Evolution of Flowshop Scheduling Techniques: A Comprehensive Review of Permutational, Non-Permutational, and Distributed Models

Abstract

Scheduling problems in various industries have long been a focal point of research due to their critical impact on operational efficiency and productivity. This body of work encompasses several key areas including makespan, machine idle time, job idle time, tardiness, and other criteria. The field has evolved significantly with advancements in both theoretical and practical approaches. Notable contributions have been made across different types of scheduling models such as permutational flowshop, non-permutational flowshop, and distributed flowshop scheduling. Researchers have employed a range of methodologies including genetic algorithms, simulated annealing, particle swarm optimization, and hybrid approaches to address these challenges. This overview summarizes significant advancements and trends in these areas, highlighting the development of novel algorithms and practical applications that aim to improve scheduling performance.