Flow shop scheduling with makespan objective and general position weighted learning effects

This article addresses the permutation flow shop problem of minimizing the makespan of all jobs with general position weighted learning effects. Since this problem is NP-hard, we propose two simple heuristics and analyze their worst-case bounds. The lower and upper bounds are given, and a branch-and-bound algorithm and two complex heuristics (including simulated annealing and tabu search) are proposed to solve this problem. Computational results show that our algorithms can generate effective and efficient schedules.