International Journal of Information Technology & Decision Making

The analytical network process (ANP) has been widely used to evaluate the suppliers, an important subject in supply chain management. We propose an integrated ANP-based evaluation model, which systematically examines six key decision-making modules. They are: questionnaire design, matrix classification, consistency test, inconsistency identification, uncertain or missing values estimation, and sensitivity analysis of rank reversal. Module 1 involves questionnaire design formats, and Module 2 classifies the collected data. Module 3 introduces the maximum eigenvalue threshold as a critical value for consistency test. The induced bias matrix model (IBMM) is employed in Modules 4 and 5 to identify the inconsistent elements and to estimate the uncertain or missing values. In Module 6, we extend the concept of the IBMM to check for rank reversal when a new alternative or criterion is added that perturbs the evaluation system. The case company in the healthcare device industry under study validates that the proposed method is effective. The integrated model not only improves the questionnaire design and simplifies the ANP consistency tests, but also effectively identifies the inconsistent elements and estimates the missing values. The innovative approach to sensitivity analysis is especially insightful and contributes to the understanding of rank reversal issue.

This paper introduces progressive multi-objective optimization (PMOO), a novel technique to include the decision maker's preferences into the multi-objective optimization process. PMOO integrates a well-known method for multi-criteria decision making (PROMETHEE) into a simple multi-objective metaheuristic by maintaining and updating a small reference archive of nondominated solutions throughout the search. By applying this novel technique to a set of instances of the multi-objective knapsack problem, the superiority of PMOO over the commonly accepted sequential approach of generating a Pareto set approximation first and selecting a single solution afterwards is demonstrated.

This paper empirically investigates a sample of research and development (R&D) projects in Taiwanese high-tech industry with a three-fold purpose. The first objective is to assess the associations among information technology (IT) application, knowledge management (KM), team process (TP), and R&D project performance. The second objective is to determine whether TP may mediate the effect of KM on R&D project performance. The third objective is to examine the moderating role of project characteristics in the relationship between TP and R&D project performance. The structural equation modeling (SEM) approach was used to validate the research model. These analyses suggest that TP may fully mediate the effects of KM on R&D project performance in terms of quality and development success and schedule and cost performance. The results also show that industry sector (IS) and team size (TS) have a moderating effect on the relationship between TP and R&D project performance.

Stochastic multi-criteria acceptability analysis (SMAA-2) and the technique for order preference by similarity to ideal solution (TOPSIS) are methods for evaluating alternatives with multiple criteria. SMAA is a method that is used for solving multi-criteria decision-making problems with uncertain, inaccurate information, and does not require preference information from the decision makers. The TOPSIS method is based on the principle of determining a solution with the shortest distance to the ideal solution and the greatest distance from the negative-ideal solution. This paper proposes a new method, SMAA-TOPSIS, by combining the SMAA and TOPSIS methods. The SMAA-TOPSIS method was executed for two problems: drug benefit-risk analysis and machine gun selection. This paper found that TOPSIS could be used with uncertain and arbitrarily distributed values for weights and criteria measurements by using a combination of SMAA and TOPSIS. Also, we obtained clearer and consistent SMAA outputs.

Interval type-2 fuzzy sets (T2FSs) with interval membership grades are suitable for dealing with imprecision or uncertainties in many real-world problems. In the Interval type-2 fuzzy context, the aim of this paper is to develop an interactive signed distance-based simple additive weighting (SAW) method for solving multiple criteria group decision-making problems with linguistic ratings and incomplete preference information. This paper first formulates a group decision-making problem with uncertain linguistic variables and their transformation to interval type-2 trapezoidal fuzzy numbers. Concerning the relative importance of multiple decision-makers and group consensus of fuzzy opinions, a procedure using hybrid averages is then employed to construct a collective decision matrix. By an appropriate extension of the classical SAW approach, this paper utilizes the concept of signed distances and establishes an integrated programming model to manage multi-criteria group decisions under the incomplete and inconsistent preference structure. Further, an interactive procedure is established for group decision making. Finally, the feasibility and effectiveness of the proposed methods are illustrated by a collaborative decision-making problem of patient-centered care (PCC).

