A three-stage hybrid approach for weight assignment in MADM

Yang, Guo-liang; Yang, Jian‐Bo; Xu, Dong-Ling; Khoveyni, Mohammad

doi:10.1016/j.omega.2016.09.011

Cited by 49 publications

(29 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, to better support decision-makers, a clear method to solve multi-criteria decision-making and identifying the preferable alternative in case of trade-offs would be beneficial (Bond et al 2012;. One approach towards solving multi-criteria decision issues is introducing weights to the criteria in order to be able to come to an aggregated evaluation (Yang et al 2017). As the introduction of weights enables aggregated evaluation of impacts on all three sustainability dimensions, the concept of weak sustainability is assumed.…”

Section: Responsible Editor: Jeroen Guinéementioning

confidence: 99%

“…There are many methods available for eliciting weights (Alfares and Duffuaa 2008), like the data envelopment analysis (Yang et al 2017), the critic method (Diakoulaki et al 1995), Simon's procedure (Figueira and Roy 2002), and, most notably, the analytical hierarchy process (Choo et al 1999;Scholl et al 2005;Alfares and Duffuaa 2008) as well as conjoint analysis (Scholl et al 2005;Alfares and Duffuaa 2008).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Introducing a product sustainability budget at an automotive company—one option to increase the use of LCSA results in decision-making processes

Tarne

Lehmann

Kantner³

et al. 2018

Int J Life Cycle Assess

View full text Add to dashboard Cite

Purpose:The assessment and improvement of sustainability impacts of products is integral to achieve sustainable development. A way to assess the impact of a product over its entire life cycle on all three dimensions of sustainability (environment, economy and society) is Life Cycle Sustainability Assessment (LCSA). LCSA suggests to carry out three complementary assessments: Life Cycle Assessment (LCA), Life Cycle Costing (LCC) and Social Life Cycle Assessment (S-LCA). While LCA is widely applied across various industries, the use of LCSA at industrial companies is still scarce. This is, inter alia, due to the challenges in data acquisition in S-LCA and missing support in decision making based on LCSA results as well as their implementation in the decision processes at companies. This thesis aims at remediating these shortcomings to achieve its overarching goal: Increasing the applicability of LCSA to identify and implement product sustainability improvement potential. For this, three research objectives were defined to address the main impediments. First, an approach to select social indicators and to focus primary data collection in S-LCA. Second, a method to interpret LCSA results for decision makers. Third, a mechanism to integrate LCSA based results into product-related decision making at an automotive company. Methods:To put the LCSA framework into practice at an automotive company, it was applied to the component level of a vehicle to assess the impact of every component over its life cycle. In the proposed framework, criticality scores for every LCSA dimension were defined. They were calculated based on the relative performance of a component in the respective LCSA dimension (LCA, LCC and S-LCA) to the rest of the components of the assessed vehicle. This conceptual approach formed the bearing for the integration of the individual research objectives. The three research objectives were addressed in individual publications. The results of these separate papers were combined to increase the applicability of the conceptual approach of the LCSA framework. Multi-Regional Input-Output (MRIO) databases were assessed regarding their possible use to address the first research objective.The findings were applied to enable an assessment of the S-LCA dimension in the LCSA framework.A study on the weighting of sustainability dimensions by decision makers in an automotive company was carried out to tackle the second research objective. The results were used to determine an overall LCSA impact result for every component of a vehicle. To address the third research objective, the customer added value of product sustainability features was investigated. The results were used to create a mechanism that enabled the integration of the assessment results of the LCSA framework alongside other indicators in product-related decision processes at an automotive company. Results:Supply chain analysis based on MRIO analysis did not offer substantial support for focusing primary data collection in S-LCA. Therefore, a material-based appr...

show abstract

Section: Responsible Editor: Jeroen Guinéementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Introducing a product sustainability budget at an automotive company—one option to increase the use of LCSA results in decision-making processes

Tarne

Lehmann

Kantner³

et al. 2018

Int J Life Cycle Assess

View full text Add to dashboard Cite

show abstract

“…There is no doubt that the assessment results of all ranking techniques are affected by uncertain attribute weights, and an appropriate weight assignment is a key step in the ER algorithm to solve UMADM problems. In the literature, three categories of methods integrating ER have been used to obtain attribute weights: subjective, objective, and hybrid methods (Yang, Yang, Xu, & Khovenyi, ). Subjective methods rely entirely on the subjective preference of decision makers (DMs).…”

Section: Introductionmentioning

confidence: 99%

A stochastic multicriteria acceptability analysis–evidential reasoning method for uncertain multiattribute decision‐making problems

et al. 2019

View full text Add to dashboard Cite

Evidential reasoning (ER) is an effective approach for assessing alternatives with uncertain attribute values in the context of decision making. For the ER approach to be able to handle variations in the weights of uncertain attributes in an appropriate manner, this paper proposes a method to solve problems of uncertain multiattribute decision making that involve both uncertain attribute values and uncertain attribute weights, which this method does by combining the ER approach and stochastic multicriteria acceptability analysis‐2 (SMAA‐2). First, the uncertainty in attribute values is described by using a belief decision matrix as in the ER approach. The analytical ER algorithm is then used to create the utility function in the SMAA‐2 model, and that function is used to calculate the probability of different sorting positions of the decision units under weight‐related restrictions. Finally, the results of ranking are obtained by combining the sorting weights. An example is provided to verify the effectiveness of the proposed method.

show abstract

“…Considering these advantages of this method, therefore, the weight optimization method of railway transportation safety performance measurement based on maximizing variance is proposed in this research. Firstly, based on the initial weights given by experts, the weight change interval is calculated by the method of expert group decision-making [25,26] based on fuzzy theory [27,28]. Thereafter, constrained by the fuzzy weighted interval and the principle of weight normalization, the optimal weight of each index of the railway transportation safety performance measurement is obtained by solving the quadratic programming model using the iterative optimization methods.…”

Section: Introductionmentioning

confidence: 99%

A Variance Maximization Based Weight Optimization Method for Railway Transportation Safety Performance Measurement

et al. 2018

View full text Add to dashboard Cite

Based on the idea of maximizing variances, a weight optimization method is proposed in this research to improve railway transportation safety evaluation. Firstly, the main evaluation indicators that can reflect the safety of railway transportation are selected as the independent variables. Secondly, in order to avoid the influence of experts' empowerment on the evaluation results of railway transport safety, fuzzy set theory is introduced to generate the variation range of the weights of each evaluation index, which is used as the constraint of weight optimization model. Then, the weight optimization model for railway transportation safety performance measurement is established based on the principle of maximum variance. The structure of the optimization model shows the characteristics of the quadratic programming model. Therefore, the optimal weight is calculated by using the branch bounded algorithm, which is one of the quadratic programming model solution algorithms. Finally, the empirical analysis of the safety performance measurement for 18 railway bureaus shows that using the optimized index weight for safety performance measurement can not only make full use of prior information but also ensure that 18 railway bureaus can be distinguished to the maximum extent.

show abstract

A three-stage hybrid approach for weight assignment in MADM

Cited by 49 publications

References 42 publications

Introducing a product sustainability budget at an automotive company—one option to increase the use of LCSA results in decision-making processes

Introducing a product sustainability budget at an automotive company—one option to increase the use of LCSA results in decision-making processes

A stochastic multicriteria acceptability analysis–evidential reasoning method for uncertain multiattribute decision‐making problems

A Variance Maximization Based Weight Optimization Method for Railway Transportation Safety Performance Measurement

Contact Info

Product

Resources

About