Multi-objective optimization for high recyclability material selection using genetic algorithm

Sakundarini, Novita; Taha, Zahari; Abdul-Rashid, Salwa Hanim; Ghazilla, Raja Ariffin Raja; Gonzales, Julirose

doi:10.1007/s00170-013-4933-x

Cited by 15 publications

(7 citation statements)

References 18 publications

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“…Product recycling potential can be estimated, for some authors (Sakundarini et al, 2013a;Sakundarini et al, 2013b;Umeda et al, 2013), by analyzing the recycling ability of the materials. For Dhouib and Elloumi (2011), another way to evaluate product recycling can be by analyzing the end-of-life destination.…”

Section: Product Recycling and Disassembly Evaluatedmentioning

confidence: 99%

A design tool to diagnose product recyclability during product design phase

Aguiar

Oliveira

Silva

et al. 2017

Journal of Cleaner Production

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Section: Product Recycling and Disassembly Evaluatedmentioning

confidence: 99%

A design tool to diagnose product recyclability during product design phase

Aguiar

Oliveira

Silva

et al. 2017

Journal of Cleaner Production

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“…The use of MCDM goes from autonomous drive [21] to assessment of Mars mission [22]. Part of applications are related to supplier selection and evaluation [23][24][25][26][27][28][29][30][31] and materials selection [32][33][34][35][36][37][38][39][40][41][42][43]. The application of MCDM methods have become easier for users and decision makers by improvement of computer techniques [44].…”

Section: Introductionmentioning

confidence: 99%

A review on multi-criteria decision-making for energy efficiency in automotive engineering

Castro¹,

Parreiras

2020

ACI

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Governments around the world instituted guidelines for calculating energy efficiency of vehicles not only by models, but by the whole universe of new vehicles registered. This paper compiles Multi-criteria decision-making (MCDM) studies related to automotive industry. We applied a Systematic Literature Review on MCDM studies published until 2015 to identify patterns on MCDM applications to design vehicles more fuel efficient in order to achieve full compliance with energy efficiency guidelines (e.g., Inovar-Auto). From 339 papers, 45 papers have been identified as describing some MCDM technique and correlation to automotive industry. We classified the most common MCDM technique and application in the automotive industry. Integrated approaches were more usual than individual ones. Application of fuzzy methods to tackle uncertainties in the data was also observed. Despite the maturity in the use of MCDM in several areas of knowledge, and intensive use in the automotive industry, none of them are directly linked to car design for energy efficiency. Analytic Hierarchy Process was identified as the common technique applied in the automotive industry.

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“…In order to optimise the multiobjective problem in the selection of recyclable materials Sakundarini et al (2013) employed non-dominated sorting GA (NSGA-II). GA was employed for solving a multi-objective problem in order to achieve optimum tolerance synthesis with process and machine selection, implying known mathematical models of this problem (Geetha et al 2013).…”

Section: Multiresponse Optimisation Based On Genetic Algorithmmentioning

confidence: 99%

Review of Multiresponse Process Optimisation Methods

Šibalija

Majstorovic

2015

Advanced Multiresponse Process Optimisation

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The review of methods used for multiresponse process parameter design and similar multiobjective optimisation problems is discussed in this chapter, implying the following classification: (1) conventional methods based on statistical or mathematical techniques: (i) experimental design techniques (response surface methodology, Taguchi's robust parameter design and related approaches), and (ii) iterative mathematical search techniques; (2) non-conventional methods based on artificial intelligence techniques: (i) fuzzy logic, (ii) artificial neural networks, (iii) metaheuristic search techniques (genetic algorithm, simulated annealing, particle swarm optimisation, ant colony optimisation, tabu search, and recent evolutionary algorithms such as artificial bee colony algorithm, biogeography-based optimisation, and teaching-learning-based optimisation), and (iv) expert systems.

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Multi-objective optimization for high recyclability material selection using genetic algorithm

Cited by 15 publications

References 18 publications

A design tool to diagnose product recyclability during product design phase

A design tool to diagnose product recyclability during product design phase

A review on multi-criteria decision-making for energy efficiency in automotive engineering

Review of Multiresponse Process Optimisation Methods

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