Minimizing total flowtime in flow shop scheduling by a quantum-inspired swarm evolutionary algorithm

Abstract

In this paper, the permutation flow shop scheduling problem (PFSP) is considered with the objective of minimizing the total flowtime by using a novel quantum-inspired swarm evolutionary algorithm (QSEA). In this QSEA, the quantum chromosomes are encoded by using the quantum rotating angle and a simple converting mechanism for determining job sequence is proposed for the representation of PFSP firstly. Then, we adopt the particle swarm optimization (PSO) strategy to perform the updating of quantum gate and the local search to perform thorough exploitation in promising solutions. By merging the advantages of PSO strategy, local search with QEA, we can obtain high performance. Also, this paper is the first to adopt the QSEA to minimize the total flowtime of permutation FSP and we make the simulation. The comparisons with other state-of-the-art approaches demonstrate the effectiveness of the proposed QSEA for permutation flowshop scheduling problem.