A modified Genetic Algorithm approach to minimize total weighted tardiness with stochastic rework and reprocessing times

Abstract

Abstract Scheduling challenges are typical with electronics manufacturing services (EMS) providers. The rework and reprocessing of failed electronics components consume more time in the production line, causing jobs to miss their due dates. A mathematical model and a Modified Shortest Total Estimated Processing Time (MSTEPT) Algorithm to minimize the Total Weighted Tardiness (TWT) are proposed in this research. This research then develops a novel modified Genetic Algorithm approach to solve the scheduling problem with stochastic rework and reprocessing time. While the Genetic Algorithm as a methodology to solve scheduling problems has been developed in earlier research articles, the existing set of genes in the chromosomes of a regular Genetic Algorithm would not be able of handle jobs waiting to undergo reprocessing. The modified Genetic Algorithm in this research introduces the concept of priority genes, specifically encoded to handle jobs waiting to be reprocessed after they have been reworked. Experimental results indicate that the proposed modified GA outperforms the best of different commonly used dispatch rules, in terms of solution quality. For small-to-medium-sized job shops, the proposed algorithm outperforms optimal results from CPLEX® optimal solver, as well as those from the MSTEPT algorithm.