Workload-aware loop schedulers were introduced to deliver better performance than classical strategies, but they present limitations on work-load estimation, chunk scheduling and integrability with applications. Targeting these challenges, in this work we propose a novel workload-aware loop sched-uler that is called BinLPT and it is based on three features. First, it relies on some user-supplied estimation of the workload of the target parallel loop. Second , BinLPT uses a greedy bin packing heuristic to adaptively partition the iteration space in several chunks. The maximum number of chunks to be produced is a parameter that may be fine-tuned. Third, it schedules chunks of iterations using a hybrid scheme based on the LPT rule and on-demand scheduling. We integrated BinLPT in OpenMP, and we evaluated its performance in a large-scale NUMA machine using a synthetic kernel and 3D N-Body Simulations. Our results revealed that BinLPT improves performance over OpenMP's strategies by up to 45.13% and 37.15% in the synthetic and application kernels, respectively.