access options for this content please click here
Article
Publication date: 1 September 2005

J. Régnier, B. Sareni and X. Roboam

This paper presents a methodology based on Multiobjective Genetic Algorithms (MOGAs) for the design of electrical engineering systems. MOGAs allow one to optimize multiple…

Abstract

Purpose

This paper presents a methodology based on Multiobjective Genetic Algorithms (MOGAs) for the design of electrical engineering systems. MOGAs allow one to optimize multiple heterogeneous criteria in complex systems, but also simplify couplings and sensitivity analysis by determining the evolution of design variables along the Pareto‐optimal front.

Design/methodology/approach

To illustrate the use of MOGAs in electrical engineering, the optimal design of an electromechanical system has been investigated. A rather simplified case study dealing with the optimal dimensioning of an inverter – permanent magnet motor – reducer – load association is carried out to demonstrate the interest of the approach. The purpose is to simultaneously minimize two objectives: the global losses and the mass of the system. The system model is described by analytical model and we use the MOGA called NSGA‐II.

Findings

From the extraction of Pareto‐optimal solutions, MOGAs facilitate the investigation of parametric sensitivity and the analysis of couplings in the system. Through a simple but typical academic problem dealing with the optimal dimensioning of a inverter – permanent magnet motor – reducer – load association, it has been shown that this multiobjective a posteriori approach could offer interesting outlooks in the global optimization and design of complex heterogeneous systems. The final choice between all Pareto‐optimal configurations can be a posteriori done in relation to other issues which have not been considered in the optimization process. In this paper, we illustrate this point by considering the cogging torque for the final decision.

Originality/value

We have proposed an original quantitative methodology based on correlation coefficients to characterize the system interactions.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 24 no. 3
Type: Research Article
ISSN: 0332-1649

Keywords

To view the access options for this content please click here
Article
Publication date: 16 November 2018

Masatoshi Muramatsu and Takeo Kato

The purpose of this paper is to propose the selection guide of the multi-objective optimization methods for the ergonomic design. The proposed guide enables designers to…

Abstract

Purpose

The purpose of this paper is to propose the selection guide of the multi-objective optimization methods for the ergonomic design. The proposed guide enables designers to select an appropriate method for optimizing the human characteristics composed of the engineering characteristics (e.g. users’ height, weight and muscular strength) and the physiological characteristics (e.g. brain wave, pulse-beat and myoelectric signal) in the trade-off relationships.

Design/methodology/approach

This paper focuses on the types of the relationships between engineering or physiological characteristics and their psychological characteristics (e.g. comfort and usability). Using these relationships and the characteristics of the multi-objective optimization methods, this paper classified them and constructed a flow chart for selecting them.

Findings

This paper applied the proposed selection guide to a geometric design of a comfortable seat and confirmed its applicability. The selected multi-objective optimization method optimized the contact area of seat back (engineering characteristic associated with the comfortable fit of the seat backrest) and the blood flow volume (physiological characteristic associated with the numbness in the lower limb) on the basis of each design intent such as a deep-vein thrombosis after long flight.

Originality/value

Because of the lack of the selection guide of the multi-objective optimization methods, an inappropriate method is often applied in industry. This paper proposed the selection guide applied in the ergonomic design having a lot of the multi-objective optimization problem.

Details

Journal of Engineering, Design and Technology, vol. 17 no. 1
Type: Research Article
ISSN: 1726-0531

Keywords

To view the access options for this content please click here
Article
Publication date: 16 May 2016

Emad Elbeltagi, Mohammed Ammar, Haytham Sanad and Moustafa Kassab

Developing an optimized project schedule that considers all decision criteria represents a challenge for project managers. The purpose of this paper is to provide a…

Downloads
1564

Abstract

Purpose

Developing an optimized project schedule that considers all decision criteria represents a challenge for project managers. The purpose of this paper is to provide a multi-objectives overall optimization model for project scheduling considering time, cost, resources, and cash flow. This development aims to overcome the limitations of optimizing each objective at once resulting of non-overall optimized schedule.

Design/methodology/approach

In this paper, a multi-objectives overall optimization model for project scheduling is developed using particle swarm optimization with a new evolutionary strategy based on the compromise solution of the Pareto-front. This model optimizes the most important decisions that affect a given project including: time, cost, resources, and cash flow. The study assumes each activity has different execution methods accompanied by different time, cost, cost distribution pattern, and multiple resource utilization schemes.

Findings

Applying the developed model to schedule a real-life case study project proves that the proposed model is valid in modeling real-life construction projects and gives important results for schedulers and project managers. The proposed model is expected to help construction managers and decision makers in successfully completing the project on time and reduced budget by utilizing the available information and resources.

Originality/value

The paper presented a novel model that has four main characteristics: it produces an optimized schedule considering time, cost, resources, and cash flow simultaneously; it incorporates a powerful particle swarm optimization technique to search for the optimum schedule; it applies multi-objectives optimization rather than single-objective and it uses a unique Pareto-compromise solution to drive the fitness calculations of the evolutionary process.

