This paper addresses the Permutation Flow Shop Scheduling Problem with Heterogeneous Workers (PFSP-HW), an extension of the classical problem in which processing times depend not only on the job and machine, but also on the assigned worker. This variant better reflects practical environments where worker capabilities and proficiencies vary significantly. We propose a new Iterated Greedy (IG) heuristic adapted to handle worker heterogeneity. The IG heuristic combines destruction and reconstruction mechanisms with a local search procedure tailored for the problem. We develop two versions of the proposed algorithm and compare them with adapted state-of-the-art heuristics and metaheuristics from related problems. The algorithms were tested on a large benchmark set comprising 360 instances generated under various shop configurations. The suggested IG heuristics surpass current approaches in terms of solution quality and execution time, as determined by computational and statistical evaluations, making them reliable and efficient tools for solving the PFSP-HW.

Advanced production systems usually are complex in nature and aim to deal with multiple performance measures simultaneously. Therefore, in most cases, the consideration of a single objective function is not sufficient to properly solve scheduling problems. This paper investigates the multi-objective mixed no-idle flowshop scheduling problem. The addressed optimization case is minimizing makespan subject to an upper bound on total completion time. To solve this problem, we proposed a two-stage iterated greedy and a multi-objective constructive heuristic. Moreover, we developed a new multi-objective improvement procedure focusing on increasing the performance of the developed methods in solving the addressed problem. and a new initialization procedure. We performed several computational tests in order to compare our developed methods with the main algorithms from similar scheduling problems in the literature. It was revealed that the proposed approaches give the best results compared with other state-of-the-art performing methods.