An intelligent optimization algorithm for blocking flow-shop scheduling based on differential evolution

Abstract

Owing to its large scale, the blocking flow-shop scheduling problem (BFSP) cannot be solved effectively by traditional optimization methods. To solve the problem, this paper develops a novel intelligent optimization algorithm based on differential evolution (DE) for the BFSP with a single objective: minimizing the total flow time (TFT). On the one hand, a new heuristic method was introduced to balance the quality and diversity of the initial population. On the other hand, a new operator was adopted to update the acceleration, velocity and position of each particle. In this way, the population will not converge prematurely to local optimums, and the local and global search abilities are perfectly balanced. Simulation on standard test set proves that our algorithm outperformed most commonly used methods in solving the BFSP. (Received, processed and accepted by the Chinese Representative Office.).