A Novel Fuzzy Hybrid Ranking Approach for Job Sequencing Problems Aimed at Reducing Elapsed Time

Abstract

Job sequencing problems in uncertain environments, such as manufacturing and logistics, often face imprecise processing times that traditional models cannot adequately handle. This paper introduces a novel hybrid ranking approach for solving job sequencing problems where processing times are represented as  triangular fuzzy numbers (TFNs). By combining elements of Yager's ranking and a new weighted centroid method, the proposed technique defuzzifies TFNs to enable the application of extended Johnson's algorithm for optimal sequencing. The objective is to minimize the total fuzzy elapsed time while calculating machine idle times.