Parallel-machine scheduling to minimize makespan with fuzzy processing times and learning effects

• We study the parallel machine scheduling problem with fuzzy processing times and learning effects.
• The objective is to minimize the fuzzy makespan based on the possibility measure.
• A genetic algorithm and a simulated annealing algorithm are developed.
• The results show that SA outperforms GA in this model.

This paper addresses parallel machine scheduling with learning effects. The objective is to minimize the makespan. To satisfy reality, we consider the processing times as fuzzy numbers. To the best of our knowledge, scheduling with learning effects and fuzzy processing times on parallel machines has never been studied. The possibility measure will be used to rank the fuzzy numbers. Two heuristic algorithms, the simulated annealing algorithm and the genetic algorithm, are proposed. Computational experiments have been conducted to evaluate their performance.