BMW-TOPSIS: A generalized TOPSIS model based on three-way decision

The multiobjective stochastic CRITIC–TOPSIS approach for solving the shipboard crane selection problem

COVID-19 is an epidemic threat to human health caused by a novel coronavirus. Different countries are still struggling with whether to extend lockdown policies considering economic recession outcomes or lift the lockdown and recover the fragile economy. This research narrows the knowledge gap by applying a decision-making approach in a fuzzy environment to consider the uncertainties in the supply chain risk assessment of the construction industry during the COVID-19 pandemic. Besides, risk factors (RFs) are identified and interrelationships between RFs are depicted. RFs are categorized using expert panel surveys and literature reviews. The interdependencies between different RFs are evaluated using a combination of interval-valued intuitionistic fuzzy numbers, decision making trial and evaluation laboratory, and the Choquet integral to allow a strong interrelationship between the sum of rows and the sum of columns of RFs in structural correlation analysis. Next, priorities of RFs are obtained by considering interdependencies between different RFs using a combination of the technique for order preference by similarity to the ideal solution (TOPSIS) method with the intuitionistic fuzzy set. An intuitionistic fuzzy weighted average operator is utilized to aggregate individual opinions of decision makers for rating the importance of RFs. Results reported that the scarcity of material and financial resources due to movement restrictions with overall weights of 0.0614 and 0.413 are the most prominent RFs in the supply chain due to the COVID-19 pandemic. The results of this research can assist decision-makers in developing strategic policies to prevail over the risk challenges and subsequently aid experts in redesigning supply chains. IEEE

A Hybrid Approach for the Supply Chain Risk Assessment of the Construction Industry During the COVID-19 Pandemic

The demand for distributed manufacturing systems (DMS) in the manufacturing sector has notably gained vast popularity as a suitable choice to accomplish sustainability benefits. Manufacturing companies are bound to face critical barriers in their pursuit of sustainability goals. However, the extent to which the DMS attributes relate to sustainable performance and impact on critical barriers to sustainability is considerably unknown. To help close this gap, this study proposes a methodology to determine the relative importance of sustainability barriers, the influence of DMS on these barriers, and the relationship between DMS attributes and sustainable performance. Drawing upon a rich data pool from the Chinese manufacturing industry, the Best-Worst Method (BWM) is used to investigate the relative importance of the sustainability barriers and determine how the DMS attributes influence these barriers and relate to sustainability. The study findings show that ‘organizational barriers’ are the most severe barriers and indicate that ‘reduced carbon emissions’ has the highest impact on ‘organizational’ and ‘socio-cultural barriers’ whereas ‘public approval’ has the highest impact on ‘organizational barriers. The results infer that ‘reduction of carbon emission’ is the DMS strategy strongly linked to improved sustainable performance. Hence, the results can offer in-depth insight to decision-makers, practitioners, and regulatory bodies on the criticality of the barriers and the influence of DMS attributes on the sustainability barriers and improve sustainable performance for increased global competitiveness. Moreover, our study offers a solid foundation for further studies on the link between DMS and sustainable performance.

Closing the Gap: The Role of Distributed Manufacturing Systems for Overcoming the Barriers to Manufacturing Sustainability

Previous road transportation sustainability studies not only neglected the epistemic uncertainty that surrounds the impact of innovation and Research and Development (R&D) expenditure on pollutant emissions performance but also failed in designing and simultaneously exploring the strengths of alternative MCDM (Multiple-Criteria Decision-Making) approaches to better discriminate performance scores. This paper focuses on these two gaps by presenting a road transportation sustainability performance assessment of 29 Chinese provinces for a 14-year period in light of relevant socioeconomic and demographic variables. First, a novel TEA-IS model is developed to assess road transportation sustainability performance. Besides possessing the beneficial features of each model, this hybrid DEA-TOPSIS can analyze the sustainability performance from the perspective of the synergistic effects among the criteria. From the socioeconomic and demographic perspectives, we use machine learning techniques for predicting high-low performance and synergistic Chinese provinces. Results suggest that the discriminatory power of TEA-IS is good and there is high synergy in Chinese provinces in terms of sustainable road transportation. We further find that there is a high level of heterogeneity in road transportation sustainability among different geographical locations in China. The results further suggest that higher levels of synergy are strongly associated with medium and high-performing provinces. Finally, we find that innovation index, foreign direct investment, and non-coastal cities have both higher performance and synergy levels. We recommend that Chinese government should further provide favorable policies to enhance innovation and efforts should also be made to provide better environment for attracting more foreign direct investment. Finally, instead of road transportation, other modes of transportation, such as air and sea, are recommended to be further developed and utilized. 

Impact of R&amp;D and innovation in Chinese road transportation sustainability performance: A novel trigonometric envelopment analysis for ideal solutions (TEA-IS)

In this article, we present a method for ranking alternatives in multiple attribute decision making (MADM) and group MADM problems. Our method first determines the importance of separations of each decision from positive ideal solution and negative ideal solution and then computes the relative closeness of each alternative. The alternatives are ranked based on relative closeness. The method is illustrated using three examples and the results are compared with other methods.

Ranking the Alternatives With a Modified TOPSIS Method in Multiple Attribute Decision Making Problems

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Ranking the Alternatives With a Modified TOPSIS Method in Multiple Attribute Decision Making Problems