Uniform parallel‐machine scheduling to minimize makespan with position‐based learning curves

Scheduling with learning effects has become a popular topic in the past decade; however, most of the research focuses on single‐machine problems. In many situations, there are machines in parallel and the skills of workers might be different due to their individual experience. In this paper, we study a uniform parallel machine problem in which the objective is to jointly find an optimal assignment of operators to machines and an optimal schedule to minimize the makespan. Two heuristic algorithms are proposed and computational experiments are conducted to evaluate their performance.