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WASPAS Multi-Criteria Decision-Making Approach for Selecting Oxygen Delignification Additives in the Pulp and Paper Industry
Published in Shwetank Avikal, Amit Raj Singh, Mangey Ram, Sustainability in Industry 4.0, 2021
Kumar Anupam, Pankaj Kumar Goley, Anil Yadav
The WASPAS method is one of the latest MCDM techniques. It was first reported by Zavadskas et al. (2012). This method has emerged as an efficacious decision-making technique in recent years and presents a peculiar consolidation of WSM (weighted sum model) and WPM (weighted product model). The WASPAS method searches for a combined criterion of optimality based on the dual-criteria of optimality wherein the first is derived from WSM while the second is derived from WPM (Chakraborty et al., 2015). WSM and WPM are well-known and well-practised MCDM methods. The accuracy of WASPAS is 1.6 and 1.3 times greater than WSM and WPM, respectively (Kumar et al., 2020). Like other MCDM techniques, the performance of WASPAS is greatly influenced by normalization techniques and criteria-weighting methods. Generally, linear normalization techniques yield better results in the WASPAS method. Different criteria weighting methods such as entropy, SWARA (step-wise weight assessment ratio analysis), AHP, CRITIC (criteria importance through inter criteria correlation), equal weights method, standard deviation method, etc., have been tested for WASPAS. With the progress of research, several versions of WASPAS, such as fuzzy WASPAS, extended WASPAS, rough WASPAS, neutrosophic WASPAS, etc. have evolved. WASPAS finds application in various manufacturing environments (Chakraborty & Zavadskas, 2014).
Introduction to Decision-Making and Optimization Techniques
Published in Anindya Ghosh, Prithwiraj Mal, Abhijit Majumdar, Advanced Optimization and Decision-Making Techniques in Textile Manufacturing, 2019
Anindya Ghosh, Prithwiraj Mal, Abhijit Majumdar
MADM deals with the selection of the best alternative or a set of preferred alternatives under the presence of a finite number of decision criteria and alternatives. Weighted-sum model (WSM), weighted-product model (WPM), AHP model, TOPSIS, ELECTRE model, decision-making trial and evaluation laboratory (DEMATEL) technique, and preference ranking organization method for enrichment of evaluations (PROMETHEE) are some of the widely used methods of MCDM.
Multi-Criteria Decision-Making Applications in Conventional and Unconventional Machining Techniques
Published in Catalin I. Pruncu, Jamal Zbitou, Advanced Manufacturing Methods, 2023
Şenol Bayraktar, Erhan Şentürk
WASPAS is a multi-response appropriate decision-making method. A mutual optimality criterion is determined based on two optimality criteria in this method. It is used to evaluate a set of alternatives according to a set of decision criteria. It is quite practical and utilizes heavily on the concept of ranking accuracy. It is preferred in multiple response systems in terms of different engineering fields to find the optimum parametric setting for combined output responses [34]. The WASPAS technique is a unique combination of two commonly used MCDM techniques. In other words, WASPAS, which combines the Weighted Sum Model (WSM) and the Weighted Product Model (WPM), increases the ranking accuracy of the alternatives [35, 36]. xijn×m decision matrix consisted of according to Eq. 3.15 with “n” alternative and “m” criteria in the first stage. xijn×m=x11x12…x1mx21x22…x2m…………xn1xn2…xnm
Method for the development of Software-Defined Manufacturing equipment
Published in International Journal of Production Research, 2023
Adrian Barwasser, Joachim Lentes, Oliver Riedel, Nikolas Zimmermann, Manfred Dangelmaier, Jingyi Zhang
A characteristic of the weighted sum model is that the determination of the utility value is not only based on quantitative but also on qualitative information (Reinicke 2004). In other words, not only numbers such as money and time but also subjective information like explicitly formulated statements can be used as criteria. Therefore, each user may obtain different results from using the weighted sum model due to its subjectivity. It should be critically noted that the weighted sum model does not lead to an unambiguous result but can only support the decision-making process. Given the lack of concrete information about economic prospects in early stages of product development, this can actually be seen as a strength of the sub-method as it will allow users to influence the result based on experience. The comparison of estimations by different users may be useful in removing the influence of personal bias of any individual user.
Sustainability evaluation methods for public transport with a focus on Latin American cities: A literature review
Published in International Journal of Sustainable Transportation, 2022
Alexandra Velasco Arevalo, Regine Gerike
Assessment Indicator Models (AIM): AIM are a set of techniques for combining different indicators to composite single-level indices, multi-level indices or multi-dimensional matrices (Awasthi et al., 2018). Smith (2019) adapted the Public Transit Sustainable Mobility Analysis Tool (PTSMAT) of Miller et al. (2016) into a neutrosophic composite index to deal with partial or incomplete information, and to identify the most sustainable combination of PT systems among light rail transit, rapid rail transit and bus rapid transit in Vancouver. In contrast, Tan and Chen (2011) developed a multi-level index, which includes an accessibility index, a comfort index, a network performance index and a coordination index, to guide PT network planning in a satellite town near Beijing. AIM involve three main processes: Normalization: This is required when indicators with different measurement units need to be transformed into the same units to be comparable. The most common techniques are the z-score, where the indicators are converted to a common scale with a mean of zero and standard deviation of one (El Gibari et al., 2019); minimum – maximum scales where a particular value is transformed between the range established by the max value (leader) and the min value (laggard); and distance to a reference, which measures the position of a given indicator with respect to a reference level (Miller et al., 2016).Weighting: The most common technique is the EWA (equally weighted average) that gives equal weight to all dimensions and indicators (Currie & De Gruyter, 2018; De Gruyter et al., 2016). Another technique is the Budget Allocation Process (BAP), where experts are asked to allocate a “budget” of points to the indicator set based on their experience and subjective judgment of the importance of every variable (Errampalli et al., 2020).Aggregation: There are two main approaches: the additive aggregation and the geometric aggregation. In the first approach, the Weighted Sum Model (WSM) is the most common technique for weighting, especially in single dimensional problems. It is an additive utility model where the total value of each alternative is equal to the sum of the products as a full compensatory function, where badly performing indicators can be compensated with other better performing ones (Triantaphyllou, 2000).