Solving the Csum permutation flowshop scheduling problem by genetic local search

A new meta-heuristic method is proposed to solve the classical permutation flowshop scheduling problem with the objective of minimizing sum of completion times. The representative neighbourhood combines the stochastic sampling method (mainly used in simulated annealing) and the best descent method (elaborated in the tabu search), and integrates them naturally into a single method. The method is further extended into the genetic local search framework by using a population and a special crossover operator called multi-step crossover fusion. Computational experiments using benchmark problems demonstrate the effectiveness of the proposed method