An Algorithmic Framework for Multiobjective Optimization

Ganesan, Timothy; Elamvazuthi, Irraivan; Shaari, Ku Zilati Ku; Vasant, Pandian

doi:10.1155/2013/859701

Cited by 11 publications

(3 citation statements)

References 53 publications

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“…However, it can also be perceived that in many cases, only the bi-objective MO problem is measured. This may be due to the fact that MO optimization is an innovative and upcoming field, and only in recent times (with the aid of modern computational power) has this field spread its horizons into real-world industrial applications [216].…”

Section: Multi-objective Optimization For Ammentioning

confidence: 99%

A state-of-the-art review on energy consumption and quality characteristics in metal additive manufacturing processes

Majeed

Ahmed

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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This paper aims to study the energy consumption and quality characteristics of the parts fabricated by additive manufacturing (AM) technologies with a special focus on metal AM processes. AM is a family of manufacturing techniques, which is broadly used to fabricate complex and lightweight structures. The energy savings during AM processes have a significant influence on the AM industry, only if the quality of the fabricated part meets the requirements. The quality is generally represented by the surface and dimensional quality, mechanical properties, relative density, hardness, etc. The energy saving is important for environmentally benign and cleaner production, and improved product quality is useful for its application as a functional part in the aerospace, automobile, and biomedical industries. A comprehensive review of the energy consumption and quality characteristics of AM-fabricated (with special focus on metal AM) parts was carried out. Firstly, the specific energy consumption of various AM techniques has been reviewed to address the importance of energy and cleaner production. Then, the qualifications of products fabricated by different metal AM techniques have been discussed for different materials, such as titanium alloys, steel alloys, nickel alloys, and aluminum alloys. Also, by considering the practical importance of thin-walled structures fabricated by AM, a detailed analysis of their qualification has been presented. Moreover, different optimization techniques have also been reviewed for various AM process parameters and objectives. Overall, this paper provides an overview of AM, including a survey on the energy consumption and quality characteristics with the development of AM technologies for manufacturing of quality products. Finally, several future research directions are suggested, specifically the need for a framework for metal AM processes for the fabrication of quality products with minimum energy consumption.

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Section: Multi-objective Optimization For Ammentioning

confidence: 99%

A state-of-the-art review on energy consumption and quality characteristics in metal additive manufacturing processes

Majeed

Ahmed

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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show abstract

“…Among them are scalarization methods, which include weighted sum approach, goal attainment, and lexicographic method; and non-Pareto methods, which include ɛ-constraint. However, the numerical method requires mathematical equations including defining the iteration [8]. However, the former approach is able to generate one solution at each iteration and they are sensitive to the shape of Pareto curve, although they have fast convergence and high searching efficiency.…”

Section: Overview Of Multiobjective Optimization Approachesmentioning

confidence: 99%

Overview of Multiobjective Optimization Methods in in Silico Metabolic Engineering

Daud¹,

Zakaria²,

Kunayat³

et al. 2018

IJIE

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However, unlike single objective optimization, multiobjective optimization involves optimizing multiple conflicting objectives. As an example, in E.coli, the production of succinate acid is at the highest rate when the growth rate is at 0 and vice versa. Thus, it is necessary Abstract: Multiobjective optimization requires of finding a trade-off between multiple objectives. However, most of the objectives are contradict towards each other, thus makes it difficult for the traditional approaches to find a solution that satisfies all objectives. Fortunately, the problems are able to solve by the aid of Pareto methods. Meanwhile, in in silico Metabolic Engineering, the identification of reaction knockout strategies that produce mutant strains with a permissible growth rate and product rate of desired metabolites is still hindered. Previously, Evolutionary Algorithms (EAs) has been successfully used in determining the reaction knockout strategies. Nevertheless, most methods work by optimizing one objective function, which is growth rate or production rate. Furthermore, in bioprocesses, it involves multiple and conflicting objectives. In this review, we aim to show the different multiobjective evolutionary optimization methods developed for tackling the multiple and conflicting objectives in in silico metabolic engineering, as well as the approaches in multiobjective optimization.

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“…1 to 5 and 17 to 28. Multi-objective optimization problems have been increasingly encountered in different fields, and various techniques have been used to solve them, e.g., [19][20][21][22][23][24]. Here, without loss of generality, we assume that the objective functions (1) to (4) are in the order of importance, and therefore, a lexicographic method is proposed for this multi-objective optimization problem.…”

Section: The First Phasementioning

confidence: 99%

A comprehensive solution for continuous casting production planning and scheduling

Yadollahpour

ArbabShirani

2015

Int J Adv Manuf Technol

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Production planning and scheduling in steelmaking-continuous casting (SCC) process consists of decision making about three important issues: consolidating orders into charges, grouping charges into casts, and scheduling the casts on machines. In contrast to earlier investigations that mostly focus on one of these issues, a comprehensive twophase approach is presented in this paper in order to solve the whole problem. In the first phase, a continuous-time formulation is developed to determine what casts in what sequences should be scheduled on which machines. The solution obtained is then used as an optimal framework to create the final schedule in the second phase where the assignment rules embedded in a heuristic algorithm are used to consolidate orders into charges and group charges into casts. This method has been successfully used for Mobarakeh Steel Company (MSC), the biggest steel enterprise in the Middle East. Computational experiments demonstrate that the proposed method substantially increases the productivity in MSC.

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An Algorithmic Framework for Multiobjective Optimization

Cited by 11 publications

References 53 publications

A state-of-the-art review on energy consumption and quality characteristics in metal additive manufacturing processes

A state-of-the-art review on energy consumption and quality characteristics in metal additive manufacturing processes

Overview of Multiobjective Optimization Methods in in Silico Metabolic Engineering

A comprehensive solution for continuous casting production planning and scheduling

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