Kevin W. Li

An approach to multiattribute decision making with interval-valued intuitionistic fuzzy assessments and incomplete weights

This article proposes an approach to multiattribute decision making with incomplete attribute weight information where individual assessments are provided as interval-valued intuitionistic fuzzy numbers (IVIFNs). By employing a series of optimization models, the proposed approach derives a linear program for determining attribute weights. The weights are subsequently used to synthesize individual IVIFN assessments into an aggregated IVIFN value for each alternative. In order to rank alternatives based on their aggregated IVIFN values, a novel method is developed for comparing two IVIFNs by introducing two new functions: the membership uncertainty index and the hesitation uncertainty index. An illustrative investment decision problem is employed to demonstrate how to apply the proposed procedure and comparative studies are conducted to show its overall consistency with existing approaches.

Preference uncertainty in the graph model for conflict resolution

A new preference structure is introduced into the graph model for conflict resolution. This structure can handle a decision-makers (DM) strict preference for one state or scenario over another, equal preference for states, and uncertain or unknown preference in the comparison of two states. Built upon this preference structure, four types of solution definitions modeling human behavior under conflict are extended to accommodate uncertainty in preferences. Four distinct ways to consider uncertain preference information are identified, producing sixteen extended stability definitions. Interrelationships of these definitions within and across the four definition sets are investigated. Illustrative examples of two-DM and multi-DM conflict models are presented to show how the new solution concepts can be applied in practice.

A case-based distance model for multiple criteria ABC analysis

In ABC analysis, a well-known inventory planning and control technique, stock-keeping units (SKUs) are sorted into three categories. Traditionally, the sorting is based solely on annual dollar usage. The aim of this paper is to introduce a case-based multiple-criteria ABC analysis that improves on this approach by accounting for additional criteria, such as lead time and criticality of SKUs, thereby providing more managerial flexibility. Using decisions from cases as input, preferences over alternatives are represented intuitively using weighted Euclidean distances which can be easily understood by a decision maker. Then a quadratic optimization program finds optimal classification thresholds. This system of multiple criteria decision aid is demonstrated using an illustrative case study.

Goal programming approaches to deriving interval weights based on interval fuzzy preference relations

This article investigates the consistency of interval fuzzy preference relations based on interval arithmetic, and new definitions are introduced for additive consistent, multiplicative consistent and weakly transitive interval fuzzy preference relations. Transformation functions are put forward to convert normalized interval weights into consistent interval fuzzy preference relations. By analyzing the relationship between interval weights and consistent interval fuzzy preference relations, goal-programming-based models are developed for deriving interval weights from interval fuzzy preference relations for both individual and group decision-making situations. The proposed models are illustrated by a numerical example and an international exchange doctoral student selection problem.

Social responsibility allocation in two-echelon supply chains: Insights from wholesale price contracts

Corporate social responsibility (CSR) is considered in a two-echelon supply chain consisting of an upstream supplier and a downstream firm that are bound by a wholesale price contract. CSR performance (the outcome of CSR conduct) of the whole supply chain is gauged by a global variable and the associated cost of achieving this CSR performance is only incurred by the supplier with an expectation of being shared with the downstream firm via the wholesale price contract. As such, the key issue is to determine who should be allocated as the responsibility holder with the right of offering the contract and how this right should be appropriately restricted. Game-theoretical analyses are carried out on six games, resulting from different interaction schemes between the supplier and the firm, to derive their corresponding equilibriums. Comparative institutional analyses are then conducted to determine the optimal social responsibility allocation based on both economic and CSR performance criteria. Main results are furnished in a series of propositions and their implications to the real-world business practice are discussed. The key findings are threefold: under the current model settings: (1) the optimal allocation scheme is to assign the supplier as the responsibility holder with appropriate restrictions on the corresponding rights to determine the wholesale price; (2) inherent conflict exists between the economic and CSR performance criteria and, hence, the two maxima cannot be achieved simultaneously; and (3) although integrative channel profit is not attainable, the system-wide profit will be improved by implementing optimal social responsibility allocation schemes.

