Heuristic and exact algorithms for single-machine scheduling problems with general truncated learning effects

This paper addresses single-machine scheduling problems with truncated learning effects. The objective is to determine the optimal job schedule such that the makespan, the total weighted completion time and the maximum lateness are to be minimized. All the considered problems are NP-hard; hence, for each problem, we propose the heuristic and branch-and-bound algorithms. Extensive numerical experiments validate the efficiency of the proposed solution algorithms on a set of randomly generated instances.