A Discrete-Event Heuristic for Makespan Optimization in Multi-Server Flow-Shop Problems with Machine re-entering

Abstract

Modern Manufacturing, known as Industrial Internet or Industry 4.0, is more than ever determined by customer-specific products, that are to be manufactured and delivered in given lead times and due-dates. Many of these manufacturing systems can be modeled as flow-shops where some of the processes can handle jobs on parallel machines. In addition, complex manufacturing environments contain specific machine loops or re-entry cycles where jobs might re-enter specific processes at some point of the flow-shop chain. A specific server is assigned to a job the first time it visits a machine, and it is quite usual that this job has to be processed by exactly the same server if it re-visits the machine due to quality issues. With the goal of minimizing the makespan, this paper analyzes this complex flow-shop setting and proposes an original discrete-event heuristic for solving it in short computing times. Our algorithm combines biased (non-uniform) randomization strategies with the use of a discrete-event list, which iteratively processes as the simulation clock advances. A series of computational experiments contribute to illustrate the performance of our methodology.