Supplier selection is one of the key activities of purchase management in supply chain. Supplier selection is a multifaceted problem relating qualitative and quantitative multi-criteria. This paper deals with a supplier selection problem in an Indian automobile company. The work presents selection of headlamp supplier using integrated fuzzy multi-criteria decision-making approaches: analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS). The selection process starts with identifying the criteria based on literature review and interviewing industry experts. Weights to criteria are assigned using AHP, and suppliers are ranked using AHP and TOPSIS. Consistency tests are carried out to check the quality of expert’s inputs. Also, sensitivity analysis is done to check the robustness of the approach. The results address that fuzzy approaches could be effective and more accurate than the existing approaches for supplier selection problems. © 2016, The Natural Computing Applications Forum.

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Jain V

Sakhuja S

Thoduka N

Aggarwal R.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

&nbsp;

&nbsp;

VIT University

Neural Computing and Applications

Next generation wireless networks will integrate various heterogeneous technologies like WLAN, WiMax and cellular technologies etc., to support multimedia services with higher bandwidth and guaranteed quality of service (QoS). In order to keep the mobile user always connected to the best wireless network in terms of QoS parameters and user preferences, an optimal network selection technique in heterogeneous networks is required. This paper proposes a novel fuzzy-Analytic Hierarchy Process (AHP) based network selection in heterogeneous wireless networks. Triangular fuzzy numbers are used to represent the elements in the comparison matrices for voice, video and best effort applications. Deriving crisp weights from these fuzzy comparison matrices is a challenging task. When extent analysis method is applied, irrational zero weights are obtained for some attributes. Due to this, many important criteria are not considered in the decision making process. To overcome this problem, a new non-linear fuzzy optimization model for deriving crisp weights from fuzzy comparison matrices for network selection is presented. The weights obtained from this model are more consistent than the existing optimization models. Also, parameterized utility functions are used to model the different Quality of Service (QoS) attributes (bandwidth, delay, jitter, bit error rate) and user preferences (cost) for three different types of applications. Finally, scores are calculated exclusively for each network by three MADM (Multiple Attribute Decision Making) methods Simple Additive Weighting (SAW), TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) and MEW (Multiplicative Exponential Weighting). Results show that the MEW method gives more appropriate scores with utility functions than the SAW and TOPSIS methods. © 2017 Elsevier B.V.

Applied Soft Computing

The utility based non-linear fuzzy AHP optimization model for network selection in heterogeneous wireless networks

Now-a-days, social network sites have become quite popular for communication in the society. People have entangled their day-to-day activities around social media platforms. Social Networks have allowed the users to share their opinions on different topics. In social media, sentiment analysis is an important character to determine opinions of users. Moreover, user’s can be ranked to determine their relative influence. This paper proposes a methodology to rank the users involving sentiment related parameters such as likes, comments and corresponding likescount. Analysis of users’ comments is carried-out. Weights are assigned to these parameters and scores are calculated for each user. Users are ranked on the basis of scores obtained and compared with existing technique. In order to verify the effectiveness of proposed methodology, data is extracted from a verified Facebook page ‘Panjab University, Chandigarh’. Mean, standard deviation and variance are computed to capture the usefulness of ranks obtained by the proposed method. Results depict that the proposed methodology is better than existing technique since it incorporates several features indicating positive and negative behavior of users. This technique can be used to determine the highly trusted and the most distrusted users in a social media user’s profile. Users with negative scores can be considered for outlier analysis. The proposed methodology can also be extended to work on other social media platforms. © 2017, Springer Science+Business Media New York.

Multimedia Tools and Applications

The case analysis on sentiment based ranking of nodes in social media space

In sustainable computing, Intelligent Decision Support Systems (IDSS) has been adopted for prediction, optimization and decision making challenges under variable number constraints based on un-structured data. The traditional systems are lack of efficiency, limited computational ability, inadequate and impreciseness nature of handling sustainable problems. Despite, Computational Intelligence (CI) paradigms have used for high computational power of intelligence system to integrate, analyze and share large volume of un-structured data in a real time, using diverse analytical techniques to discover sustainable information suitable for better decision making. In addition, CI has the ability to handle complex data using sophisticated mathematical models, analytical techniques. This chapter provides a brief overview of computational intelligence (CI) paradigms and its noteworthy character in intelligent decision support and analytics of sustainable computing problems. The objective of this chapter is to study and analyze the effect of CI for overall advancement of emerging sustainable computing technologies. © Springer International Publishing AG 2017.

