A hybrid multicriteria decision-making (MCDM) framework, namely “criteria importance through inter-criteria correlation-combinative distance-based assessment” (CRITIC-CODAS) is introduced to rank automotive brake friction composite materials based on their physical and tribological properties. The ranking analysis was performed on ten brake friction composite material alternatives that contained varying proportions (5% and 10% by weight) of hemp, ramie, pineapple, banana, and Kevlar fibers. The properties of alternatives such as density, porosity, compressibility, friction coefficient, fade-recovery performance, friction fluctuation, cost, and carbon footprint were used as selection criteria. An increase in natural fiber content resulted in a decrease in density, along with an increase in porosity and compressibility. The composite with 5 wt.% Kevlar fiber showed the highest coefficient of friction, while the 5 wt.% ramie fiber-based composites exhibited the lowest levels of fade and friction fluctuations. The wear performance was highest in the composite containing 10 wt.% Kevlar fiber, while the composite with 10 wt.% ramie fiber exhibited the highest recovery. The results indicate that including different fibers in varying amounts can affect the evaluated performance criteria. A hybrid CRITIC-CODAS decision-making technique was used to select the optimal brake friction composite. The findings of this approach revealed that adding 10 wt.% banana fiber to the brake friction composite can give the optimal combination of evaluated properties. A sensitivity analysis was performed on several weight exchange scenarios to see the stability of the ranking results. Using Spearman’s correlation with the ranking outcomes from other MCDM techniques, the suggested decision-making framework was further verified, demonstrating its effectiveness and stability.

Based on the criteria importance through inter-criteria correlation (CRITIC) and the multi-attributive border approximation area comparison (MABAC), a decision-making algorithm was developed to select the optimal biocomposite material according to several conflicting attributes. Poly(lactic acid) (PLA)-based binary biocomposites containing wood waste and ternary biocompos-ites containing wood waste/rice husk with an overall additive content of 0, 2.5, 5, 7.5 and 10 wt.% were manufactured and evaluated for physicomechanical and wear properties. For the algorithm, the following performance attributes were considered through testing: the evaluated physical (density, water absorption), mechanical (tensile, flexural, compressive and impact) and sliding wear proper-ties. The water absorption and strength properties were found to be the highest for unfilled PLA, while modulus performance remained the highest for 10 wt.% rice husk/wood-waste-added PLA biocomposites. The density of PLA biocomposites increased as rice husk increased, while it decreased as wood waste increased. The lowest and highest density values were recorded for 10 wt.% wood waste and rice husk/wood-waste-containing PLA biocomposites, respectively. The lowest wear was exhibited by the 5 wt.% rice husk/wood-waste-loaded PLA biocomposite. The experimental results were composition dependent and devoid of any discernible trend. Consequently, prioritizing the performance of PLA biocomposites to choose the best one among a collection of alternatives became challenging. Therefore, a decision-making algorithm, called CRITIC–MABAC, was used to select the optimal composition. The importance of attributes was determined by assigning weight using the CRITIC method, while the MABAC method was employed to assess the complete ranking of the biocomposites. The results achieved from the hybrid CRITIC–MABAC approach demonstrated that the 7.5 wt.% wood-waste-added PLA biocomposite exhibited the optimal physicomechanical and wear properties.