Details

Engineering, Construction and Architectural Management, vol. 23 no. 3
Type: Research Article
ISSN: 0969-9988

Keywords

To view the access options for this content please click here
Article
Publication date: 16 November 2010

Christian Magele, Michael Jaindl, Alice Köstinger, Werner Renhart, Bogdan Cranganu‐Cretu and Jasmin Smajic

The purpose of this paper is to extend a (μ/ρ, λ) evolution strategy to perform remarkably more globally and to detect as many solutions as possible close to the Pareto

Abstract

Purpose

The purpose of this paper is to extend a (μ/ρ, λ) evolution strategy to perform remarkably more globally and to detect as many solutions as possible close to the Pareto optimal front.

Design/methodology/approach

A C‐link cluster algorithm is used to group the parameter configurations of the current population into more or less independent clusters. Following this procedure, recombination (a classical operator of evolutionary strategies) is modified. Recombination within a cluster is performed with a higher probability than recombination of individuals coming from detached clusters.

Findings

It is shown that this new method ends up virtually always in the global solution of a multi‐modal test function. When applied to a real‐world application, several solutions very close to the front of Pareto optimal solutions are detected.

Research limitations/implications

Stochastic optimization strategies need a very large number of function calls to exhibit their ability to reach very good local if not the global solution. Therefore, the application of such methods is still limited to problems where the forward solutions can be obtained with a reasonable computational effort.

Originality/value

The main improvement is the usage of approximate number of isolated clusters to dynamically update the size of the population in order to save computation time, to find the global solution with a higher probability and to use more than one objective function to cover a larger part of the Pareto optimal front.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 29 no. 6
Type: Research Article
ISSN: 0332-1649

Keywords

To view the access options for this content please click here
Article
Publication date: 22 June 2010

Konstantinos Kirytopoulos, Vrassidas Leopoulos, George Mavrotas and Dimitra Voulgaridou

The strategic importance of sourcing is inherent in the positioning of the purchasing operation in a supply chain and supplier evaluation – a crucial step in sourcing – is…

Downloads
2919

Abstract

Purpose

The strategic importance of sourcing is inherent in the positioning of the purchasing operation in a supply chain and supplier evaluation – a crucial step in sourcing – is a complex multicriteria decision making (MCDM) problem. The purpose of this paper is to provide a meta‐model for supplier evaluation and order quantity allocation, based on a MCDM method, namely the Analytic Network Process (ANP) and a multiobjective mathematical programming method (MOMP), the AUGMECON.

Design/methodology/approach

The proposed approach consists of two parts. The former develops and applies the ANP method in order to evaluate the suppliers in qualitative terms. The latter implements the AUGMECON method in order to find the Pareto optimal solutions for the allocation of order quantities in a multiple sourcing environment. The integrated meta‐model is exposed through an illustrative case concerning the parapharmaceutical enterprise cluster in Greece.

Findings

The proposed meta‐model constitutes an efficient method that enables managers to actively participate in the decision making process and exploit the “qualitative value” of their suppliers, while minimizing the costs and the mean delivery times. In addition, it is proved to be suitable for the enterprise clusters, as it adapts a multiple sourcing strategy and enhances the partnership among the members.

Research limitations/implications

The outcome of the model is highly dependent on the inputs provided by the decision maker. Moreover, the ANP method is computational intensive, but this limitation can be alleviated by appropriate software tools.

Originality/value

The proposed meta‐model is an innovative approach for decision making in the area of multiple sourcing and order allocation.

Details

Supply Chain Management: An International Journal, vol. 15 no. 4
Type: Research Article
ISSN: 1359-8546

Keywords

To view the access options for this content please click here
Article
Publication date: 8 November 2018

Amos H.C. Ng, Florian Siegmund and Kalyanmoy Deb

Stochastic simulation is a popular tool among practitioners and researchers alike for quantitative analysis of systems. Recent advancement in research on formulating…

Abstract

Purpose

Stochastic simulation is a popular tool among practitioners and researchers alike for quantitative analysis of systems. Recent advancement in research on formulating production systems improvement problems into multi-objective optimizations has provided the possibility to predict the optimal trade-offs between improvement costs and system performance, before making the final decision for implementation. However, the fact that stochastic simulations rely on running a large number of replications to cope with the randomness and obtain some accurate statistical estimates of the system outputs, has posed a serious issue for using this kind of multi-objective optimization in practice, especially with complex models. Therefore, the purpose of this study is to investigate the performance enhancements of a reference point based evolutionary multi-objective optimization algorithm in practical production systems improvement problems, when combined with various dynamic re-sampling mechanisms.

Design/methodology/approach

Many algorithms consider the preferences of decision makers to converge to optimal trade-off solutions faster. There also exist advanced dynamic resampling procedures to avoid wasting a multitude of simulation replications to non-optimal solutions. However, very few attempts have been made to study the advantages of combining these two approaches to further enhance the performance of computationally expensive optimizations for complex production systems. Therefore, this paper proposes some combinations of preference-based guided search with dynamic resampling mechanisms into an evolutionary multi-objective optimization algorithm to lower both the computational cost in re-sampling and the total number of simulation evaluations.

