Comparison of the RAFSI and PIV method in multi-criteria decision making: application to turning processes

Abstract

Multi-criteria decision-making (MCDM) methods are used in many fields so as to rank alternatives and find the best one. However, rank reversal after adding or removing an alternative can occur in using some of the methods. In this study, two methods RAFSI and PIV were compared for application of making multi-criteria decisions. They are known to be capable of avoiding rank reversal problems. Sixteen 9XC steel turning tests were performed for the experiment. Tool holder length, spindle speed, feed rate and depth of cut are parameters that vary in each test. Three criteria for evaluating the turning process consist of MRR, RE and Ra. Four methods including MEREC, ROC, RS and EQUAL were used for determining weights of the criteria. The blend of two multi-criteria decision making methods (RAFSI and PIV) with four weight-determining methods resulted in eight ranking options. This is a new approach of the study. A positive outcome was reached that all eight ranking options identified the same best test. The best experiment must ensure to have maximum MRR and minimum RE and Ra simultaneously. A detailed discussion of the ranking results in each case was also carried out. Finally, the directions and issues that need to be studied further were pointed out in this paper as well.

Disaster readiness assessment model using integrated statistical variance and proximity value index

“…Evolving as one of the recent multi-criteria decision-making tools, this method is gaining more attention, and thus it has been applied in various areas such as manufacturing job sequencing (Ahmad et al, 2021), material selection (Wakeel et al, 2021a;Wakeel et al, 2021b;Ajith et al, 2022;Jahan et al, 2022), manufacturing process selection (Raigar et al, 2020), selecting manufacturing parameter process (Trung, 2021a(Trung, , 2021b, cross counties logistics competitiveness comparison (Biswas and Anand, 2020), manufacturing facility location selection (Ulutas and Karakus, 2021), design combustion cooling system (Seraj et al, 2020), e-learning website selection (Khan et al, 2019), assessment of healthcare supply chain resiliency (Zamiela et al, 2022) and Offshore windfarm site selection (Yu et al, 2022). Several studies have validated the performance of the PVI by comparing it with other multi-criteria decision-making tools (Jahan et al, 2022;Trung et al, 2022). In addition, scholars have integrated the PVI with other multi-criteria decision-making tools (Trung et al, 2022).…”

“…Several studies have validated the performance of the PVI by comparing it with other multi-criteria decision-making tools (Jahan et al, 2022;Trung et al, 2022). In addition, scholars have integrated the PVI with other multi-criteria decision-making tools (Trung et al, 2022).…”

“…, 2022), manufacturing process selection (Raigar et al ., 2020), selecting manufacturing parameter process (Trung, 2021a, 2021b), cross counties logistics competitiveness comparison (Biswas and Anand, 2020), manufacturing facility location selection (Ulutas and Karakus, 2021), design combustion cooling system (Seraj et al ., 2020), e-learning website selection (Khan et al ., 2019), assessment of healthcare supply chain resiliency (Zamiela et al ., 2022) and Offshore windfarm site selection (Yu et al ., 2022). Several studies have validated the performance of the PVI by comparing it with other multi-criteria decision-making tools (Jahan et al ., 2022; Trung et al. , 2022).…”

Expanding the Data Normalization Strategy to the MACONT Method for Multi-Criteria Decision Making

“…These methods were chosen because they do not use (2), ( 3), ( 4), (7), and (8). Furthermore, TOPSIS is considered the most popular MCDM [24,25], and PIV was proven to have a lower reverse phenomenon rate [26,27]. Both TOPSIS and PIV methods are only used (1) for normalizing data.…”

“…Both TOPSIS and PIV methods are only used (1) for normalizing data. Detailed procedures for these methods can be found in [24][25][26][27]. Table IX shows the ranking results for this problem.…”