A novel differential evolution algorithm for no-idle permutation flow-shop scheduling problems

A novel Discrete Differential Evolution (DDE) algorithm is proposed in this paper for solving no-idle permutation flow-shop scheduling problems with maximum completion time (makespan) criterion. Firstly, individuals of the DDE algorithm are represented as discrete job permutations, and new mutation and crossover operators are developed. Secondly, a local search algorithm based on insert neighbourhood is embedded in the DDE algorithm to balance the exploration and exploitation and to enhance the local searching ability. In addition, we present two simple approaches to calculate makespan and a speed-up method for insert neighbourhood to improve the efficiency of the whole algorithm. Computational simulations and comparisons based on some well-known benchmarks demonstrate that the DDE algorithm is not only superior to the improved greedy and Kalczynski-Kamburowski heuristics in terms of searching quality, but also superior to the particle swarm optimisation and differential evolution algorithms according to searching quality, robustness and efficiency. [Received 9 July 2007; Revised 10 October 2007; Accepted 30 October 2007]