Date of Award
Master of Science (MS)
Dr. Mary E. Kurz
Dr. Scott J. Mason
Dr. Amin Khademi
Previous research on scheduling flexible flow lines (FFL) to minimize makespan has utilized approaches such as branch and bound, integer programming, or heuristics. Metaheuristic methods have attracted increasing interest for solving scheduling problems in the past few years. Particle swarm optimization (PSO) is a population-based metaheuristic method which finds a solution based on the analogy of sharing useful information among individuals. In the previous literature different PSO algorithms have been introduced for various applications. In this research we study some of the PSO algorithms, continuous and discrete, to identify a strong PSO algorithm in scheduling flexible flow line to minimize the makespan. Then the effectiveness of this PSO algorithm in FFL scheduling is compared to genetic algorithms.
Experimental results suggest that discrete particle swarm performs better in scheduling of flexible flow line with makespan criteria compared to continuous particle swarm. Moreover, combining discrete particle swarm with a local search improves the performance of the algorithm significantly and makes it competitive with the genetic algorithm (GA).
Amiri, Parastoo, "Discrete Particle Swarm Optimization for Flexible Flow Line Scheduling" (2015). All Theses. 2087.