Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

Sen, Dilip Kumar (2017) Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories. PhD thesis.



The present work articulates few case empirical studies on decision making in industrial
context. Development of variety of Decision Support System (DSS) under uncertainty and
vague information is attempted herein. The study emphases on five important decision making
domains where effective decision making may surely enhance overall performance of the
organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier
selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply
chain’s g-resilient index and v) risk assessment in e-commerce exercises.
Firstly, decision support systems in relation to robot selection are conceptualized through
adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey
set theory is also found useful in this regard; and is combined with TODIM approach to
identify the best robot alternative. In this work, an attempt is also made to tackle subjective
(qualitative) and objective (quantitative) evaluation information simultaneously, towards
effective decision making.
Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a
novel decision support framework is proposed to address g-resilient (green and resilient)
supplier selection issues. Green capability of suppliers’ ensures the pollution free operation;
while, resiliency deals with unexpected system disruptions. A comparative analysis of the
results is also carried out by applying well-known decision making approaches like Fuzzy-
In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance-
Based’ model in combination with grey set theory to deal with 3PL provider selection,
considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is
articulated to demonstrate application potential of the proposed model. The results, obtained
thereof, have been compared to that of grey-TOPSIS approach.
Another part of this dissertation is to provide an integrated framework in order to assess gresilient
(ecosilient) performance of the supply chain of a case automotive company. The
overall g-resilient supply chain performance is determined by computing a unique ecosilient
(g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with
Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient
criteria in accordance to their current status of performance.
The study is further extended to analyze, and thereby, to mitigate various risk factors (risk
sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are
recognized and evaluated in a decision making perspective by utilizing the knowledge
acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying
parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying
parameters are assessed in a subjective manner (linguistic human judgment), rather than
exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to
various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk
factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem
context (toward e-commerce success). Risks are now categorized into different levels of
severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic).
Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect
the company’s e-commerce performance are recognized through such categorization. The
overall risk extent is determined by aggregating individual risks (under ‘critical’ level of
severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then
used to obtain structural relationship amongst aforementioned five risk sources. An
appropriate action requirement plan is also suggested, to control and minimize risks associated
with e-commerce exercises.

Item Type:Thesis (PhD)
Uncontrolled Keywords:Decision Support System (DSS); Fuzzy Set Theory (FST); Grey Set Theory; Fuzzy-PROMETHEE; Fuzzy-TODIM; Fuzzy-TOPSIS; Fuzzy-VIKOR; Grey- TODIM; Interpretive Structural Modeling (ISM); Robot Selection; Supplier Selection; 3PL Service Provider; Fuzzy Performance Importance Index (FPII); Degree of Similarity (DOS)
Subjects:Engineering and Technology > Mechanical Engineering > Robotics
Divisions: Engineering and Technology > Department of Mechanical Engineering
ID Code:8656
Deposited By:Mr. Kshirod Das
Deposited On:10 Jul 2017 15:36
Last Modified:26 Nov 2019 16:48
Supervisor(s):Datta , Saurav and Patel, Saroj Kumar

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