Huan-Jyh Shyur

An extension of TOPSIS for group decision making

An extension of TOPSIS (technique for order performance by similarity to ideal solution), a multi-attribute decision making (MADM) technique, to a group decision environment is investigated. TOPSIS is a practical and useful technique for ranking and selection of a number of externally determined alternatives through distance measures. To get a broad view of the techniques used, we provide a few options for the operations, such as normalization, distance measures and mean operators, at each of the corresponding steps of TOPSIS. In addition, the preferences of more than one decision maker are internally aggregated into the TOPSIS procedure. Unlike in previous developments, our group preferences are aggregated within the procedure. The proposed model is indeed a unified process and it will be readily applicable to many real-world decision making situations without increasing the computational burden. In the final part, the effects of external aggregation and internal aggregation of group preferences for TOPSIS with different computational combinations are compared using examples. The results have demonstrated our model to be both robust and efficient.

A hybrid MCDM model for strategic vendor selection

Proposed in this study is a hybrid model for supporting the vendor selection process in new task situations. First, the vendor evaluation problem is formulated by the combined use of the multi-criteria decision-making (MCDM) approach and a proposed five-step hybrid process, which incorporates the technique of an analytic network process (ANP). Then the modified TOPSIS (technique for order performance by similarity to idea solution) is adopted to rank competing products in terms of their overall performances. The newly developed ANP will eventually yield the relative weights of the multiple evaluation criteria, which are obtained from the nominal group technique (NGT) with interdependence. An example is solved to illustrate the effectiveness and feasibility of the suggested model. The empirical study has demonstrated how the approach can be used for the strategic vendor selection problem.

COTS Evaluation using modified TOPSIS and ANP

This paper models the COTS evaluation problem as an MCDM problem and proposes a five-phase COTS selection model, combining the technique of ANP (analytic network process) and modified TOPSIS (technique for order performance by similarity to idea solution). This article discusses using the ANP to determine the relative weights of multiple evaluation criteria. The modified TOPSIS approach is used to rank competing products in terms of their overall performance. To illustrate how the approach is used for the COTS evaluation problem, an empirical study of a real case is conducted. The case study demonstrates the effectiveness and feasibility of the proposed evaluation procedure.

A stochastic software reliability model with imperfect-debugging and change-point

In this paper, we consider the software reliability growth model that incorporates with both imperfect debugging and changepoint problem. The proposed model utilizes the failure data collected from software development projects to analyze the software reliability and the remaining errors of a released software program. The maximum likelihood approach is derived to estimate the unknown parameters of the new model. We investigate the new model and demonstrate its applicability in the software reliability engineering field. Our analysis suggests that if a change-point exists in a testing process, it should be considered in creating a software reliability estimation model.

A quantitative model for aviation safety risk assessment

The objective of this research is to develop an analytic method that uses data on both accident and safety indicators to quantify the aviation risk which are caused by human errors. A specified proportional hazard model considering the baseline hazard function as a quadratic spline function has investigated and demonstrated its applicability in aviation risk assessment. The use of the proposed model allows investigation of non-linear effects of aviation safety factors and flexible assessment of aviation risk. A subset of data gathered from the Fight Safety Management Information System (FSMIS) developed by the office of the Taiwan Civil Aeronautics Administration (CAA) was applied to accomplish this study. The results demonstrate that the proposed model is a more promising approach with the potential of becoming very useful in practice and leading to further generalization of aviation risk analysis.

A general model for accelerated life testing with time-dependent covariates

This paper introduces a general or “distribution-free” model to analyze the lifetime of components under accelerated life testing. Unlike the accelerated failure time (AFT) models, the proposed model shares the advantage of being “distribution-free” with the proportional hazard (PH) model and overcomes the deficiency of the PH model not allowing survival curves corresponding to different values of a covariate to cross. In this research, we extend and modify the extended hazard regression (EHR) model using the partial likelihood function to analyze failure data with time-dependent covariates. The new model can be easily adopted to create an accelerated life testing model with different types of stress loading. For example, stress loading in accelerated life testing can be a step function, cyclic, or linear function with time. These types of stress loadings reduce the testing time and increase the number of failures of components under test. The proposed EHR model with time-dependent covariates which incorporates multiple stress loadings requires further verification. Therefore, we conduct an accelerated life test in the laboratory by subjecting components to time-dependent stresses, and we compare the reliability estimation based on the developed model with that obtained from experimental results. The combination of the theoretical development of the accelerated life testing model verified by laboratory experiments offers a unique perspective to reliability model building and verification.

A Semi-Structured Process for ERP Systems Evaluation: Applying Analytic Network Process

This paper illustrates a four-step semi-structured process for ERP system evaluation. We suggest using the Analytic Network Process (ANP) for qualitative reviews of ERP, reviews involving multiple criteria and interdependency of properties. The ANP method is based on the feedback system framework of the well-known Analytic Hierarchy Process. A case study indicates that the evaluated aspects of the method are feasible and the method improves the quality of ERP system selection compared with traditional approaches.

Reliability curve fitting for aging helicopter components

This paper presents a comparison of alternative reliability curve fitting techniques for components of two model types of helicopters. Both mathematical function-based and neural network models were investigated. Preliminary results suggest that the neural network models compare very favorably with standard curve fitting techniques, and may provide better curve fitting for component reliability data from sparse data sets where the hazard rates are either constant or monotonically increasing.

Using neural networks to predict component inspection requirements for aging aircraft

Abstract Currently under development by the Federal Aviation Administration (FAA), the Safety Performance Analysis System (SPAS) will contain indicators of aircraft safety performance that can identify potential problem areas for inspectors. The Service Difficulty Reporting (SDR) system is one data source for SPAS and contains data related to the identification of abnormal, potentially unsafe conditions in aircraft or aircraft components/equipment. A higher expected number of SDRs suggests a greater possibility of a maintenance problem and may be used to alert Aviation Safety Inspectors (ASIs) of the need for preemptive safety or repair actions. The preliminary SDR performance indicator in SPAS is not well defined and is too general to be of practical value. In this study, an artificial neural network model is created to predict the number of SDRs that could be expected by part location using sample data from the SDR database that have been merged with aircraft utilization data. The predictions from the neural network models are then compared with results from multiple regression models. The methodological comparison suggests that artificial neural networks offer a promising technology in predicting component inspection requirements for aging aircraft.

A data mining approach to discovering reliable sequential patterns

Sequential pattern mining is a data mining method for obtaining frequent sequential patterns in a sequential database. Conventional sequence data mining methods could be divided into two categories: Apriori-like methods and pattern growth methods. In a sequential pattern, probability of time between two adjacent events could provide valuable information for decision-makers. As far as we know, there has been no methodology developed to extract this probability in the sequential pattern mining process. We extend the PrefixSpan algorithm and propose a new sequential pattern mining approach: P-PrefixSpan. Besides minimum support-count constraint, this approach imposes minimum time-probability constraint, so that fewer but more reliable patterns will be obtained. P-PrefixSpan is compared with PrefixSpan in terms of number of patterns obtained and execution efficiency. Our experimental results show that P-PrefixSpan is an efficient and scalable method for sequential pattern mining.