Supplier selection using fuzzy TOPSIS multi criteria model for a small scale steel manufacturing unit

Kumar, Sanjay; Kumar, Saurabh; Barman, Asim Gopal

doi:10.1016/j.procs.2018.07.097

Cited by 65 publications

(31 citation statements)

References 22 publications

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“…Azizi et al (2015) proposed Fuzzy TOPSIS to determine an appropriate automotive supplier based on significant criteria and sub-criteria in industry. Kumar et al (2018) used a Fuzzy TOPSIS model for selecting the suitable supplier for the small-scale manufacturing of steel in India based on the criteria of costs and benefits.…”

Section: Supply Chain Master Planningmentioning

confidence: 99%

Fuzzy multi-objective optimization with α-cut analysis for supply chain master planning problem

Sutthibutr¹,

Chiadamrong²

2019

10.5267/j.uscm

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This study considers a supply chain master planning problem in an uncertain environment where operating costs, customer demand, production capacity, manufacturer's acceptable defective rate, and manufacturer's acceptable service level are uncertain. Our supply chain consists of one manufacturer, multiple suppliers, and multiple distribution centers. While one objective is to minimize the total costs of logistics that consists of purchasing cost, production cost, and distribution cost, the other objective is to maximize total value of purchasing. These objectives are in conflict with each other. In this paper, the fuzzy multi-objective linear model is applied with-Cut analysis to achieve the optimal supply chain master planning in an uncertain environment by balancing these two conflicting objectives. The-Cut analysis is introduced to ensure decision-makers that the outcome satisfies their preferences based on a specified minimum allowed satisfaction value ().

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Section: Supply Chain Master Planningmentioning

confidence: 99%

Fuzzy multi-objective optimization with α-cut analysis for supply chain master planning problem

Sutthibutr¹,

Chiadamrong²

2019

10.5267/j.uscm

View full text Add to dashboard Cite

show abstract

“…e process of decision-making needs the previous definition and fulfilment of specific factors, particularly when need to solving the problems in the more complex zones [5] Also, in the science, the theory of MCDM takes a special and important place [6]. e application of multicriteria decision-making (MCDM) techniques assistances the decisionmaker to take into their consideration diverse criteria or goals in order to arrive a compromise between all potentially incompatible parameters [7,8]; consequently, decisionmaker has to take into consideration and evaluate quantitative and qualitative factors [9]. e MCDM approach may consider both subjective and quantitative attributes (and criteria).…”

Section: Introductionmentioning

confidence: 99%

Assessment and Comparison of Various MCDM Approaches in the Selection of Manufacturing Process

Ghaleb

Kaid

Al-Samhan

et al. 2020

Advances in Materials Science and Engineering

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The selection of manufacturing processes for a given application is a complex problem of multicriteria decision-making although there have been several different approaches that can be utilized to select a suitable alternative. However, identifying appropriate multicriteria decision-making approach from the list of available methods for a given application is a difficult task. This work suggests a methodology to assess different selection approaches, which are the technique for order of preference by similarity to ideal solution (TOPSIS), analytic hierarchy process (AHP), and VIKOR: stepwise procedure. This valuation was done depending on the following factors: number of alternative processes and criteria, agility through the process of decision-making, computational complexity, adequacy in supporting a group decision, and addition or removal of a criterion. A case study in this study was presented to analyse the evaluation methodology. The criteria used to evaluate and identify the best manufacturing process were categorized into productivity, accuracy, complexity, flexibility, material utilization, quality, and operation cost. Five manufacturing processes were considered, including gravity die casting, investment casting, pressure die casting, sand casting, and additive manufacturing. The results showed that each approach was suitable for the problems of manufacturing process selection, in particular toward the support of group decision-making and uncertainty modelling. Manufacturing processes were ranked based on their respective weights for AHP, TOPSIS, and VIKOR, and sand casting is the best. In terms of computational complexity, the VIKOR method performed better than TOPSIS and AHP. Moreover, the VIKOR and TOPSIS methods were better convenient to the selection of manufacturing processes for agility during the process of decision-making, the number of alternative processes and criteria, adequacy in supporting a group decision, and addition or removal of a criterion.

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“…Shaw et al [11] proposed an integrated fuzzy-AHP and fuzzy multi-objective linear programming for solving a supplier selection problem taking into account greenhouse gas emission, costs, quality, lead time and demand as criteria for evaluating and ranking of suppliers. Kumar et al [12] used fuzzy TOPSIS method to gain more efficient steal manufacturing throughout the supplier selection for raw materials. Luthra et al [13] employed AHP and VIKOR methods for analyzing and ranking the sustainable suppliers in a supply chain.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Three Fuzzy McDm Methods for Solving the Supplier Selection Problem

et al. 2019

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The evaluation and selection of an optimal, efficient and reliable supplier is becoming more and more important for companies in today’s logistics and supply chain management. Decision-making in the supplier selection domain, as an essential component of the supply chain management, is a complex process since a wide range of diverse criteria, stakeholders and possible solutions are embedded into this process. This paper shows a fuzzy approach in multi – criteria decision-making (MCDM) process. Criteria weights have been determined by fuzzy SWARA (Step-wise Weight Assessment Ratio Analysis) method. Chosen methods, fuzzy TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution), fuzzy WASPAS (Weighted Aggregated Sum Product Assessment) and fuzzy ARAS (Additive Ratio Assessment) have been used for evaluation and selection of suppliers in the case of procurement of THK Linear motion guide components by the group of specialists in the “Lagerton” company in Serbia. Finally, results obtained using different MCDM approaches were compared in order to help managers to identify appropriate method for supplier selection problem solving.

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Supplier selection using fuzzy TOPSIS multi criteria model for a small scale steel manufacturing unit

Cited by 65 publications

References 22 publications

Fuzzy multi-objective optimization with α-cut analysis for supply chain master planning problem

Fuzzy multi-objective optimization with α-cut analysis for supply chain master planning problem

Assessment and Comparison of Various MCDM Approaches in the Selection of Manufacturing Process

Comparison of Three Fuzzy McDm Methods for Solving the Supplier Selection Problem

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