Kajal Chatterjee

Evaluation and selection of medical tourism sites: A rough analytic hierarchy process based multi-attributive border approximation area comparison approach

In this paper, a novel multiple criteria decision making (MCDM) methodology is presented for assessing and prioritizing medical tourism destinations in uncertain environment. A systematic evaluation and assessment method is proposed by integrating rough number based AHP (Analytic Hierarchy Process) and rough number based MABAC (Multi-Attributive Border Approximation area Comparison). Rough number is used to aggregate individual judgments and preferences to deal with vagueness in decision making due to limited data. Rough AHP analyzes the relative importance of criteria based on their preferences given by experts. Rough MABAC evaluates the alternative sites based on the criteria weights. The proposed methodology is explained through a case study considering different cities for healthcare service in India. The validity of the obtained ranking for the given decision making problem is established by testing criteria proposed by Wang and Triantaphyllou (2008) along with further analysis and discussion.

Supplier Selection Using Ranking Interval Type-2 Fuzzy Sets

In face of global competition, supplier management is emerging as crucial issue to any companies striving for business success. This paper develops a framework for selecting suitable outsourced suppliers of upstream supply chain in uncertain environment. Emerging supply risk arising from outsourcing are analyzed to reduce cost and increase the sustainability of supply chain network. The study applies ranking based interval type-2 fuzzy set exploring the risk factors and ranking supplier companies. The performance rating weights of risk criteria in supply chain are evaluated based on decision makers. Finally, an empirical study is conducted for Indian Oil Corporation Limited (IOCL) to demonstrate the applicability of the proposed algorithm to select the suitable crude oil supplier(s).

Strategic Decisions Using Intuitionistic Fuzzy Vikor Method for Information System (IS) Outsourcing

Outsourcing is a common trend in information system field in recent decade. Selection of appropriate outsourcing partners is an important goal for multi-national organizations. This study propose a Hybrid algorithm based on the Intuitionistic fuzzy-VIKOR method to evaluate five potential supplier alternatives using five criteria and four decision makers illustrated by a case study. The advantages of the proposed method are highlighted by comparing the result with IF-SIR and IF-TOPSIS methods. The ranking based result provides a reference that assists organizations to improve the efficiency of IS-outsourcing process.

A Hybrid MCDM Technique for Risk Management in Construction Projects

Multi-stakeholder based construction projects are subject to potential risk factors due to dynamic business environment and stakeholders’ lack of knowledge. When solving project management tasks, it is necessary to quantify the main risk indicators of the projects. Managing these requires suitable risk mitigation strategies to evaluate and analyse their severity. The existence of information asymmetry also causes difficulties with achieving Pareto efficiency. Hence, to ensure balanced satisfaction of all participants, risk evaluation of these projects can be considered as an important part of the multi-criteria decision-making (MCDM) process. In real-life problems, evaluation of project risks is often uncertain and even incomplete, and the prevailing methodologies fail to handle such situations. To address the problem, this paper extends the analytical network process (ANP) methodology in the D numbers domain to handle three types of ambiguous information’s, viz. complete, uncertain, and incomplete, and assesses the weight of risk criteria. The D numbers based approach overcomes the deficiencies of the exclusiveness hypothesis and completeness constraint of Dempster–Shafer (D–S) theory. Here, preference ratings of the decision matrix for each decision-maker are determined using a D numbers extended consistent fuzzy preference relation (D-CFPR). An extended multi-attributive border approximation area comparison (MABAC) method in D numbers is then developed to rank and select the best alternative risk response strategy. Finally, an illustrative example from construction sector is presented to check the feasibility of the proposed approach. For checking the reliability of alternative ranking, a comparative analysis is performed with different MCDM approaches in D numbers domain. Based on different criteria weights, a sensitivity analysis of obtained ranking of the hybrid D-ANP-MABAC model is performed to verify the robustness of the proposed method.

A hybrid MCDM approach for selection of financial institution in supply chain risk management

Efficiency assessment and determining optimal low risk financial institution for monetary aid is among the crucial issues which multinational companies are facing in risk oriented supply chain. Supported by fuzzy-soft tools we present a hybrid model for assessing the uncertain fragile and risky structure of the financial sector which has direct impact on processing stages of supply chain network. By integrating Trapezoidal Interval based Type-2 fuzzy soft sets with Extended Type-2 TOPSIS we propose a first-hand approach to multi-criteria decision making problem. Using linguistic rating system, weights of risk criteria are assessed based on trapezoidal interval type-2 fuzzy soft sets. Through Type-2 TOPSIS, the paper proposes Euclidean distance between ideal solutions and relative degree of closeness as evaluative standard for ranking the financial alternatives. Finally, the largest six commercial financial institution of Indian Banking Sector are examined and validated on basis of five risk financial criterias. The proposed method furnishes the solution of decision problem with less computational effort.

