A discrete butterfly-inspired optimization algorithm for solving Permutation Flow-Shop scheduling Problems

Abstract

Permutation Flow-Shop Scheduling Problem (PFSP) which exists in many manufacturing systems is a classic combinatorial optimization problem. Studies have shown that the PFSP including more than three machines belongs to the NP-hard problems and is difficult to solve. Based on a new bio-inspired algorithm – Artificial Butterfly Optimization (ABO) algorithm, this paper presents a Discrete Artificial Butterfly Optimization (DABO) algorithm to find the permutation that gives the smallest completion time or the smallest total flow time. The performance of the proposed algorithm is tested on well-known benchmark suites of Car, Reeves and Taillard. The experimental results show that the proposed algorithm is able to provide very promising and competitive results on most benchmark functions. The DABO algorithm is then employed for one production optimization problem.