New upper bounds for the permutation flowshop scheduling problem

Abstract

The paper proposes an implementation of the population learning algorithm (PLA) for solving the permutation flowshop scheduling problem (PFSP). The PLA can be considered as a useful framework for constructing a hybrid approaches. In the proposed implementation the PLA scheme is used to integrate evolutionary, tabu search and simulated annealing algorithms. The approach has been evaluated experimentally. Experiment has produced 14 new upper bounds for the standard benchmark dataset containing 120 PFSP instances and has shown that the approach is competitive to other algorithms. © Springer-Verlag Berlin Heidelberg 2005.