A metaheuristic algorithm and simulation to study the effect of learning or tiredness on sequence-dependent setup times in a parallel machine scheduling problem. (1st March 2019) - Expósito-Izquierdo, Christopher; Angel-Bello, Francisco; Melián-Batista, Belén; Alvarez, Ada; Báez, Sarahí
Highlights: Learning and deterioration effects on sequence-dependent setup times in a parallel machine scheduling problem. Variable Neighborhood Search to provide a set of high-quality and diverse solutions. Simulation model based on the agent-based simulation paradigm to handle randomness. Abstract: This work analyses the effects of learning or tiredness on the setup times in a scheduling problem with identical parallel machines. This problem involves setup times that depend on the sequence of jobs with the goal of minimizing the sum of total completion times. Due to the complexity of the problem and the assumption that high-quality solutions of the problem without effects are also high-quality solutions when these effects are considered, we firstly propose a metaheuristic algorithm aimed at finding high-quality and diverse solutions, ignoring the learning/tiredness issues. Then, we study the effects of learning or tiredness on the obtained solutions in a real-world scenario by using a multi-agent simulation approach. The computational experiments carried out demonstrate that the simulation model developed in this work is valid to handle randomness in a practical scenario, allowing to be adapted to different learning or tiredness effects. Furthermore, the computational experiments underscore the fact that the proposal can be used as a decision support tool aimed at estimating the amount of job to be assigned to the available machines on the basis of the operator profile.