A real-time optimization algorithm for the integrated planning and scheduling problem towards the context of Industry 4.0

In this paper, we analyze the integration of two different problems in the supply chain, concerning the tactical and operational levels, and how the integration of two complex problems can be profitable towards the context of industry 4.0. More precisely, we address the integrated planning and scheduling problem on parallel and identical machines, seeking fast solutions that are globally optimal and flexible. In the planning phase, a set of jobs is assigned to their processing periods of time. On the other hand, in the scheduling phase, jobs are assigned to a machine in a given order. We propose a new metaheuristic approach through a variable neighborhood descent algorithm which iteratively explores four neighborhood structures with a first improvement strategy. The suggested algorithm was extensively tested using a large set of benchmark instances. The obtained results are discussed and compared with other approaches from literature.