Resilient supplier selection under a fuzzy environment

Haldar, Anupam; Ray, Amitava; Banerjee, Debamalya; Ghosh, Sampad

doi:10.1080/17509653.2013.869040

Cited by 56 publications

(57 citation statements)

References 29 publications

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“…It is true that the existing research have shown promising potential of MCDA methods and fuzzy techniques in supplier selection problems (Gan et al, 2019;Haldar et al, 2012Haldar et al, , 2014Hasan, Shohag, Azeem, Paul, & Management, 2015;Jiang, Faiz, & Hassan, 2018). However, there are limitations of the state-of-the-art literatures: (i) to the best of our knowledge, no existing research extends F-MADM framework in supplier evaluation problems leveraging large number of information (time series and graphical information), (ii) no prior study investigated F-MADM approach for evaluating suppliers' performance from resilience and logistics 4.0 perspective, simultaneously, and (iii) it is not clear how fuzzy based TOPSIS can be extended to process inherent uncertainty in decision relevant information associated with both the quantitative and qualitative attributes.…”

Section: Literature Reviewmentioning

confidence: 99%

Resilient supplier selection in logistics 4.0 with heterogeneous information

Hasan

Jiang

Ullah

et al. 2020

Expert Systems with Applications

100

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Supplier selection problem has gained extensive attention in the prior studies. However, research based on Fuzzy Multi-Attribute Decision Making (F-MADM) approach in ranking resilient suppliers in logistic 4.0 is still in its infancy. Traditional MADM approach fails to address the resilient supplier selection problem in logistic 4.0 primarily because of the large amount of data concerning some attributes that are quantitative, yet difficult to process while making decisions. Besides, some qualitative attributes prevalent in logistic 4.0 entail imprecise perceptual or judgmental decision relevant information, and are substantially different than those considered in traditional suppler selection problems. This study develops a Decision Support System (DSS) that will help the decision maker to incorporate and process such imprecise heterogeneous data in a unified framework to rank a set of resilient suppliers in the logistic 4.0 environment.The proposed framework induces a triangular fuzzy number from large-scale temporal data using probability-possibility consistency principle. Large number of non-temporal data presented graphically are computed by extracting granular information that are imprecise in nature. Fuzzy linguistic variables are used to map the qualitative attributes. Finally, fuzzy based TOPSIS method is adopted to generate the ranking score of alternative suppliers. These ranking scores are used as input in a Multi-Choice Goal Programming (MCGP) model to determine optimal order allocation for respective suppliers. Finally, a sensitivity analysis assesses how the Supplier's Cost versus Resilience Index (SCRI) changes when differential priorities are set for respective cost and resilience attributes.

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Section: Literature Reviewmentioning

confidence: 99%

Resilient supplier selection in logistics 4.0 with heterogeneous information

Hasan

Jiang

Ullah

et al. 2020

Expert Systems with Applications

100

View full text Add to dashboard Cite

show abstract

“…They used five resilience criteria (density, complexity, node criticality, responsiveness and re-engineering) for the supplier selection process. More recently, Haldar, Ray, Banerjee and Ghosh [21] proposed a fuzzy group decision-making approach for resilient supplier selection using triangular and trapezoidal fuzzy numbers. They considered investment, responsiveness, and capacity of holding inventory as resilient criteria.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Thus it crucial to provide a reliable level of resilience to the supply side to protect such shortages especially during HILP events [9].Several studies have been conducted to consider resilience in the supply chain [8,10,11]. The concept of resilience in specific to the supplier selection problem has also been discussed by several authors [12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Most of these studies focused on multiple sourcing and operational performance of suppliers.…”

mentioning

confidence: 99%

Interactive Fuzzy Multi Criteria Decision Making Approach for Supplier Selection and Order Allocation in a Resilient Supply Chain

et al. 2019

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Modern supply chains are vulnerable to high impact, low probability disruption risks. A supply chain usually operates in such a network of entities where the resilience of one supplier is critical to overall supply chain resilience. Therefore, resilient planning is a key strategic requirement in supplier selection decisions for a competitive supply chain. The aim of this research is to develop quantitative resilient criteria for supplier selection and order allocation in a fuzzy environment. To serve the purpose, a possibilistic fuzzy multi-objective approach was proposed and an interactive fuzzy optimization solution methodology was developed. Using the proposed approach, organizations can tradeoff between cost and resilience in supply networks. The approach is illustrated using a supply chain case from a garments manufacturing company.

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“…It is clear that the ranking order for the five alternatives is A 2 A 4 A 3 A 5 A 1 and A 2 is the most suitable supplier. The above supplier selection problem was also solved by the fuzzy VIKOR [18] and the fuzzy TOPSIS [21] methods. The ranking results of the candidate suppliers as derived via the application of these methods and the proposed IFH-VIKOR method are shown in Figure 1.…”

Section: Criteria Decision Makersmentioning

confidence: 99%

An Extended VIKOR Method Using Intuitionistic Fuzzy Sets and Combination Weights for Supplier Selection

et al. 2017

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Supplier selection is a complex multiple criteria decision-making (MCDM) problem, which considers a number of alternative suppliers as well as conflicting and noncommensurable criteria. Considering the fact that it is difficult to precisely determine criteria weights and the ratings of alternatives on each criterion in real-life situations, the VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje) method has been modified for intuitionistic fuzzy data in this study for supplier evaluation and selection. Further, we take into account both subjective and objective weights of criteria in the decision-making process, as most supplier selection approaches consider only subjective criteria weights. Finally, two supplier selection examples are provided to illustrate the proposed intuitionistic fuzzy hybrid VIKOR (IFH-VIKOR) method, and its merits are highlighted by comparing it with other relevant approaches.

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Resilient supplier selection under a fuzzy environment

Cited by 56 publications

References 29 publications

Resilient supplier selection in logistics 4.0 with heterogeneous information

Resilient supplier selection in logistics 4.0 with heterogeneous information

Interactive Fuzzy Multi Criteria Decision Making Approach for Supplier Selection and Order Allocation in a Resilient Supply Chain

An Extended VIKOR Method Using Intuitionistic Fuzzy Sets and Combination Weights for Supplier Selection

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