Status quo analysis in the graph model for conflict resolution

The graph model for conflict resolution, an analysis paradigm for strategic conflicts, has mainly relied on stability analysis for its conclusions. This paper proposes algorithms to apply another analysis technique, status quo analysis, to a graph model. Status quo analysis is dynamic and forward-looking, in contrast to Stability Analysis, which is static and contingent. Status quo analysis is carried out by means of a directed graph that tracks moves and countermoves from a status quo state, and a table that records the reachability status of states from the status quo. Different algorithms are proposed to produce status quo diagrams with and without restrictions on moves; more efficient versions of the algorithm for the case of transitive graphs are also put forward. Properties of diagrams generated by different algorithms are investigated. A case study illustrates how status quo analysis can be applied, and how it interacts with other analysis methodologies.

Distance-based consensus models for fuzzy and multiplicative preference relations

This paper proposes a distance-based consensus model for fuzzy preference relations where the weights of fuzzy preference relations are automatically determined. Two indices, an individual to group consensus index (ICI) and a group consensus index (GCI), are introduced. An iterative consensus reaching algorithm is presented and the process terminates until both the ICI and GCI are controlled within predefined thresholds. The model and algorithm are then extended to handle multiplicative preference relations. Finally, two examples are illustrated and comparative analyses demonstrate the effectiveness of the proposed methods.

A mathematical programming approach to multi-attribute decision making with interval-valued intuitionistic fuzzy assessment information

This article proposes an approach to handle multi-attribute decision making (MADM) problems under the interval-valued intuitionistic fuzzy environment, in which both assessments of alternatives on attributes (hereafter, referred to as attribute values) and attribute weights are provided as interval-valued intuitionistic fuzzy numbers (IVIFNs). The notion of relative closeness is extended to interval values to accommodate IVIFN decision data, and fractional programming models are developed based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to determine a relative closeness interval where attribute weights are independently determined for each alternative. By employing a series of optimization models, a quadratic program is established for obtaining a unified attribute weight vector, whereby the individual IVIFN attribute values are aggregated into relative closeness intervals to the ideal solution for final ranking. An illustrative supplier selection problem is employed to demonstrate how to apply the proposed procedure.

Incomplete interval fuzzy preference relations and their applications

This paper investigates incomplete interval fuzzy preference relations. A characterization, which is proposed by Herrera-Viedma et al. (2004), of the additive consistency property of the fuzzy preference relations is extended to a more general case. This property is further generalized to interval fuzzy preference relations (IFPRs) based on additive transitivity. Subsequently, we examine how to characterize IFPR. Using these new characterizations, we propose a method to construct an additive consistent IFPR from a set of n-1 preference data and an estimation algorithm for acceptable incomplete IFPRs with more known elements. Numerical examples are provided to illustrate the effectiveness and practicality of the solution process.

An interval-valued intuitionistic fuzzy multiattribute group decision making framework with incomplete preference over alternatives

This article proposes a framework to handle multiattribute group decision making problems with incomplete pairwise comparison preference over decision alternatives where qualitative and quantitative attribute values are furnished as linguistic variables and crisp numbers, respectively. Attribute assessments are then converted to interval-valued intuitionistic fuzzy numbers (IVIFNs) to characterize fuzziness and uncertainty in the evaluation process. Group consistency and inconsistency indices are introduced for incomplete pairwise comparison preference relations on alternatives provided by the decision-makers (DMs). By minimizing the group inconsistency index under certain constraints, an auxiliary linear programming model is developed to obtain unified attribute weights and an interval-valued intuitionistic fuzzy positive ideal solution (IVIFPIS). Attribute weights are subsequently employed to calculate distances between alternatives and the IVIFPIS for ranking alternatives. An illustrative example is provided to demonstrate the applicability and effectiveness of this method.