In this paper, the focus is put on multi-core branch-and-bound algorithms for solving large-scale permutation-based optimization problems. We investigate five work stealing (WS) strategies with a new data structure called integer-vector-matrix (IVM). In these strategies, each thread has a private IVM allowing the local management of a set of subproblems enumerated using a factorial system. The WS strategies differ in the way the victim thread is selected and the granularity of stolen work units (intervals of factoradics). To assess the efficiency of the private IVM-based WS approach, the five WS strategies have been extensively experimented on the flowshop scheduling permutation problem and compared with their conventional linked-list-based counterparts. The obtained results demonstrate that the IVM-based WS outperforms the linked-list-based one in terms of CPU time, memory usage and number of performed WS operations. Copyright (c) 2016 John Wiley & Sons, Ltd.