Selection of projects for automotive assembly structures using a hybrid method composed of the group-input compatible, best-worst method for criteria weighting and TrBF-TOPSIS

Abstract The number of cloud services has dramatically increased over the past few years. Consequently, finding a service with the most suitable quality of service (QoS) criteria matching the user’s requirements is becoming a challenging task. Although various decision-making methods have been proposed to help users to find their required cloud services, some uncertainties such as dynamic QoS variations hamper the users from employing such methods. Additionally, the current approaches use either static or average QoS values for cloud service selection and do not consider dynamic QoS variations. In this paper, we overcome this drawback by developing a broker-based approach for cloud service selection. In this approach, we use recently monitored QoS values to find a timeslot weighted satisfaction score that represents how well a service satisfies the user’s QoS requirements. The timeslot weighted satisfaction score is then used in Best-Worst Method, which is a multi-criteria decision-making method, to rank the available cloud services. The proposed approach is validated using Amazon’s Elastic Compute Cloud (EC2) cloud services performance data. The results show that the proposed approach leads to the selection of more suitable cloud services and is also efficient in terms of performance compared to the existing analytic hierarchy process-based cloud service selection approaches.

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Data generation of vector maps using a hybrid method of analysis and selection of geodata necessary to optimize the process of spatial planning

The 9th International Conference "Environmental Engineering 2014" ◽

10.3846/enviro.2014.134 ◽

2014 ◽

Author(s):

Dominika Strumiłło-Rembowska ◽

Michał Bednarczyk ◽

Iwona Cieślak

Keyword(s):

Hybrid Method ◽

Spatial Planning ◽

Data Generation ◽

Vector Maps ◽

Selection Of

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Hybrid method for selection of the optimal process of leachate treatment in waste treatment and valorization plants or landfills

Clean Technologies and Environmental Policy ◽

10.1007/s10098-014-0834-4 ◽

2014 ◽

Vol 17 (4) ◽

pp. 873-885 ◽

Cited By ~ 18

Author(s):

Manuel Martin-Utrillas ◽

Manuel Reyes-Medina ◽

Jorge Curiel-Esparza ◽

Julian Canto-Perello

Keyword(s):

Hybrid Method ◽

Waste Treatment ◽

Optimal Process ◽

Leachate Treatment ◽

Selection Of

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Evaluating the Use of Artificial Neural Networks, Graph Theory, and Complexity Theory to Predict Automotive Assembly Defects

Volume 4: 21st Design for Manufacturing and the Life Cycle Conference; 10th International Conference on Micro- and Nanosystems ◽

10.1115/detc2016-59664 ◽

2016 ◽

Cited By ~ 2

Author(s):

Apurva Patel ◽

Patrick Andrews ◽

Joshua D. Summers

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Prediction Models ◽

Assembly Model ◽

Training Set ◽

Automotive Assembly ◽

Artificial Neural ◽

Training Set Selection ◽

Selection Of ◽

Assembly Models

Artificial Neural Networks (ANNs) have been used to predict assembly time and market value from assembly models. This was done by converting the assembly models into bipartite graphs and extracting 29 graph complexity metrics which were used to train the ANN prediction models. This paper presents the use of sub-assembly models instead of the entire assembly model to predict assembly quality defects at an automotive OEM. The size of the training set, order of the bipartite graph, selection of training set, and defect type were experimentally studied. With a training size of 28 parts, an interpolation focused training set selection, and second order graph seeding, over 70% of the predictions were within 100% of the target value. The study shows that with an increase in training size and careful selection of training sets, assembly defects can be predicted reliably from sub-assemblies complexity data.

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Using Best Worst Method for Ranking Criteria for Selection of the Best Transportation Mode During Pandemic

Advances in Intelligent Systems and Computing - Progress in Intelligent Decision Science ◽

10.1007/978-3-030-66501-2_44 ◽

2021 ◽

pp. 560-571

Author(s):

Yagmur Unal ◽

Gül T. Temur

Keyword(s):

Transportation Mode ◽

Ranking Criteria ◽

Best Worst Method ◽

Criteria For Selection ◽

Selection Of

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Selection of sustainable material for the manufacturing of complex automotive products using a new hybrid Goal Programming Model for Best Worst Method–Proximity Indexed Value method

Proceedings of the Institution of Mechanical Engineers Part L Journal of Materials Design and Applications ◽

10.1177/1464420720966347 ◽

2020 ◽

pp. 146442072096634

Author(s):

Saif Wakeel ◽

Sedat Bingol ◽

M. Nasir Bashir ◽

Shafi Ahmad

Keyword(s):

Decision Making ◽

Carbon Fiber ◽

Goal Programming ◽

Programming Model ◽

Multi Criteria Decision Making ◽

Goal Programming Model ◽

Alternative Materials ◽

Carbon Fiber Epoxy ◽

Best Worst Method ◽

Selection Of

Selection of the most suitable sustainable material to fulfill the requirements of a product in a specific application is a complex task. Material selection problems are basically multi-criteria decision making problems as selection of the optimal material is based on the evaluation of conflicting criteria. Considering the limitations such as ranking reversal problem of the various multi-criteria decision making methods available in the literature, a combination of two recently developed techniques, i.e. the Goal Programming Model for Best Worst Method and Proximity Indexed Value method, is employed in the present study. This hybrid method was used for selection of the best possible material for manufacturing of a complex automobile part for which F1 race car as advanced automotive and its gearbox casing as sensitive part was used. Available alternative materials considered in the present study are alloys of aluminum, magnesium, titanium, and carbon fiber/epoxy laminate. Whereas, criteria affecting gearbox casing’s performance are tensile strength/density, cost, stiffness, damping capacity, thermal conductivity, and sustainable criteria, such as CO2 emission and recycling energy. Goal Programming Model for Best Worst Method is used to determine weights of the criteria and Proximity Indexed Value method is employed for final selection of material. Furthermore, ranking of alternatives was also supported by other multi-criteria decision making methods namely, range of value, weighted product model, simple additive weighting, the technique for order of preference by similarity to ideal solution, a combined compromise solution, and the multi-attributive border approximation area comparison. Membership degree method was also employed to obtain the final optimal ranking of alternative materials from individual results of applied multi-criteria decision making methods. Besides, sensitivity analysis is done to validate reliability of the results and to determine the most critical evaluation criterion. The result of this study revealed that carbon fiber/epoxy laminate is the best alternative material.

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