International Journal of Information Technology & Decision Making

Supply chain management has become more important as an academic topic due to trends in globalization leading to massive reallocation of production related advantages. Because of the massive amount of data that is generated in the global economy, new tools need to be developed in order to manage and analyze the data, as well as to monitor organizational performance worldwide. This paper proposes a framework of business analytics for supply chain analytics (SCA) as IT-enabled, analytical dynamic capabilities composed of data management capability, analytical supply chain process capability, and supply chain performance management capability. This paper also presents a dynamic-capabilities view of SCA and extensively describes a set of its three capabilities: data management capability, analytical supply chain process capability, and supply chain performance management capability. Next, using the SCM best practice, sales & operations planning (S&OP), the paper demonstrates opportunities to apply SCA in an integrated way. In discussing the implications of the proposed framework, finally, the paper examines several propositions predicting the positive impact of SCA and its individual capability on SCM performance.

In multiobjective linear programming, the weighted-sum functions can be used to characterize the entire set of efficient solutions, but in multiobjective combinatorial-{0,1} problems these functions can only determine a small subset of efficient solutions, called supported efficient solutions. In this paper, we show how the entire set of efficient solutions can be found with the same technique by modifying the original problem. An algorithm is proposed. Some results are presented and the effect of some parameters of the proposed algorithm is illustrated with the multiobjective {0,1}-knapsack problem.

The paper discusses the meaning and nature of urban cultural heritage, and the available methods for its valuation in the perspective of sustainable city development. From this perspective, decision-making problems of renovation often involve a complex decision-making process in which multiple requirements and conditions have to be taken into consideration simultaneously. In project development it is hardly possible to get exhaustive and accurate information. As a result, the situations occur, the consequences of which can be very damaging to the project. Sometimes the loss is related to symbolic values that the public perceive as disregarded by the project, despite the overall improved conditions. This paper presents the multiple criteria assessment of alternatives of the cultural heritage renovation projects in Vilnius city. The model consists of the following elements: determining attributes set affecting built and human environment renovation; information collection and analysis, decision modeling and solution selection. The main purpose of the model is to improve the condition of the built and human environment through efficient decision making in renovation supported by multiple attribute evaluation. Delphi, AHP and ARAS-G methods, considering different environment factors as well as stakeholders' needs, are applied to solve problem.

Flow-based methods based on the outranking relation theory are extensively used in multiple criteria classification problems. Flow-based methods usually employed an overall preference index representing the flow to measure the intensity of preference for one pattern over another pattern. A traditional flow obtained by the pairwise comparison may not be complete since it does not globally consider the differences on each criterion between all the other patterns and the latter. That is, a traditional flow merely locally considers the difference on each criterion between two patterns. In contrast with traditional flows, the relationship-based flow is newly proposed by employing the grey relational analysis to assess the flow from one pattern to another pattern by considering the differences on each criterion between all the other patterns and the latter. A genetic algorithm-based learning algorithm is designed to determine the relative weights of respective criteria to derive the overall relationship index of a pattern. Our method is tested on several real-world data sets. Its performance is comparable to that of other well-known classifiers and flow-based methods.

In this paper we develop a measure for calculating the association coefficient between Atanassov's intuitionistic fuzzy sets (A-IFSs), and show its desirable axiomatic properties. Then we present an algorithm for clustering A-IFSs. The algorithm first utilizes the association coefficient of A-IFSs to construct an association matrix, and then calculates the λ-cutting matrix of the association matrix no matter whether it is an equivalent matrix or not. After that, the λ-cutting matrix is used to cluster A-IFSs (if the λ-cutting matrix is just only a similarity matrix, then we can easily transform it into an equivalent matrix). Three examples are used to show the effectiveness of the association coefficient and the algorithm for clustering A-IFSs. Furthermore, we extend the algorithm to cluster interval-valued intuitionistic fuzzy sets (IVIFSs), and finally, we use another numerical example to illustrate the latter algorithm.

Airline selection is a multi-criteria decision-making (MCDM) problem faced in business travel. The evaluation process primarily involves the evaluation of several complex factors for selecting an airline that will best meet business travel needs. Previous studies have proposed several different methods that a company or organization can use to evaluate or select an airline. In its proposed approach to solving airline selection, this study integrates the analytical hierarchy process (AHP) with a type of preference ordering involving the determination a solution's similarity to an ideal solution (TOPSIS) and multi-segment goal programming (MSGP). A real-life case study on selecting an airline is also presented.

Mobile recommender systems target on recommending the right product or information to the right mobile users at anytime and anywhere. It is well known that the contextual information is often the key for the performances of mobile recommendations. Therefore, in this paper, we provide a focused survey of the recent development of context-aware mobile recommendations. After briefly reviewing the state-of-the-art of recommender systems, we first discuss the general notion of mobile context and how the contextual information is collected. Then, we introduce the existing approaches to exploit contextual information for modeling mobile recommendations. Furthermore, we summarize several existing recommendation tasks in the mobile scenarios, such as the recommendations in the tourism domain. Finally, we discuss some key issues that are still critical in the field of context-aware mobile recommendations, including the privacy problem, the energy efficiency issues, and the design of user interfaces.