Hybrid genetic algorithm for SDST flow shop scheduling with due dates: a case study

Abstract

Sequence dependent setup times (SDSTs) flow shop scheduling problems with due date related performance measures have been increasing attention from managers and researchers. Most of flow shop scheduling problems is NP hard and various heuristics and metaheuristics have been developed for finding solutions in a very reasonable time. In this work, the authors have proposed hybrid genetic algorithm (HGA) for SDST flow shop scheduling problems using strong computational power of matrix laboratory (MATLAB) and robustness of metaheuristics. Four modified NEH-based HGA have been developed and their performance has been compared with the help of a defined index for medium to large size problems. The objective function considered is minimisation of total weighted squared tardiness with due dates and weighting coefficient attached to each job. Comparison shows that the modified NEH4-based HGA performs better for medium to large size problems. The work has been supported by the computational results and application to the cold rolling division of a steel industry.