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Article
Publication date: 29 November 2020

Yiying Li and Shiyou Yang

The purpose of this paper is to develop a pertinent design optimization methodology for symmetric designs of a metamaterial (MM) unit.

Abstract

Purpose

The purpose of this paper is to develop a pertinent design optimization methodology for symmetric designs of a metamaterial (MM) unit.

Design/methodology/approach

A cell division mechanism is introduced and used to design a new selecting mechanism in the proposed algorithm, a non-dominated sorting cellular genetic algorithm (NSCGA).

Findings

The numerical results on solving standard multi-objective test functions and a prototype MM unit positively demonstrate the advantages of the proposed NSCGA.

Originality/value

A new NSGAII-based optimization algorithm, NSCGA, for multi-objective optimization designs of a MM unit is proposed.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 40 no. 1
Type: Research Article
ISSN: 0332-1649

Keywords

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Article
Publication date: 2 October 2017

Tawfik Guesmi and Badr M. Alshammari

Low-frequency oscillations of 0.1 to 3 Hz are prejudicial to the power system stability. Within this context, this study aims to present an improved artificial bee colony…

Abstract

Purpose

Low-frequency oscillations of 0.1 to 3 Hz are prejudicial to the power system stability. Within this context, this study aims to present an improved artificial bee colony (ABC)-based algorithm for optimal setting of multimachine power system stabilizers (PSSs) under several loading conditions simultaneously.

Design/methodology/approach

The proposed approach symbolized by GCABC incorporates the grenade explosion technique and the Cauchy operator in the employed bee and onlooker bee phases to avoid random search. The parameters of the grenade explosion method and Cauchy operator based ABC(GCABC)-based PSSs (GCABC-PSSs) are tuned to place all undamped and lightly damped electromechanical modes in a prespecified zone in the s-plan.

Findings

Simulation results based on eigenvalue analysis and nonlinear time-domain simulation show the potential and the dominance of the proposed controllers GCABC-PSSs in the improvement of the system stability under several disturbances and large set of operating points compared with the classical ABC method and genetic algorithm-based PSSs.

Originality/value

The novelty of the study is to efficiently implement a new optimization method called GCABC for an optimum design of PSSs. The design problem is formulated as a multi-objective optimization problem. In addition, all PSS parameters have been included in the space research.

Details

Engineering Computations, vol. 34 no. 7
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 8 May 2017

Mahdi Rezaei, Mohsen Akbarpour Shirazi and Behrooz Karimi

The purpose of this paper is to develop an Internet of Things (IoT)-based framework for supply chain (SC) performance measurement and real-time decision alignment. The…

Abstract

Purpose

The purpose of this paper is to develop an Internet of Things (IoT)-based framework for supply chain (SC) performance measurement and real-time decision alignment. The aims of the proposed model are to optimize the performance indicator based on integrated supply chain operations reference metrics.

Design/methodology/approach

The SC multi-dimensional structure is modeled by multi-objective optimization methods. The operational presented model considers important SC features thoroughly such as multi-echelons, several suppliers, several manufacturers and several products during multiple periods. A multi-objective mathematical programming model is then developed to yield the operational decisions with Pareto efficient performance values and solved using a well-known meta-heuristic algorithm, i.e., non-dominated sorting genetic algorithm II. Afterward, Technique for Order of Preference by Similarity to Ideal Solution method is used to determine the best operational solution based on the strategic decision maker’s idea.

Findings

This paper proposes a dynamic integrated solution for three main problems: strategic decisions in high level, operational decisions in low level and alignment of these two decision levels.

Originality/value

The authors propose a human intelligence-based process for high level decision and machine intelligence-based decision support systems for low level decision using a novel approach. High level and low level decisions are aligned by a machine intelligence model as well. The presented framework is based on change detection, event driven planning and real-time decision alignment.

