A Matheuristic-based Rescheduling Method for Flexible Job Shops with Lot-streaming and Machine Reconfigurations

Abstract

This paper studies a flexible job shop rescheduling problem with lot-streaming and machine reconfigurations (FJRP-LSMR) to minimize the sum of the instability and total weighted tardiness, where machine reconfigurations are performed by assembling selected auxiliary modules for processing different batches of products. In this case, a rescheduling process is triggered by dynamic events, and requires to determine the lot-sizing plan, machine assignment, and sublot sequencing simultaneously. To address the intractable problem with multiple decision-making processes, a matheuristic integrating the genetic algorithm (GA) and the mixed integer linear programming (MILP) technique is proposed, where an MILP model is developed for optimally solving the lot-sizing sub-problem, and is embedded to the GA as a local search function. The proposed matheuristic is tested on randomly-generated instances to investigate the performance of all the algorithmic components. Experimental results demonstrate that the GA representation is effective in the complicated dynamic scheduling problem, and the lot-sizing sub-problem can be well addressed by the proposed MILP-based local search.