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• Piston temperature and peak cylinder pressure are the main parameters considered. • For simulation Ansys forte software is used, and the output is used as input to AHP. • AHP based MCDM design methodology is proposed for selecting Piston material. Piston is one of the critical components of an engine as combustion flame directly hits the piston surface. The aim of this research is to develop a methodology to select the material of piston for a new design of engine by using multi-criteria decision making (MCDM) method to solve the piston material selection problem. The Analytical Hierarchy Process (AHP) is designed to select material of the piston for a particular application. Open source data related to critical parameters of Caterpillar engine (viz. temperature and peak cylinder pressure) are considered, which directly affect the strength of piston material. To accurately predict the values of these parameters Ansys forte FEA simulation tool is used for combustion analysis. Simulation results are used as input to design material selection benchmark using AHP methodology. From the results obtained from Ansys forte simulation used as input to AHP based MCDM design methodology turned out to be fruitful for selecting Piston material for rapid decision making at the designer level. [ABSTRACT FROM AUTHOR]

The objective of this research is to present an approach to guide Greater Mekong Subregion (GMS) countries in order to minimise the total transportation costs with maximum satisfaction in distributing goods between the distribution centre (DC) and the customer, the so-called logistics distribution network. The decision support system (DSS) model is the combination of a number of models beginning with the analytic hierarchy process (AHP) model to achieve priorities from customers and the DC operator's viewpoint on the alternatives (logistics distribution network), followed by the Mixed Integer Linear Programming (MILP) which integrates priorities from both sides to achieve maximum satisfaction. After that the multi-commodity transportation model is employed to calculate the optimum number of products transported to customers, which yields minimum transportation costs. The recommendations, limitations and further research are also presented. Keywords: Decision support model Logistics distribution network

Purpose – Consistent with the requirements of online auditing performance assessments, the purpose of this paper is to propose an influence factors analysis method using analytic hierarchy process (AHP) and grey incidence analysis (GIA) to analyze the importance degree of influence factors on online auditing performance quantitatively. Design/methodology/approach – A grey incidence model is developed to analyze the influence factors of online auditing performance based on the characteristics of online auditing. Then, the AHP is used to compute the weights of each assessment criterion of online auditing, and the performance of online auditing are computed. Finally, representing the performance assessment results computed by AHP and values of each assessment criterion as two sequences, GIA is used to analyze the importance degree of influence factors of online auditing performance quantitatively. Findings – The main, secondary and minor influence factors of performance assessment of the online auditing project are identified. For online auditing projects, costs incurred are not the main influence factors of performance. Online auditing projects with higher benefits, higher quality and better design are the really effective ones. Besides, there is no direct relationship between the value of the weight of each criterion and the value of the degree of grey incidence. Practical implications – The results of this study provide useful decision information to implement online auditing projects. Originality/value – An effective method for analyzing the importance degree of influence factors of online auditing performance quantitatively is provided in this study. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we develop mathematical models for simultaneous consideration of suitability and optimality in asset allocation. We use a hybrid approach that combines behavior survey, cluster analysis, analytical hierarchy process and fuzzy mathematical programming. [Copyright &y& Elsevier]

Abstract: This study uses the variable precision rough set (VPRS) model as a tool to support group decision-making (GDM) in credit risk management. We consider the case that the classification in decision tables consisting of risk exposure (RE) may be partially erroneous, and use a variable precision factor β k to adjust the classification error. In this paper, we firstly combine VPRS and AHP to obtain the weight of condition attribute sets decided by each decision-maker (DM). Then, the integrated risk exposure (IRE) of attributes is obtained based on the three VPRS-based models. Subsequently, a new procedure of obtaining β k -stable intervals for DM k is investigated. To verify the effectiveness of these proposed methods, an illustrative example is presented. The experimental results suggest that the VPRS-based IRE have advantages in recognizing important attributes. [Copyright &y& Elsevier]