Findings

This paper shows the performance enhancements of the reference-point based algorithm, R-NSGA-II, when augmented with three different dynamic resampling mechanisms with increasing degrees of statistical sophistication, namely, time-based, distance-rank and optimal computing buffer allocation, when applied to two real-world production system improvement studies. The results have shown that the more stochasticity that the simulation models exert, the more the statistically advanced dynamic resampling mechanisms could significantly enhance the performance of the optimization process.

Originality/value

Contributions of this paper include combining decision makers’ preferences and dynamic resampling procedures; performance evaluations on two real-world production system improvement studies and illustrating statistically advanced dynamic resampling mechanism is needed for noisy models.

Details

Journal of Systems and Information Technology, vol. 20 no. 4
Type: Research Article
ISSN: 1328-7265

Keywords

To view the access options for this content please click here
Article
Publication date: 6 July 2021

Emmanuel Frimpong and Elvis Twumasi

The paper presents a technique for predicting the energy consumption of unregulated energy loads (UELs) in office buildings. It also presents an approach for determining a…

Abstract

Purpose

The paper presents a technique for predicting the energy consumption of unregulated energy loads (UELs) in office buildings. It also presents an approach for determining a set of optimum values required by the technique.

Design/methodology/approach

The proposed technique uses the optimum power drawn and optimum usage period in three modes of device operation, for the prediction. The usage modes are active mode, idle (low active) mode and off mode. The optimum powers and usage times are inserted into a linear mathematical equation to predict the energy consumption. Regarding the approach for determining the optimum values, the non-dominated sorting genetic algorithm II (NSGA-II) is applied to a range of values obtained from field measurements. The proposed prediction method and approach for determining optimum values were tested using data of energy consumption of UELs in a case study facility.

Findings

Test results show that the method for predicting the energy consumption of UELs in offices is highly accurate and suitable for adoption by energy modelers, building designers and building regulatory agencies. The approach for determining the optimum values is also effective and can aid the establishment of workable benchmark values.

Originality/value

A new and simple model has been developed for the prediction of unregulated energy. A method for determining a set of optimum values of power and usage periods required by the model has also been developed. Furthermore, optimum values have been suggested that can be fine-tuned for use as benchmark values. The proposed approaches are the first of their kind.

Details

International Journal of Building Pathology and Adaptation, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2398-4708

Keywords

To view the access options for this content please click here
Article
Publication date: 13 April 2021

Mariana Souza Rocha, Luiz Célio Souza Rocha, Marcia Barreto da Silva Feijó, Paula Luiza Limongi dos Santos Marotta and Samanta Cardozo Mourão

The mucilage of the Linum usitatissimum L. seed (Linseed) is one of the natural mucilages that presents a great potential to provide a food hydrocolloid with potential…

Abstract

Purpose

The mucilage of the Linum usitatissimum L. seed (Linseed) is one of the natural mucilages that presents a great potential to provide a food hydrocolloid with potential applications in both food and pharmaceutical industries. To increase the yield and quality of linseed oil during its production process, it is necessary to previously extract its polysaccharides. Because of this, flax mucilage production can be made viable as a byproduct of oil extraction process, which is already a product of high commercial value consolidated in the market. Thus, the purpose of this work is to optimize the mucilage extraction process of L. usitatissimum L. using the normal-boundary intersection (NBI) multiobjective optimization method.

Design/methodology/approach

Currently, the variables of the process of polysaccharide extraction from different sources are optimized using the response surface methodology. However, when the optimal points of the responses are conflicting it is necessary to study the best conditions to achieve a balance between these conflicting objectives (trade-offs) and to explore the available options it is necessary to formulate an optimization problem with multiple objectives. The multiobjective optimization method used in this work was the NBI developed to find uniformly distributed and continuous Pareto optimal solutions for a nonlinear multiobjective problem.

Findings

The optimum extraction point to obtain the maximum fiber concentration in the extracted material was pH 3.81, temperature of 46°C, time of 13.46 h. The maximum extraction yield of flaxseed was pH 6.45, temperature of 65°C, time of 14.41 h. This result confirms the trade-off relationship between the objectives. NBI approach was able to find uniformly distributed Pareto optimal solutions, which allows to analyze the behavior of the trade-off relationship. Thus, the decision-maker can set extraction conditions to achieve desired characteristics in mucilage.

Originality/value

The novelty of this paper is to confirm the existence of a trade-off relationship between the productivity parameter (yield) and the quality parameter (fiber concentration in the extracted material) during the flaxseed mucilage extraction process. The NBI approach was able to find uniformly distributed Pareto optimal solutions, which allows us to analyze the behavior of the trade-off relationship. This allows the decision-making to the extraction conditions according to the desired characteristics of the final product, thus being able to direct the extraction for the best applicability of the mucilage.

Details

British Food Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0007-070X

Keywords

1 – 10 of 626