Distinguished Representation of Criteria Based on Trapezoidal Fuzzy Numbers and Fuzzy Memberships in Selection of Contractors

More attention has been drawn to the selection of contractors in recent years,but little research has been conducted on distinguishing representation of performance values for qualitative and quantitative criteria in one decision-making(DM) problem yet.Thus a novel method based on fuzzy number is proposed to solve the representation issue,in which linguistic terms and fuzzy memberships are used to separately describe qualitative and quantitative criteria,and then the linguistic terms are translated into fuzzy numbers which will be defuzzified and unified with fuzzy memberships.The novel method is used to develop a synthetic decision model for selection of contractors.The model is developed to verify the feasibility of the novel method,which(1) builds a twolevel criteria system based on survey and literature summary;(2)utilizes the analytic network process(ANP) to determine criteria weights;(3) uses the novel method to make different representations of performance values for qualitative and quantitative criteria in one DM problem and then unifies different representations for decision matrices;(4) utilizes three multi-criteria decision-making(MCDM) methods containing simple additive weighting(SAW),technique for order preference by similarity to ideal solution(TOPSIS) and complex proportional assessment(COPRAS) to deal with decision matrices.Besides,a case of contractor selection is given to check the novel method by applying developed model,and the case results show that the novel method can suitably solve the representation issue of the performance values for two types of criteria as well as simplify the processes of TOPSIS and COPRAS.

Application of sensor data based predictive maintenance and artificial neural networks to enable Industry 4.0

Possessing an efficient production line relies heavily on the availability of the production equipment.Thus,to ensure that the required function for critical equipment is in compliance,and unplanned downtime is minimized,succeeding with the field of maintenance is essential for industrialists.With the emergence of advanced manufacturing processes,incorporating predictive maintenance capabilities is seen as a necessity.Another field of interest is how modern value chains can support the maintenance function in a company.Accessibility to data from processes,equipment and products have increased significantly with the introduction of sensors and Industry 4.0 technologies.However,how to gather and utilize these data for enabling improved decision making within maintenance and value chain is still a challenge.Thus,the aim of this paper is to investigate on how maintenance and value chain data can collectively be used to improve value chain performance through prediction.The research approach includes both theoretical testing and industrial testing.The paper presents a novel concept for a predictive maintenance platform,and an artificial neural network(ANN)model with sensor data input.Further,a case of a company that has chosen to apply the platform,with the implications and determinants of this decision,is also provided.Results show that the platform can be used as an entry-level solution to enable Industry 4.0 and sensor data based predictive maintenance.