A matheuristic with re-lot-sizing strategies for flexible job-shop rescheduling problem with lot-streaming and machine reconfigurations

This paper investigates a flexible job-shop rescheduling problem with lot-streaming and machine reconfigurations (FJRP-LSMR) for the total weighted tardiness minimization, where production setups between sublots are performed by assembling selected auxiliary modules to reconfigure machines. When a given long-term schedule is interrupted by dynamic events, such as machine breakdowns and job insertions, a rescheduling process is triggered to determine the lot-sizing plan, sublot sequences, and machine configurations simultaneously. A matheuristic with re-lot-sizing strategies (MH${}_{RLS}$) is proposed to address the FJRP-LSMR, which takes the genetic algorithm as the main framework and introduces a mixed integer linear programming (MILP) based lot-sizing optimization (LSO${}_{RLS}$) function to improve lot-sizing plans. Two re-lot-sizing strategies, namely complete re-lot-sizing and partial re-lot-sizing, are defined to reset more sublot sizes in rescheduling processes, thus the solution space that can be visited by the MILP model is greatly expanded for further improvements. Four groups of test instances and a complex real-world industrial case are adopted to evaluate the performance of the proposed methods. Extensive experimental results demonstrate that, with the help of re-lot-sizing strategies, the LSO${}_{RLS}$ can find high-quality lot-sizing plans within a short period of time, and the proposed MH${}_{RLS}$ shows the best performance in the optimality, stability, and convergence.