Details

Industrial Management & Data Systems, vol. 117 no. 4
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 5 March 2018

Benoit Delinchant, Guillaume Mandil and Frédéric Wurtz

Life cycle analysis (LCA) is more and more used in the context of electromagnetic product design. But it is often used to check a design solution regarding environmental…

Abstract

Purpose

Life cycle analysis (LCA) is more and more used in the context of electromagnetic product design. But it is often used to check a design solution regarding environmental impacts after technical and economical choices. This paper aims to investigate life cycle impact optimization (LCIO) and compare it with the classical life cycle cost optimization (LCCO).

Design/methodology/approach

First, a model of a dry-type transformer using different materials for windings and the magnetic core is presented. LCCO, which is a mixed continuous-discrete, multi-objective technico-economic optimization, is done using both deterministic and genetic algorithms. LCCO results and optimization performances are analyzed, and an LCA is presented for a set of optimal solutions. The final part is dedicated to LCIO, where the paper shows that these optimal solutions are close to those obtained with LCCO.

Findings

This paper investigated LCIO using an environmental impacts model that has been introduced in the optimization framework Component Architecture for the Design of Engineering Systems. The paper shows how a mixed continuous-discrete, multi-objective technico-economic optimization can be done using an efficient deterministic optimization algorithm such as Sequential Quadratic Programming. Thanks to the technico-economic-environmental model and the efficient optimization algorithm, both LCCO and LCIO were performed separately and together. It has been shown that optimal solutions are similar, leading to the conclusion that only one modeling is required (economic or environmental) but on the life cycle.

Originality/value

The classical sequential methodology of design is improved here by the use of a model of calculation of the environmental impacts allowing the optimization. This original optimization allowed the authors to show that an analysis of the life cycle from an economic point of view or from an environmental point of view led to quasi-equivalent technical solutions. The key is to take into account the life cycle of the product.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 37 no. 2
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 4 September 2019

Behzad Karimi, Mahsa Ghare Hassanlu and Amir Hossein Niknamfar

The motivation behind this research refers to the significant role of integration of production-distribution plans in effective performance of supply chain networks under…

Abstract

Purpose

The motivation behind this research refers to the significant role of integration of production-distribution plans in effective performance of supply chain networks under fierce competition of today’s global marketplace. In this regard, this paper aims to deal with an integrated production-distribution planning problem in deterministic, multi-product and multi-echelon supply chain network. The bi-objective mixed-integer linear programming model is constructed to minimize not only the total transportation costs but also the total delivery time of supply chain, subject to satisfying retailer demands and capacity constraints where quantity discount on transportation costs, fixed cost associated with transportation vehicles usage and routing decisions have been included in the model.

Design/methodology/approach

As the proposed mathematical model is NP-hard and that finding an optimum solution in polynomial time is not reasonable, two multi-objective meta-heuristic algorithms, namely, non-dominated sorting genetic algorithm II (NSGAII) and multi-objective imperialist competitive algorithm (MOICA) are designed to obtain near optimal solutions for real-sized problems in reasonable computational times. The Taguchi method is then used to adjust the parameters of the developed algorithms. Finally, the applicability of the proposed model and the performance of the solution methodologies in comparison with each other are demonstrated for a set of randomly generated problem instances.

Findings

The practicality and applicability of the proposed model and the efficiency and efficacy of the developed solution methodologies were illustrated through a set of randomly generated real-sized problem instances. Result. In terms of two measures, the objective function value and the computational time were required to get solutions.

Originality/value

The main contribution of the present work was addressing an integrated production-distribution planning problem in a broader view, by proposing a closer to reality mathematical formulation which considers some real-world constraints simultaneously and accompanied by efficient multi-objective meta-heuristic algorithms to provide effective solutions for practical problem sizes.

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Article
Publication date: 16 May 2019

Abhilasha Panwar, Kamalendra Kumar Tripathi and Kumar Neeraj Jha

The purpose of this paper is to develop a qualitative framework for the selection of the most appropriate optimization algorithm for the multi-objective trade-off problem…

Abstract

Purpose

The purpose of this paper is to develop a qualitative framework for the selection of the most appropriate optimization algorithm for the multi-objective trade-off problem (MOTP) in construction projects based on the predefined performance parameters.

