A Multicriteria Decision Making Environment for Engineering Design and Production Decision-Making

Mosavi, Amir

doi:10.5120/11807-7457

Cited by 8 publications

(5 citation statements)

References 61 publications

(117 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this framework, [24] has implemented RSO multi-objective optimization software for selecting the sustainable textiles composites materials. Yet, [25] proposed a hybrid Group Multi-criteria Decision Making (GMCDM) model integrating the Rank Order Centroid (ROC) and the PROMETHEE methods to choose the best industrial performance indicators of single braking disc.…”

Section: Mcdm In Selection Sustainable Componentsmentioning

confidence: 99%

Investigation on Multi-Criteria Decision Making Methods Application in Sustainable Product Design

Guebsi

Zouari

2022

Spekta

View full text Add to dashboard Cite

Background: Integrating sustainability development' aspects in the design process is becoming, a growth area in companies. Consequently, sustainable product design has to consider the different aspects of sustainability throughout its life cycle phases in addition of other requirements. This integration is becoming more complicated due the difficulty of managing the constraints and alternatives related to the product and stakeholders needs. This study aims to highlights the most used Multi-Criteria Decision Making (MCDM) tools and methods used in sustainable product design process. Contribution: Product design process involves interesting decisional tasks such as the choice of materials, standard parts, technical solutions. Hence, the contribution of this work is to help designer to adopt relevant MCDM tools and methods that can be integrated to other tools to facilitate and to justify their decisional tasks. Method: Several methods have been affected to solve the problems related to this complexity such as MCDM. A literature review was conducted based on Siencedirect and GoogleScholar articles databases. After filtering more than 200 articles only 62 articles were considered to analyze the correlation between sustainable product design and MCDM. Results: Classified MCDM use according to the type of choices to achieve SPD goals. This paper allowed us to find matches between MCDM methods and SPD problem. The majority of case studies result show that a large portion of sustainable design methods, techniques, and tools are applied to the sustainable product’ along its different life cycle phases Conclusion: It is noticed that the use of MCDM methods are an important outcome in the sustainable product design process and deeply helps designers to make suitable choices. Also, several matches relating MCDM, other methods and sustainable product design sphere are discussed

show abstract

Section: Mcdm In Selection Sustainable Componentsmentioning

confidence: 99%

Investigation on Multi-Criteria Decision Making Methods Application in Sustainable Product Design

Guebsi

Zouari

2022

Spekta

View full text Add to dashboard Cite

show abstract

“… [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44]…”

Section: Uncited Referencesunclassified

Multi-criterion Intelligent Decision Support system for COVID-19

Aggarwal

Goswami

Sachdeva

2021

Applied Soft Computing

View full text Add to dashboard Cite

COVID-19 is a buzz word nowadays. The deadly virus that started in China has spread worldwide. The fundamental principle is “if the disease can travel faster information has to travel even faster”. The sequence of events reveals the upheaval need to strengthen the ability of the early warning system, risk reduction, and management of national and global risks. Digital contact tracing apps like Aarogya setu (India) and Pan- European privacy preserving proximity tracing (German) has somehow helped but they are more effective in the initial stage and less relevant in the community spread phase. Thus, there is a need to devise a Decision Support System (DSS) based on machine learning algorithms. In this paper, we have attempted to propose an Additive Utility Assumption Approach for Criterion Comparison in Multi-criterion Intelligent Decision Support system for COVID-19. The dataset of Covid-19 has been taken from government link for validating the results. In this paper, an additive utility assumption-based approach for multi-criterion decision support system (MCDSS) with an accurate prediction of identified risk factors on certain well-defined input parameters is proposed and validated empirically using the standard SEIR model approach (Susceptible, Exposed, Infected and Recovered).The results includes comparative analysis in tabular form with already existing approaches to illustrate the potential of the proposed approach including the parameters such as Precision, Recall and F-Score. Other advanced parameters such as, MCC (Matthews Correlation Coefficient), ROC (Receiver Operating Characteristics) and PRC (Precision Recall) have also been considered for validation and the graphs are illustrated using Jupyter notebook. The statistical analysis of the most affected top eight states of India is undertaken effectively using then Weka software tool and IBM Cognos software to correctly predict the outbreak of pandemic situation due to Covid-19. Finally, the article has immense potential to contribute to the COVID-19 situation and may prove to be instrumental in propelling the research interest of researchers and providing some useful insights for the current pandemic situation.

show abstract

“…For resource allocation in distributed scheduling, Xu et al [5] present a nondominated sorting genetic algorithm-based multi-objective method (NSGA-II) [10]. They aimed at minimizing the time and cost in load balancing using resources to achieve Pareto optimal front.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Resource Allocation in Cloud Computing

Mousavi¹,

Mosavi²,

Varkonyi-Koczy³

et al. 2017

ACTA POLYTECH HUNG

View full text Add to dashboard Cite

Utilizing dynamic resource allocation for load balancing is considered as an important optimization process in cloud computing. In order to achieve maximum resource efficiency and scalability in a speedy manner this process is concerned with multiple objectives for an effective distribution of loads among virtual machines. In this realm, exploring new algorithms, as well as development of novel algorithms, is highly desired for technological advancement and continued progress in resource allocation application in cloud computing. Accordingly, this paper explores the application of two relatively new optimization algorithms and further proposes a hybrid algorithm for load balancing which can contribute well in maximizing the throughput of the cloud provider's network. The proposed algorithm is a hybrid of teaching-learning-based optimization algorithm (TLBO) and grey wolves optimization algorithm (GW). The hybrid algorithm performs more efficiently than utilizing every single one of these algorithms. Furthermore, it well balances the priorities and effectively considers load balancing based on time, cost, and avoidance of local optimum traps, which consequently leads to minimal amount of waiting time. To evaluate the effectiveness of the proposed algorithm, a comparison with the TLBO and GW algorithms is conducted and the experimental results are presented.

show abstract

A Multicriteria Decision Making Environment for Engineering Design and Production Decision-Making

Cited by 8 publications

References 61 publications

Investigation on Multi-Criteria Decision Making Methods Application in Sustainable Product Design

Investigation on Multi-Criteria Decision Making Methods Application in Sustainable Product Design

Multi-criterion Intelligent Decision Support system for COVID-19

Dynamic Resource Allocation in Cloud Computing

Contact Info

Product

Resources

About