Flexible Flowshop Scheduling Problem Considering Manpower Skill-based Processing Times

Scheduling for flexible flowshop environments is generally limited by resources such as manpower and machines. However, the majority of efforts tackle machines as the only constrained resource. This paper aims to investigate the problem of scheduling in flexible flowshop environments considering different skills as human resource constraints to minimize the total completion time. In this way, a mathematical model of complex integer linear programming is presented for solving small-sized problems in a reasonable computational time. In addition, due to the NP-hard nature of the problem, a whale hybrid optimization algorithm is tuned to solve the problem in large-sized dimensions. In order to evaluate the performance of the proposed optimization algorithm, the results are compared with five known optimization algorithms in the research background. All evaluations and results show the good performance of the whale hybrid algorithm. Especially, the final solution of the proposed algorithm shows a 0.75% deviation of the best solution in solving different instances on large-scale sizes. However, the genetic algorithm, memetic global and local search algorithm, and hybrid salp swarm algorithm are in the next ranks with 3.31, 3.52, and 4.02 percent respectively. In addition, proper discussions and managerial insights are provided for the relevant managers.