Two meta-heuristics for solving a new two-machine flowshop scheduling problem with the learning effect and dynamic arrivals.

This paper develops a new mathematical model and proposes two meta-heuristics for solving a two-machine flowshop scheduling problem that minimizes bi-objectives, namely the total idle time and the mean deviation from a common due data. In this paper, we assume the arrival time of jobs is dynamic, in which each job has a time window and can arrive in its time window randomly. We also assume the learning effect on the processing times considering as a position-dependent effect. Since the problem is an NP-hard one, we present a multiobjective genetic algorithm (MOGA) and a multiobjective simulated annealing (MOSA) algorithm to solve the given problems. The computational results confirm that the proposed MOGA has a better solution in comparison with the proposed MOSA, especially in large-sized problems. [ABSTRACT FROM AUTHOR]