Xiuwu Liao

A model for selecting an ERP system based on linguistic information processing

An enterprise resource planning (ERP) is an enterprise-wide application software package that integrates all necessary business functions into a single system with a common database. In order to implement an ERP project successfully in an organization, it is necessary to select a suitable ERP system. This paper presents a new model, which is based on linguistic information processing, for dealing with such a problem. In the study, a similarity degree based algorithm is proposed to aggregate the objective information about ERP systems from some external professional organizations, which may be expressed by different linguistic term sets. The consistency and inconsistency indices are defined by considering the subject information obtained from internal interviews with ERP vendors, and then a linear programming model is established for selecting the most suitable ERP system. Finally, a numerical example is given to demonstrate the application of the proposed method.

Decision support for risk analysis on dynamic alliance

Dynamic alliance has been considered a temporary organization of member enterprises formed to pool their core competencies and exploit fast changing market opportunities. Although many problems on dynamic alliance such as partner selection, operation management, information exchanges and their standards, etc., have been investigated, the risk management of dynamic alliance has not received deserved attention until now. For risk evaluation is the most important phase of risk management, so this paper is mainly concerned with the risk evaluation problem. Various kinds of risk factors that affect the operation of alliance are identified, and their levels are measured by three parameters such as risk occurrence likelihood, consequence severity and risk control degree. These parameters are expressed by trapezoidal fuzzy numbers. Furthermore, risk evaluation is regarded as a multiattribute decision-making (MADM) problem with uncertainty. A new evaluation approach based on the framework of the evidential reasoning (ER) is proposed. Finally, a numerical example is given to illustrate the application of this approach in the process of risk evaluation.

Approaches to attribute reductions based on rough set and matrix computation in inconsistent ordered information systems

In order to conduct classification analysis in inconsistent ordered information systems, notions on possible and compatible distribution reductions are proposed in this paper. The judgement theorems and discernibility matrices associated with the two reductions are examined, from which we can obtain an approach to the two reductions in rough set theory. Furthermore, the dominance matrix, possible and compatible decision distribution matrices are also considered for approach to these two forms of reductions in inconsistent ordered information systems. Algorithms of matrix computation for possible and compatible distribution reductions are constructed, by which we can provide another efficient approach to these two forms of distribution reductions. To interpret and help understand the algorithm, an experimental computing program is designed and two cases are employed as case study. Results of the small-scale case are calculated and compared by the discernibility matrix and the matrix computation to verify the new method we study in this paper. The large-scale case are calculated by the experimental computing program and validated by the definition of the reductions.

On single valued neutrosophic relations

Smarandache initiated neutrosophic sets (NSs) which can be used as a mathematical tool for dealing with indeterminate and inconsistent information. In order to apply NSs conveniently, single valued neutrosophic sets (SVNSs) were proposed by Wang et al. In this paper, we propose single valued neutrosophic relations (SVNRs) and study their properties. The notions of anti-reflexive kernel, symmetric kernel, reflexive closure, and symmetric closure of a SVNR are introduced, respectively. Their accurate calculate formulas and some properties are explored. Some examples are also given. Finally, single valued neutrosophic relation mappings and inverse single valued neutrosophic relation mappings are introduced, and some interesting properties are also obtained.

A hybrid model of single valued neutrosophic sets and rough sets: single valued neutrosophic rough set model

Smarandache initiated neutrosophic sets (NSs) as a tool for handling undetermined information. Wang et al. proposed single valued neutrosophic sets (SVNSs) that is an especial NSs and can be used expediently to deal with real-world problems. In this paper, we propose single valued neutrosophic rough sets by combining single valued neutrosophic sets and rough sets. We study the hybrid model by constructive and axiomatic approaches. Firstly, by using the constructive approach, we propose the lower/upper single valued neutrosophic approximation operators and illustrate the connections between special single valued neutrosophic relations (SVNRs) and the lower/upper single valued neutrosophic approximation operators. Then, by using the axiomatic approach, we discuss the operator-oriented axiomatic characterizations of single valued neutrosophic rough sets. We obtain that different axiom sets of the lower/upper single valued neutrosophic set-theoretic operators guarantee the existence of different classes of SVNRs which produce the same operators. Finally, we introduce single valued neutrosophic rough sets on two-universes and an algorithm of decision making based on single valued neutrosophic rough sets on two-universes, and use an illustrative example to demonstrate the application of the proposed model.

A group decision-making approach based on evidential reasoning for multiple criteria sorting problem with uncertainty

A new group decision-making approach is developed to address a multiple criteria sorting problem with uncertainty. The uncertainty in this paper refers to imprecise evaluations of alternatives with respect to the considered criteria. The belief structure and the evidential reasoning approach are employed to represent and aggregate the uncertain evaluations. In our approach, the preference information elicited from a group of decision makers is composed of the assignment examples of some reference alternatives. The disaggregation–aggregation paradigm is utilized to infer compatible preference models from these assignment examples. To help the group reach an agreement on the assignment of alternatives, we propose a consensus-reaching process. In this process, a consensus degree is defined to measure the agreement among the decision makers’ opinions. When the decision makers are not satisfied with the consensus degree, possible solutions are explored to help them adjust assignment examples in order to improve the consensus level. If the consensus degree arrives at a satisfactory level, a linear program is built to determine the collective assignment of alternatives. The application of the proposed approach to a customer satisfaction analysis is presented at the end of the paper.