Bank customer churn prediction is one of the key businesses for modern commercial banks. Recently, various methods have been investigated to identify the customers who would leave away. This paper proposed a new framework based on feature clustering and classification technique to help commercial banks make an effective decision on customer churn problem. The proposed method benefits from the result of data explorations, clusters the customer features, and makes a decision with a state-of-the-art classifier. When facing the data with large amount of missing items, it does not directly remove the features by some subjective threshold, but clusters the features through the consideration of the relationship and the missing ratio. Real-world data from a major commercial bank of China verifies the feasibility of our framework in industrial applications.

In this paper, we present a clustering heuristic for solving demand covering models where the objective is to determine locations for servers that optimally cover a given set of demand points. This heuristic is based on the concept of biclusters and processes the set of demand points as well as the set of potential servers and determines biclusters that result in smaller problems. Given a coverage matrix, a bicluster is defined as a sub-matrix spanned by both a subset of rows and a subset of columns, such that rows are the most similar to each other when compared over columns. The algorithm starts by using any biclustering algorithm in order to identify appropriate biclusters of the coverage matrix and then combines selected biclusters to define an aggregate solution to the original problem. The algorithm can be easily adapted to address a whole family of covering problems including set covering, maximal covering and backup covering problems. The proposed algorithm is tested in a series of widely known test datasets for various such problems. The main objective of this paper is to introduce the concept of biclustering as an efficient and effective approach to tackle covering problems and to stimulate further research in this area.

Decision making is conditioned by relevant information. This information very seldom has reliable numerical representation. Usually, decision-relevant information is perception-based. A question arises of how to proceed from perception-based information to a corresponding mathematical formalism. When perception-based information is expressed in natural language, the fuzzy set theory can be used as a corresponding mathematical formalism for decision analysis. However, perception-based decision-relevant information is not always sufficiently clear to be modeled by means of membership functions. In contrast, it remains at a level of some cloud images which are difficult to be caught by words. This imperfect information caught in perceptions cannot be precisiated by numbers or fuzzy sets and is referred to as unprecisiated information. Humans are able to make decisions based on unprecisiated visual perceptions. Modeling of this outstanding capability, even to some limited extent, becomes a difficult yet a highly promising research area. In this study, we use fuzzy geometry and the extended fuzzy logic to cope with uncertain situations coming with unprecisiated information. In this approach, the objects of computation and reasoning are geometric primitives which model human perceptions when the latter cannot be defined in terms of membership functions. For this aim, the fuzzified axioms of the incidence geometry are used. An approach to decision making with outcomes and probabilities described by geometrical primitives is developed. Examples of application of the approach to decision making on a short term investment decision and marketing decision are given. The obtained results prove the validity of the suggested approach.

This paper seeks to develop and test a model to examine the relationships between, technical aspects of IS resources (IS alignment, IS resources technical quality, IS advancement), supply chain process integration, and firm performance. A questionnaire-based survey was conducted to collect data from 227 supply chain, logistics, or procurement/purchasing managers of leading manufacturing and retail organizations. Drawing on resources-based view of the firm, and through extending the concept of process integration in supply network, as well as broadening the scope of role of IS resources in relation to process integration and performance gain from the focal firm to the entire supply chain, we found that supply chain process integration is an important multidimensional intermediate organizational capability through which the value of IS resources for supply chain management can be materialized. This capability serves as a catalyst in transforming the value of technical aspects of IS resources into higher performance gain for a firm. Thus, the importance of formation of all dimensions of this capability across supply network should be realized. Moreover, the result suggests that the technical aspects of IS resources need to be jointly developed by supply partners to effectively form supply chain capabilities.