Design/methodology/approach

A total of 6 optimization algorithms and 13 performance parameters were identified through literature review. The experts were asked to indicate their preferences between each pair of optimization algorithms and performance parameters. A multi-criteria decision-making tool, namely, consistent fuzzy preference relation was applied to analyze the responses of the experts. The results from the analysis were applied to evaluate their relative weights which were used to provide a ranking to the algorithms.

Findings

This study provided a qualitative framework which can be used to identify the most appropriate optimization algorithm for the MOTP beforehand. The outcome suggested that non-dominated sorting genetic algorithm (NSGA) was the most appropriate algorithm whereas linear programming was found to be the least appropriate for MOTPs.

Originality/value

The devised framework may provide a useful insight for the construction practitioners to choose an effective optimization algorithm tool for preparing an efficient project schedule aiming toward the desired optimal improvement in achieving the various objectives. Identification of the absolute best optimization algorithm is very difficult to attain due to various problems such as the inherent complexities and intricacies of the algorithm and different class of problems. However, the devised framework offers a primary insight into the selection of the most appropriate alternative among the available algorithms.

Details

Engineering, Construction and Architectural Management, vol. 26 no. 9
Type: Research Article
ISSN: 0969-9988

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Article
Publication date: 18 October 2018

Subhamita Chakraborty, Prasun Das, Naveen Kumar Kaveti, Partha Protim Chattopadhyay and Shubhabrata Datta

The purpose of this paper is to incorporate prior knowledge in the artificial neural network (ANN) model for the prediction of continuous cooling transformation (CCT…

Abstract

Purpose

The purpose of this paper is to incorporate prior knowledge in the artificial neural network (ANN) model for the prediction of continuous cooling transformation (CCT) diagram of steel, so that the model predictions become valid from materials engineering point of view.

Design/methodology/approach

Genetic algorithm (GA) is used in different ways for incorporating system knowledge during training the ANN. In case of training, the ANN in multi-objective optimization mode, with prediction error minimization as one objective and the system knowledge incorporation as the other, the generated Pareto solutions are different ANN models with better performance in at least one objective. To choose a single model for the prediction of steel transformation, different multi-criteria decision-making (MCDM) concepts are employed. To avoid the problem of choosing a single model from the non-dominated Pareto solutions, the training scheme also converted into a single objective optimization problem.

Findings

The prediction results of the models trained in multi and single objective optimization schemes are compared. It is seen that though conversion of the problem to a single objective optimization problem reduces the complexity, the models trained using multi-objective optimization are found to be better for predicting metallurgically justifiable result.

Originality/value

ANN is being used extensively in the complex materials systems like steel. Several works have been done to develop ANN models for the prediction of CCT diagram. But the present work proposes some methods to overcome the inherent problem of data-driven model, and make the prediction viable from the system knowledge.

Details

Multidiscipline Modeling in Materials and Structures, vol. 15 no. 1
Type: Research Article
ISSN: 1573-6105

Keywords

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Article
Publication date: 23 August 2019

Sahar Tadayonirad, Hany Seidgar, Hamed Fazlollahtabar and Rasoul Shafaei

In real manufacturing systems, schedules are often disrupted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job…

Abstract

Purpose

In real manufacturing systems, schedules are often disrupted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job cancellations. This paper aims to investigate robust scheduling for a two-stage assembly flow shop scheduling with random machine breakdowns and considers two objectives makespan and robustness simultaneously.

Design/methodology/approach

Owing to its structural and algorithmic complexity, the authors proposed imperialist competitive algorithm (ICA), genetic algorithm (GA) and hybridized with simulation techniques for handling these complexities. For better efficiency of the proposed algorithms, the authors used artificial neural network (ANN) to predict the parameters of the proposed algorithms in uncertain condition. Also Taguchi method is applied for analyzing the effect of the parameters of the problem on each other and quality of solutions.