Intelligent Decision Support Systems for Sustainable Computing Studies in Computational Intelligence

Intelligent Decision Support Systems for Sustainable Computing

The offshore/on-site teams’ knowledge transfer effectiveness is one of the key predictors for evaluating the outcome of global software development (GSD) project. This study aims at proposing an assessment framework that can be used to evaluate the GSD teams’ (offshore/on-site) knowledge transfer effectiveness in the following criteria: knowledge, team, technology, and organization factors. For evaluating the teams’ knowledge transfer effectiveness in GSD projects, we have integrated the fuzzy decision making trial and evaluation laboratory (DEMATEL) and fuzzy multi-criteria decision making (FMCDM) approaches. Moreover, the proposed DEMATEL-FMCDM methodology is used to determine priority weights of each evaluation criterion and rate knowledge transfer effectiveness on the GSD project outcome from software service outsourcing perspective into two dimensions: product success, (schedule, cost improvement, etc.) and service quality (personal satisfaction, and successful collaboration). The proposed methodology is used for the prediction of a possible rating mechanism for determining the GSD project outcome through knowledge transfer criterions of GSD teams. It also facilitates the determination of knowledge transfer effectiveness of GSD teams’ towards the success of the GSD project outcome and initiates actions to improve the project outcome in GSD environment. This study demonstrates a survey research method that addresses 35 knowledge transfer factors assessed by 70 software professionals at Inowits software organization in India.

International Journal of Machine Learning and Cybernetics

Integration of fuzzy DEMATEL and FMCDM approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome

The offshore/onsite teams’ knowledge transfer (KT) effectiveness is one of the key determinants for achieving the outcome of global software development (GSD) projects. In this study, the significance of offshore/onsite teams (GSD teams) KT effectiveness in GSD projects is measured through various factors: knowledge, team, technology, and organization factors. Moreover, the assessment framework for the integration of knowledge, team, technology, and organization factors for evaluating KT effectiveness in GSD projects has not been adequately available in the existing literature. For this motivation, the main objective of this study is to propose the assessment framework to evaluate offshore/onsite teams KT effectiveness with reference to GSD project outcome. For evaluating KT effectiveness of GSD teams, we have integrated three Fuzzy Multi-Criteria Decision Making (FMCDM) methodologies: (a) Fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), (b) Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) and (c) Elimination Et Choix Traduisant la REaite (ELECTRE). Further, the hybridization of fuzzy DEMATEL, TOPSIS, and ELECTRE has not available in the existing literature. Based on this research gap, we have integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating KT effectiveness of offshore/onsite teams in the context of GSD project outcome. Subsequently, the applicability and capability of proposed framework has been validated by software experts at Inowits Software Organization in India.

An integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome

The theoretical basis for studying the phenomenon of Global Software Development (GSD) draws upon one of the key research streams, that is, the Organizational Behavior (OB) research. The focus of this study has led to two research problems: partnership quality and service climate aspects are addressed which have given an insight into the OB research on GSD teams. Moreover, this study classifies results from the partnership quality and service climate aspects into one integrated framework, which covers 18 attributes to explore the GSD outcome factors perceived by GSD teams in OB research phenomenon. To evaluate partnership quality and team service climate aspects with reference to the GSD project outcome, we have integrated the fuzzy Decision-Making Trial and Evaluation Laboratory Model (DEMATEL) and the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) approach, which is more appropriate to find the significance of criteria. The empirical application of this hybrid approach for evaluating GSD project outcome factors has been tested in Indian software organizations. Consequently, the results of this study provide a vivid picture and facilitate the organization to reveal the importance of OB research on GSD teams. © 2014, The Natural Computing Applications Forum.

Journal	Data powered by TypesetNeural Computing and Applications
Publisher	Data powered by TypesetSpringer Science and Business Media LLC
ISSN	0941-0643
Open Access	0