Abstract: Data envelopment analysis (DEA) and multiple objective linear programming (MOLP) are tools that can be used in management control and planning. Whilst these two types of model are similar in structure, DEA is directed to assessing past performances as part of management control function and MOLP to planning future performance targets. This paper is devoted to investigating equivalence models and interactive tradeoff analysis procedures in MOLP, such that DEA-oriented performance assessment and target setting can be integrated in a way that the decision makers? preferences can be taken into account in an interactive fashion. Three equivalence models are investigated between the output-oriented dual DEA model and the minimax reference point formulations, namely the super-ideal point model, the ideal point model and the shortest distance model. These models can be used to support efficiency analysis in the same way as the conventional DEA model does and also support tradeoff analysis for setting target values by individuals or groups. A case study is conducted to illustrate how DEA-oriented efficiency analysis can be conducted using the MOLP methods and how such performance assessment can be integrated into an interactive procedure for setting realistic target values.

Abstract: Failure mode and effects analysis (FMEA) has been extensively used for examining potential failures in products, processes, designs and services. An important issue of FMEA is the determination of risk priorities of the failure modes that have been identified. The traditional FMEA determines the risk priorities of failure modes using the so-called risk priority numbers (RPNs), which require the risk factors like the occurrence (O), severity (S) and detection (D) of each failure mode to be precisely evaluated. This may not be realistic in real applications. In this paper we treat the risk factors O, S and D as fuzzy variables and evaluate them using fuzzy linguistic terms and fuzzy ratings. As a result, fuzzy risk priority numbers (FRPNs) are proposed for prioritization of failure modes. The FRPNs are defined as fuzzy weighted geometric means of the fuzzy ratings for O, S and D, and can be computed using alpha-level sets and linear programming models. For ranking purpose, the FRPNs are defuzzified using centroid defuzzification method, in which a new centroid defuzzification formula based on alpha-level sets is derived. A numerical example is provided to illustrate the potential applications of the proposed fuzzy FMEA and the detailed computational process of the FRPNs.

Abstract: New product development (NPD) is highly risky because of fierce competition, as well as rapid technological and market changes, which results in high rates of NPD project failure. A lot of research has found that the high mortality rate is, to some extent, accountable to the selection of wrong NPD projects. NPD project screening is thus a critical activity adopted in early product development stages to enhance the success rate of NPD projects. The manufacturing companies thus demand more intelligent and advanced tools that can advance their NPD screening decisions. During the NPD process, companies need to develop their new products with better and safer performance, higher quality and reliability, better environmental-friendliness and in shorter time. Such multiple criteria have to be considered and assessed at early product project screening stages, which involves a group of cross-functional experts. Due to a large number of quantitative and qualitative criteria and lack of sufficient and concrete data, it is often the case that group members have to make decisions in uncertain situations. It is therefore a big challenge for product managers and experts to move from experience-based decision making to scientific NPD project screening decision making. This paper proposes a novel methodology by integrating the evidential reasoning (ER) approach and analytic hierarchy process (AHP), which can help manufacturers in handling uncertainties and group-based decisions in the early NPD project screening stage. An ER-AHP based decision support system is then developed. A case study with an electronic consumer product manufacturer is conducted to demonstrate how the developed ER-AHP methodology and decision support system be used to support NPD project screening decisions.

Abstract: In this paper, a preference aggregation method is developed for ranking alternative courses of actions by combining preference rankings of alternatives given on individual criteria or by individual decision makers. In the method, preference rankings are viewed as constraints on alternative utilities, which are normalized, and linear programming models are constructed to estimate utility intervals, which are weighted and averaged to generate an aggregated utility interval. A simple yet pragmatic interval ranking method is used to compare and/or rank alternatives. The final ranking is generated as the most likely ranking with certain degrees of belief. Three numerical examples are examined to illustrate the potential applications of the proposed method. Scope and purpose The aggregation of preference rankings has wide applications in group decision making, social choice, committee election and voting systems. The purpose of this paper is to develop a preference aggregation method through the estimation of utility intervals, in which preference rankings are associated with utility intervals that are estimated using linear programming models and aggregated using the simple additive weighting method.