Realtime scheduling heuristics for just-in-time production in large-scale flexible job shops

This study aims to enable jobs to go smoothly between shops on a production line by completing jobs in the upstream shop just in time (JIT) for the downstream shop. We propose solutions to a factory that is seeking ways for an upstream shop to complete every job at the precise time such that the downstream shop can process the job. We model the upstream shop as a flexible job shop and propose four methods that form a realtime scheduling and control system for JIT production. We first propose a method to set for each job a due date by which the job should be completed in the upstream shop. The due dates are set in such a manner that jobs would be completed JIT for the downstream shop, if they are completed JIT for their due dates. We then propose a method to estimate the minimum number of workers needed in the upstream shop for completing the jobs by their due dates. We further propose two methods that work dynamically to complete each job neither too early nor too late for its due date. One is a dispatching rule that dynamically sequences jobs in process according to urgency degree. The other is a job-selecting heuristic that dynamically assigns workers to jobs such that jobs not nearing completion will be given priority in processing. Simulations by using data from the factory show that the methods can achieve in real time (i.e. within 0.00 seconds) JIT production for a flexible job shop problem involving hundreds of operations. More extensive simulations by using a large number of randomly generated problem instances show that solutions obtained in real time by the proposed methods greatly outperform those obtained in much longer time by metaheuristics designed for solving similar problems, and that each proposed method outperforms its rivals in the literature. The findings imply that integrating fast and high-performing heuristics and rules can be a solution to solve large-scale scheduling problems in real time.