Simulated annealing algorithms for the two-machine makespan flowshop scheduling with truncated learning consideration

Scheduling with learning effects has kept more attention in the recent days. However, it can be found that the actual processing time of a given job drops to zero precipitously as the job has a big processing time or the number of jobs increases. Moreover, most researchers paid more attention on single-machine settings, and the flowshop settings then are relative unexplored. Motivated by these observations, this paper consider a two-machine makspan flowshop problem in which the actual job processing time is a function of depending on the jobs already processed and a control parameter. This paper develops a branch-and-bound algorithm and four simulated annealing algorithms for the problem. In addition, the experimental results of all proposed algorithms are also provided