An improved iterative stochastic multiobjective acceptability analysis method for robust alternative selection in new product development Abstract   Alternative selection in new product development (NPD) is a multicriteria decisionmaking (MCDM) problem. It usually starts with incomplete, imprecise or even partially missing information. Currently, most existing methods in dealing with this problem cannot work well if required information is incomplete or missing. It is acknowledged that stochastic multiobjective acceptability analysis (SMAA) can be applied to address MCDM problem with incomplete preference information and uncertain criteria measurements. In SMAA, alternatives are evaluated based on SMAA measurements (acceptability index, central weight vector and confidence factor). The discriminability of SMAA for the optimum alternative heavily depends on differences of SMAA measurements among different alternatives. Usually, a large number of alternatives and high level of uncertainty are involved in alternative selection in NPD. In this situation, the differences among SMAA measurements are not obvious, and therefore SMAA cannot deal with such problem very well. To this end, this paper proposes an improved SMAA method called IterativeSMAA (ISMAA) for alternative selection in NPD. In the ISMAA, an iterative multistep decisionmaking process is suggested to improve differences of SMAA measurements among different alternatives, and thus assist decision makers (DMs) to positively discern from the most preferred alternative. To enhance the decisionmaking efficiency, sensitive criteria are acquired in each iteration by ranking sensitivity analysis. DMs are guided to provide partial preference information and give more accurate criteria measurements for sensitive criteria rather than all criteria. Eventually, to verify the proposed method, a numerical example of the existing literature is solved with the method, and the results are compared. And then, a practical example of a preparation equipment for coal samples is further employed to verify the practicability of the proposed ISMAA.

Using a segmenting description approach in multiple criteria decision aiding Abstract   We propose a new method for analyzing a set of parameters in a multiple criteria ranking method. Unlike the existing techniques, we do not use any optimization method, instead incorporating and extending a Segmenting Description (SD) approach. The algorithm rewrites a set of initial inequalities into an equivalent SD system by means of three fundamental operations in a way that makes it possible to either easily analyze all feasible solutions or identify all sources of inconsistency. While considering a valuebased preference disaggregation method, we demonstrate the usefulness of the introduced method in a multipurpose decision analysis exploiting a system of inequalities that models the Decision Makerxe2x80x99s (DMxe2x80x99s) preferences. We focus on the contexts where informative results can be obtained through the analysis of (in)feasibility of such systems. Specifically, we discuss how SD can be applied for verifying the consistency between the revealed and estimated preferences as well as for identifying the reasons of potential incoherence. Moreover, we employ the method for conducting robustness analysis, i.e., discovering a set of all compatible parameter values and verifying the stability of suggested recommendation in view of multiplicity of feasible solutions. In addition, we make clear its suitability for generating arguments about the validity of outcomes and the role of particular criteria. Overall, the SD approach confirms its usefulness in analyzing the relationships between the DMxe2x80x99s preference information, the compatible parameters of an assumed model, and the valuedriven ranking procedure. We also discuss the favourable characteristics of the proposed method enhancing its suitability for use in Multiple Criteria Decision Aiding. These include possibility of studying any system of inequalities without an objective function, keeping in memory an entire process of transforming a system of inequalities that makes identification of potential incoherence straightforward, and avoiding the need for processing the inequalities contained in the basic system which is subsequently enriched with some hypothesis to be verified. The latter is substantial for saving time when a multitude of hypotheses need to be checked in the same conditions. The applicability and the advantages of using the proposed method in the decision aiding context are clearly exemplified on a numerical study.