International Journal of Information and Decision Sciences 

About: International Journal of Information and Decision Sciences is an academic journal published by Inderscience Publishers. The journal publishes majorly in the area(s): Computer science & Multiple-criteria decision analysis. It has an ISSN identifier of 1756-7017. Over the lifetime, 239 publications have been published receiving 1597 citations. The journal is also known as: IJIDS & Information and decision sciences.

Data normalisation techniques in decision making: case study with TOPSIS method

Abstract: Data normalisation is essential for decision-making methods because data has to be numerical and comparable to be aggregated into a single score per alternative. In multi-criteria decision-making (MCDM), normalisation must convert criteria values into a common scale, thus, enabling rating and ranking of alternatives. Therefore, it is a challenge to select a suitable normalisation technique to represent an appropriate mapping from source data to a common scale. There are some attempts in the literature to address the subject of normalisation, but it is still an open question which technique is more appropriate for any MCDM method. Our research contribution is an assessment approach for evaluating normalisation techniques. Here, we focus on six well-known normalisation techniques and on TOPSIS method. The proposed assessment process provides a more robust evaluation and selection of the best normalisation technique for usage in TOPSIS.

Impact of social media on consumer behaviour

Abstract: The goal of this paper is to research empirically the role of social media in consumers' decision-making process for complex purchases - those characterised by significant brand differences, high c...

Decision tree classifier: a detailed survey

Abstract: Decision tree classifier (DTC) is one of the well-known methods for data classification. The most significant feature of DTC is its ability to change the complicated decision making problems into simple processes, thus finding a solution which is understandable and easier to interpret. This paper provides a brief review on various algorithms developed in literature for constructing and representing decision trees, splitting criteria for selecting best attribute and pruning methods. The readers will be able to understand why decision trees are more popular among all other methods of classification, what are their uses, limitations and applications in different diverse areas. They will also come to know about a decision tree induction algorithms, splitting criteria, pruning methods, concepts of ensemble methods, fuzzy decision trees, hybridisation of DTCs, etc. These enhancements are found very helpful in solving complex datasets with less computation in very short time period while achieving high accuracy.

A fully fuzzified data envelopment analysis model

Abstract: In the conventional data envelopment analysis (DEA), all the data assumes the form of crisp numerical values. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Some researchers have proposed various fuzzy methods for dealing with the imprecise and ambiguous data in DEA by constructing linear programming (LP) models with 'partial' fuzzy parameters. The main purpose of this study is to evaluate the performance of a set of decision making units (DMUs) in a fully fuzzified environment. We propose a novel fully fuzzified DEA (FFDEA) model by utilising a fully fuzzified LP (FFLP) model, where all decision parameters and variables are fuzzy numbers. The contribution of this paper is threefold: first, we consider ambiguous, uncertain and imprecise input and output data in DEA; second, we address the gap in the fuzzy DEA literature for solutions to fully fuzzified problems; and third, we present a numerical example to demonstrate the applicability and efficacy of the proposed model.

Supply Chain Risk Management

Abstract: The existing models utilise a mean value approach with deterministic failure cost to determine the optimal number of suppliers in the presence of supplier failure risks. The mean value approach assumes, the firm has a linear utility function with respect to the supply disruptions. For major disruptions that could threaten the survival of the firm, the linearity assumption is questionable. Furthermore, the operating cost of working with the suppliers and the financial loss cause by failure of suppliers are subject to uncertainty. This article utilises the mean-variance approach to determine the optimal set of suppliers in the presence of supplier failure risks. The importance of cash-flow variability in the supplier selection/planning process is considered explicitly. Our methodology allows us to balance the two desirable, but conflicting objectives of cost minimisation and service levels achieved. Furthermore, traditional risk management tools like insurance are incorporate into the optimal suppliers' selection process.