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

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- TOPSIS and Fuzzy-VIKOR. 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

Development, test and comparison of two Multiple Criteria Decision Analysis(MCDA) models: A case of healthcare infrastructure location

When planning a new development, location decisions have always been a major issue. This paper examines and compares two modelling methods used to inform a healthcare infrastructure location decision. Two Multiple Criteria Decision Analysis (MCDA) models were developed to support the optimisation of this decision-making process, within a National Health Service (NHS) organisation, in the UK. The proposed model structure is based on seven criteria (environment and safety, size, total cost, accessibility, design, risks and population profile) and 28 sub-criteria. First, Evidential Reasoning (ER) was used to solve the model, then, the processes and results were compared with the Analytical Hierarchy Process (AHP). It was established that using ER or AHP led to the same solutions. However, the scores between the alternatives were significantly different; which impacted the stakeholders‟ decision-making. As the processes differ according to the model selected, ER or AHP, it is relevant to establish the practical and managerial implications for selecting one model or the other and providing evidence of which models best fit this specific environment. To achieve an optimum operational decision it is argued, in this study, that the most transparent and robust framework is achieved by merging ER process with the pair-wise comparison, an element of AHP. This paper makes a defined contribution by developing and examining the use of MCDA models, to rationalise new healthcare infrastructure location, with the proposed model to be used for future decision. Moreover, very few studies comparing different MCDA techniques were found, this study results enable practitioners to consider even further the modelling characteristics to ensure the development of a reliable framework, even if this means applying a hybrid approach

Permutation based decision making under fuzzy environment using Tabu search

One of the techniques, which are used for Multiple Criteria Decision Making (MCDM) is the permutation. In the classical form of permutation, it is assumed that weights and decision matrix components are crisp. However, when group decision making is under consideration and decision makers could not agree on a crisp value for weights and decision matrix components, fuzzy numbers should be used. In this article, the fuzzy permutation technique for MCDM problems has been explained. The main deficiency of permutation is its big computational time, so a Tabu Search (TS) based algorithm has been proposed to reduce the computational time. A numerical example has illustrated the proposed approach clearly. Then, some benchmark instances extracted from literature are solved by proposed TS. The analyses of the results show the proper performance of the proposed method

A Multi-Criteria Neutrosophic Group Decision Making Method Based TOPSIS for Supplier Selection

The process of multi-criteria group decision making (MCGDM) is of determining the best choice among all of the probable alternatives. The problem of supplier selection on which decision maker has usually vague and imprecise knowledge is a typical example ofmulti criteria group decision-making problem. The conventional crisp techniques has notmuch effective for solvingMCDMproblems because of imprecise or fuzziness nature of the linguistic assessments. To find the exact values for MCGDM problems is both difficult and impossible in more cases in real world. So, it is more reasonable to consider the values of alternatives according to the criteria as single valued neutrosophic sets (SVNS). This paper deal with the technique for order preference by similarity to ideal solution (TOPSIS) approach and extend the TOPSIS method to MCGDM problem with single valued neutrosophic information. The value of each alternative and the weight of each criterion are characterized by single valued neutrosophic numbers. Here, the importance of criteria and alternatives is identified by aggregating individual opinions of decision makers (DMs) via single valued neutrosophic weighted averaging (SVNWA) operator. The proposed method is, easy use, precise and practical for solving MCGDM problem with single valued neutrosophic data. Finally, to show the applicability of the developed method, a numerical experiment for supplier choice is given as an application of single valued neutrosophic TOPSIS method at end of this paper

A CLOUD TOPSIS MODEL FOR GREEN SUPPLIER SELECTION

Due to stringent governmental regulations and increasing consciousness of the customers, the present day manufacturing organizations are continuously striving to engage green suppliers in their supply chain management systems. Selection of the most efficient green supplier is now not only dependant on the conventional evaluation criteria but it also includes various other sustainable parameters. This selection process has already been identified as a typical multi-criteria group decision-making task involving subjective judgments of different participating experts. In this paper, a green supplier selection problem for an automobile industry is solved while integrating the Cloud model with the technique for order of preference by similarity to an ideal solution (TOPSIS). The adopted method is capable of dealing with both fuzziness and randomness present in the human cognition process while appraising performance of the alternative green suppliers with respect to various evaluation criteria. This model identifies green supplier S4 as the best choice. The derived ranking results using the adopted model closely match with those obtained from other variants of the TOPSIS method. The Cloud model can efficiently take into account both fuzziness and randomness in a qualitative attribute, and effectively reconstruct the qualitative attribute into the corresponding quantitative score for effective evaluation and appraisal of the considered green suppliers. Comparison of the derived ranking results with other MCDM techniques proves applicability, potentiality and solution accuracy of the Cloud TOPSIS model for the green supplier selection