An interactive sorting approach based on the assignment examples of multiple decision makers with different priorities

Although group decision-making is often adopted by many organizations in today’s highly complicated business environment, the multiple criteria sorting (MCS) problem in the context of group decision-making has not been studied sufficiently. To this end, we propose a new interactive method to assist a group of decision makers (DMs) with different priorities. With the goal of relieving the cognitive effort exerted by DMs, this method uses the assignment examples provided by the DMs to draw the parameters for the group preference model. In the iterative MCS process that we employ, the DMs are supported from two perspectives. When the assignment examples provided by the DMs are inconsistent, a RINCON algorithm is developed to identify all the possible solutions that the DMs can use to resolve the conflicts. When the examples are consistent, the potential and the fittest assignments of each alternative are deduced using linear programming techniques. These are then presented to the DMs to help them provide more information for the decision-making process. Furthermore, the priority of each DM is objectively and subjectively evaluated, and then progressively updated to reflect the decision-making performance of a DM at each iteration. Meanwhile, the priorities are integrated into the linear programming model to deduce the fittest assignment, as well as into the RINCON algorithm. Hence, the assignment examples of the DMs with higher priorities are emphasized in the fittest assignment, and are less likely to be revised for inconsistency. A practical example featuring MBA programs is also presented to demonstrate the proposed method.

A social ties-based approach for group decision-making problems with incomplete additive preference relations

With the rapid growth of Web 2.0 technology, a new paradigm has been developed that allows many users to participate in decision-making processes within online social networks. The social information (i.e., social ties and social influence) of the members that is stored in online social networks provides a new perspective for investigating group decision-making (GDM) problems. In this paper, a new interactive GDM approach, based on online social networks, is proposed to address a ranking problem with incomplete additive preference relations (IAPRs). This approach incorporates the strength of social ties and social influence calculated by social network analysis methods regarding the decision-making process. After decision makers (DMs) provide IAPRs, a searching algorithm is developed to identify the optimal preference information transfer path from DMs to the decision supporters who can provide the corresponding preference information. Next, a linear programming model is constructed to complete the missing preference values of the IAPRs. The main features of the linear programming model include its ability to account for other DMs preference information and to maintain consistency. To help the group reach an agreement on the ranking of alternatives, a consensus reaching process is proposed. The strength of social ties and social influence are used to calculate the acceptable adjustment coefficients for DMs in the feedback mechanism. Finally, an illustrative example and further discussion demonstrate the validity of the proposed approach.

Decision support for preference elicitation in multi-attribute electronic procurement auctions through an agent-based intermediary

Multi-attribute auctions have become increasingly popular in enterprise procurement. In the auctions, the elicitation of the preference of an auctioneer concerning multiple attributes is a central task in determining the winner(s). Considering the difficulty of explicit elicitation, a preference elicitation framework is proposed to assist the auctioneer in inferring his/her underlying preference model(s). The auctioneer is expected to provide the information of attribute weights, the holistic preference relations concerning a set of reference bids and the comparison information of intensities of preferences between some pairs of bids on all attributes and/or a particular attribute. Based on this information, a linear programming model is constructed to infer the preference model(s) of the auctioneer so that the estimations are as consistent as possible with the given preference statements. Furthermore, a method is also given to select a representative preference model from the set of compatible ones. The framework is implemented by an intelligent buyer agent called e-buyer which has five main components, i.e., a semantic analyzer, a preference elicitation module, a bid evaluation module, a model base, and a database. The e-buyer is embedded into an auction intermediary, and the proposed preference elicitation models are stored in the model base. Several graphical user interfaces are also presented to visualize the future trading intermediaries. Finally, a numerical example is given to illustrate the framework and show the effectiveness of the preference elicitation models.

A rough set approach to knowledge discovery in analyzing competitive advantages of firms

Competitive advantage analysis (CAA) is still an important issue of strategic management research. Although many studies are developed on this topic, they remain conceptual and descriptive, and it is difficult to make them operational in practice. Therefore, this article proposes an intelligent decision support approach for solving such a difficulty. The proposed approach integrates soft computing, rough set theory, and group decision making technique. In this study, CAA is considered as a multiple criteria sorting problem with multi-granularity linguistic assessment information. An algorithm based on linguistic computing is first presented to construct the decision table of exemplary decisions, and then the extended rough set theory and dominance functions are taken to induce a set of decision rules that satisfy a minimum support threshold. These rules can explicitly describe the relationship between the competitive advantage positions and the key determinant factors of competitive advantage. Finally, a numerical example is used to illustrate the application of the proposed approach.