Automated job shop scheduling with dynamic processing times and due dates using project management and industry 4.0

Abstract

This paper presents an automated model for improving job shop scheduling by incorporating Industry 4.0 and project management. The proposed model develops dynamic and adaptive schedules to incorporate real-time information about processing times (including random unexpected events) and due dates, reflecting the impact of industry 4.0 on rescheduling decisions. The model minimizes the earliness and tardiness costs while considering the rescheduling costs and is motivated by the real-life case study from a local company. This study applied Earned Value (EV) and Forecasted Total Cost at Completion $(EAC_f)$ concepts and integrated it with mixed integer linear programming (MILP) model to design an adaptive automated scheduling system. The paper presents a new application of project management concept in MILP job shop scheduling. Also, this research proposes new rescheduling concept to minimize unnecessary schedule changes while providing the best possible schedule to process all the jobs.