A Review of TOPSIS Method for Multi Criteria Optimization in Manufacturing Environment

Divya, Ch.; Raju, L. Suvarna; Singaravel, B.

doi:10.1007/978-3-030-42363-6_84

Cited by 15 publications

(8 citation statements)

References 36 publications

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“…Also, TOPSIS is a well-known MCDM method with better capability to handle qualitative and quantitative inputs in assessing the other options (Mousavi-Nasab and Sotoudeh-Anvari, 2017). It provides the optimal solution, which is nearer to the positive ideal solution and far from the negative ideal solution (Divya et al. , 2019).…”

Section: Methodsmentioning

confidence: 99%

A STEEP based hybrid multi-criteria decision making model for the evaluation of battery recycling plant location

Puviarasu

Asokan

Sherif

et al. 2023

JAMR

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PurposeIncreased demand for new batteries and strict government protocols have stressed the battery industries to collect and recycle used batteries for economic and environmental benefits. This scenario has forced the battery industries to collect used batteries and establish the formal battery recycling plant (BRP) for effective recycling. The starting of BRP includes several strategic decisions, one of the most critical decisions encountered is to find the best sustainable location for BRP. Hence, this paper aims to address the complexity of the issues faced during the BRP location selection through a hybrid framework.Design/methodology/approachIn this study, the criteria are identified under socio-cultural, technical, environmental, economic and policy and legal (STEEP) dimensions through literature review and experts' opinions. Then, the hybrid methodology integrating fuzzy decision making trial and evaluation laboratory (DEMATEL), best worst method (BWM) and technique for order preference by similarity to an ideal solution (TOPSIS) has been proposed to find the inter-relationship between criteria, the weights of criteria and the best alternative.FindingsThe identified five main criteria and 26 sub-criteria have been analyzed through fuzzy DEMATEL, and found that the policy and legal criteria have more inter-relationship with other criteria. Then from BWM results, it is found that the support from government bodies has attained the maximum weightage. Finally, the second alternative has been identified as a more suitable location for establishing BRP using TOPSIS. Further, it is found from the results that the support from government bodies, the impact of emissions, availability of basic facilities and community health are the essential criteria under STEEP dimensions for establishing BRP.Originality/valueIn addition to the various existing sustainable criteria, this study has also considered a set of policy and legal criteria for the evaluation of locations for BRP. Further, the hybrid MCDM method has been proposed in this study for selecting the best alternative. Thus, this study has yielded more insights to the decision-makers in choosing a sustainable location for BRP.

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Section: Methodsmentioning

confidence: 99%

A STEEP based hybrid multi-criteria decision making model for the evaluation of battery recycling plant location

Puviarasu

Asokan

Sherif

et al. 2023

JAMR

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“…This method has been applied in various domain applications in the literature. Due to its prominence, multiple reviews on the applications of the original TOPSIS formulation (i.e., excluding its extensions) have been reported by Behzadian et al [56] , Shukla et al [57] , Yadav et al [58] , and Divya et al [59] .…”

Section: Preliminariesmentioning

confidence: 99%

Classifying the degree of exposure of customers to COVID-19 in the restaurant industry: A novel intuitionistic fuzzy set extension of the TOPSIS-Sort

Ocampo

Tanaid

Tiu

et al. 2021

Applied Soft Computing

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Despite the rigid public safety protocols of the restaurant sector amid the COVID-19 pandemic in an effort to restart economic activities, customers do not feel secure eating at a sit-in restaurant, which is associated with prolonged restrictions on movement. As a mitigating initiative, holistically evaluating customers’ perceived degree of exposure to COVID-19 in restaurants is deemed relevant in the design of mitigation measures. Such an agenda is associated with multiple attributes under decision-making uncertainty within the framework of multiple criteria sorting (MCS). Thus, this work addresses this problem domain by proposing an intuitionistic fuzzy set extension of the previously developed TOPSIS-Sort (i.e., IF TOPSIS-Sort). As a case demonstration, 40 restaurants are evaluated under six attributes that define exposure to COVID-19. With 250 survey participants, the IF TOPSIS-Sort assigns 10, 13, and 17 restaurants to low, moderate, and high exposure classes, respectively. With this classification, crucial insights are offered to the restaurant industry for planning and policy formulation. To determine its effectiveness, a comparative analysis was carried with other distance-based MCS methods. Findings reveal that the proposed method is pessimistic and that other methods tend to underestimate the assignments, which may be counterintuitive, especially in applications related to public health. These sorting differences may be associated with addressing the vagueness and uncertainty in decision-making within the IF TOPSIS-Sort platform. The proposed novel IF TOPSIS-Sort is sufficiently generic for other domain sorting applications and contributes to the MCS literature.

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“…The optimization problem in this study is solved by utilizing the (MOGA) multi-objective genetic algorithm. MOGA is one of the most commonly used optimization techniques in manufacturing applications [40,41]. In this study, MOGA is used to optimize all three objectives (roughness, MRR, and cost), as shown in equation 3.…”

Section: A Optimization Techniquementioning

confidence: 99%

Towards an Adaptive Design of Quality, Productivity and Economic Aspects When Machining AISI 4340 Steel With Wiper Inserts

et al. 2020

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The continuous pursue of sustainable manufacturing is motivating the utilization of new advanced technology, especially for hard to cut materials. In this study, an adaptive approach for optimization of machining process of AISI 4340 using wiper inserts is proposed. This approach is based on advance yet intuitive modeling and optimization techniques. The approach is based on Artificial Neural Network (ANN), Multi-Objective Genetic Algorithm (MOGA), as well as Linear Programming Techniques for Multidimensional Analysis of Preference (LINMAP), for modeling, optimization and multicriteria decision making respectively. This integrated approach, to best of the authors' knowledge, has been deployed for the first time to adaptively serve different designs of manufacturing processes. Such designs have different orientations, namely cost, quality, productivity, and balanced orientation. The capability of the proposed approach to serving such diverse requirements answers one of the most accelerating demands in the manufacturing community due to the dynamics of the uprising smart production lines. Besides, the proposed approach is presented in a straightforward manner that can be extended easily to other design orientations as well as other engineering applications. Based on the proposed design, a balanced general setting of 197.4 m/min, 0.95 mm, and 0.168 mm/rev was recommended along with other settings for more sophisticated requirements. Confirmatory experiments showed a good agreement (i.e., no more than 7% deviation) with the predicted optimum responses. This shows the validity of the proposed approach as a viable tool for designers to promote holistic and sustainable process design. INDEX TERMS Adaptive design, Artificial neural networks, Genetic Algorithm, Modeling, Wiper inserts, Turning, NOMENCLATURE ANN Artificial Neural Network B Balanced design DOC Depth of Cut EC Intensive cost-oriented design EP Intensive productivity-oriented design EQ Intensive quality-oriented design f Feed GRA Gray Relation Analysis IC Intensive cost-oriented design IP Intensive productivity-oriented design IQ Intensive quality-oriented design LINMAP

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A Review of TOPSIS Method for Multi Criteria Optimization in Manufacturing Environment

Cited by 15 publications

References 36 publications

A STEEP based hybrid multi-criteria decision making model for the evaluation of battery recycling plant location

A STEEP based hybrid multi-criteria decision making model for the evaluation of battery recycling plant location

Classifying the degree of exposure of customers to COVID-19 in the restaurant industry: A novel intuitionistic fuzzy set extension of the TOPSIS-Sort

Towards an Adaptive Design of Quality, Productivity and Economic Aspects When Machining AISI 4340 Steel With Wiper Inserts

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