Findings

Finally, experimental study and analysis of variance (ANOVA) is done to investigate the effect of different proposed measures on the performance of the obtained results. ANOVA's results indicate the job and weight of makespan factors have a significant impact on the robustness of the proposed meta-heuristics algorithms. Also, it is obvious that the most effective parameter on the robustness for GA and ICA is job.

Originality/value

Robustness is calculated by the expected value of the relative difference between the deterministic and actual makespan.

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Article
Publication date: 11 October 2019

Fahimeh Tanhaie, Masoud Rabbani and Neda Manavizadeh

In this study, a mixed-model assembly line (MMAL) balancing problem is applied in a make-to-order (MTO) environment. One of the important problems in MTO systems is…

Abstract

Purpose

In this study, a mixed-model assembly line (MMAL) balancing problem is applied in a make-to-order (MTO) environment. One of the important problems in MTO systems is identifying the control points, which is considered by designing a control system. Furthermore, the worker assignment problem is defined by considering abilities and operating costs of workers. The proposed model is solved in two stages. First, a multi-objective model by simultaneously minimizing the number of stations and the total cost of the task duplication and workers assignment is considered. The second stage is designing a control system to minimize the work in process.

Design/methodology/approach

To solve this problem, a non-dominated sorting genetic algorithm (NSGA-II) is introduced and the proposed model is compared with four multi-objective algorithms (MOAs).

Findings

The proposed model is compared with four MOAs, i.e. multi-objective particle swarm optimization, multi-objective ant colony optimization, multi-objective firefly algorithm and multi-objective simulated annealing algorithm. The computational results of the NSGA-II algorithm are superior to the other algorithms, and multi-objective ant colony optimization has the best running time of the four MOA algorithms.

Practical implications

With attention to workers assignment in a MTO environment for the MMAL balancing problem, the present research has several significant implications for the rapidly changing manufacturing challenge.

Originality/value

To the best of the authors’ knowledge, no study has provided for the MMAL balancing problem in a MTO environment considering control points. This study provides the first attempt to fill this research gap. Also, a usual assumption in the literature that common tasks of different models must be assigned to a single station is relaxed and different types of real assignment restrictions like resource restrictions and tasks restrictions are described.

Details

Journal of Modelling in Management, vol. 15 no. 1
Type: Research Article
ISSN: 1746-5664

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Article
Publication date: 15 August 2018

Sara Bazhar, Baptiste Ristagno, Julien Fontchastagner, Noureddine Takorabet and Nicolas Labbe

This paper aims to propose a new topology of direct current (DC) machine using claw-pole stator to replace standard DC starter in micro-hybrid vehicles. The main interest…

Abstract

Purpose

This paper aims to propose a new topology of direct current (DC) machine using claw-pole stator to replace standard DC starter in micro-hybrid vehicles. The main interest of such a topology is the reduction of copper volume.

Design/methodology/approach

The design of the claw-pole machine is based on a multi-objective optimization of several topologies, based on a three-dimensional (3D) reluctance network modeling. The 3D finite element (FE) model is used to check the results of the optimization, and a prototype is manufactured and tested with satisfactory results.

Findings

The claw-pole topology with wave-shape windings allows to replace the current DC series classical starter because of to its copper volume saving.

Research limitations/implications

This model is only limited to the optimization of the claw-pole stator for a fixed geometry of the rotor.

Practical implications

The research outcome shows that claw-pole machine can replace the series-excited DC machines of starters and at the same time achieve the same performance at reduced copper volume.

Originality/value

The paper deals with a new DC machine topology to reduce the copper volume through the suppression of the classical stator end-windings. The use of Claw-Pole inductors ensures this copper reduction.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 37 no. 5
Type: Research Article
ISSN: 0332-1649

Keywords

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