A Network-TOPSIS Based Fuzzy Decision Support System for Supplier Selection in Risky Supply Chain

Supplier selection is a multi-criteria decision making problem involving several conflicting criteria on which decision makers knowledge is not precise. A systematic methodology is proposed by integrating fuzzy extent analysis, fuzzy cognitive map and fuzzy decision map forming a fuzzy decision network with fuzzy-TOPSIS to select appropriate supplier providing flexibility to decision makers. Here linguistic values in triangular fuzzy numbers are preferred to represent relative importance among inter-dependence criteria in preference matrix for local weight as well as steady-state matrix for inner-dependence criteria in fuzzy cognitive map stage. The proposed method makes use of fuzzy decision map for global weights of criteria and fuzzy TOPSIS for assessing the alternatives based on global weight. The steps of the method are described first and a numerical example for supplier selection in textile supply chain is given to illustrate the proposed method.

An Induced Fuzzy Rasch-Vikor Model for Warehouse Location Evaluation under Risky Supply Chain

This paper addresses dynamic risky nature of supply chain in warehouse site evaluation where the target is to design network flow of products so that all customers demands are satisfied at minimum risk in distribution network. Main theme of the paper is to choose the most optimal and low risk warehouse spot from a number of potential alternatives locations. A new Fuzzy- Rasch-VIKOR decision model is provided where uncertain information are exploited getting group of decision makers as multiple experts providing decision through triangular fuzzy numbers. A two-phase algorithmic approach is proposed to deal with the problem. First phase involves identification of warehouse location, evaluation of risk criteria using fuzzy Rasch model to quantify criteria weights under uncertainty and second phase ranks the location alternative by fuzzy- VIKOR method for selecting the optimal low risk site. Finally we demonstrate our decision model with a case study illustrating the application in risky supply-chain.

Supplier selection in Telecom supply chain management: a Fuzzy-Rasch based COPRAS-G method

In the past decade, global competition are forcing firms to increase their level of outsourcing for raw or semi-finished products and building long term relationship with their supply chain partners. The objective is to present a wide-ranging decision making technique for ranking supplier alternatives in view of the effect of selected criteria. A proposed method is developed aiming the usage of Fuzzy-Rasch model applying five point Likert scale for criteria weight and Grey based COmplex PRoportional ASsessment (COPRAS-G) method for evaluating and ranking the potential alternatives, as per criteria. The applicability of the induced methodology for supplier selection problem in all environments is shown through a case study in telecommunication sector. A sensitivity analysis is performed based on changing weight patterns of criteria to show the stability in ranking result of the proposed approach. Further, a comparative analysis between the ranking results of proposed method done with existing grey multi-attribute decision-making methods viz. VIKOR-G, ARAS-G and TOPSIS-G using spearman’s correlation coefficient for checking the reliability of the ranking result.

A multi-criteria decision making for renewable energy selection using Z-numbers in uncertain environment

In recent era of globalization, the world is perceiving an alarming rise in its energy consumption resulting in shortage of fossil fuels in near future. Developing countries like India, with fast growing population and economy, is planning to explore among its existing renewable energy sources to meet the acute shortage of overall domestic energy supply. For balancing diverse ecological, social, technical and economic features, selection among alternative renewable energy must be addressed in a multi-criteria context considering both subjective and objective criteria weights. In the proposed COPRAS-Z methodology, Z-number model fuzzy numbers with reliability degree to represents imprecise judgment of decision makers’ in evaluating the weights of criteria and selection of renewable energy alternatives. The fuzzy numbers are defuzzified and renewable energy alternatives are prioritized as per COmplex PropoRtional ASsessment (COPRAS) decision making method in terms of significance and utility degree. A sensitivity analysis is done to observe the variation in ranking of the criteria, by altering the coefficient of both subjective and objective weight. Also, the proposed methodology is compared with existing multi-criteria decision making (MCDM) methods for checking validity of the obtained ranking result.

Measuring Corporate Social Responsibility - Based on Fuzzy Analytic Networking Process Based Balance Scorecard Model

Over the last few decades, Corporate Social Responsibility (CSR) disclosures become a powerful driver of overall stakeholders’ development while the relationship between CSR and its performance has...