A single-machine learning effect scheduling problem with release times

In this paper, we investigate a single-machine problem with the learning effect and release times where the objective is to minimize the makespan. A branch-and-bound algorithm incorporating with several dominance properties and lower bounds is developed to derive the optimal solution. A heuristic algorithm is proposed to obtain a near-optimal solution. The computational experiments show that the branch-and-bound algorithm can solve instances up to 36 jobs, and the average error percentage of the proposed heuristic is less than 0.11%.