Digital Intelligence-Enabled Green Scheduling in Dynamic Job Shops: A Review

Abstract

Dynamic job shops must absorb new orders, machine failures and processing time variations while operating under increasingly demanding energy and carbon constraints. In such settings, an offline schedule may become obsolete soon after release, especially when production and energy states evolve on different time scales. Digital twins, data-driven models and artificial intelligence methods now make it possible to sense shop floor changes, anticipate their effects and revise schedules through feedback. This review organises the emerging literature through a ‘four loops and one layer’ framework: perception, modelling and prediction, intelligent decision-making, and execution feedback form the operating cycle, while continuous learning spans successive scheduling rounds. Studies are examined along three distinct but related dimensions—dynamic events, green objectives and digital intelligence methods. Within this D-G-I framework, the literature reveals a move from static optimisation to adaptive scheduling, from efficiency-centred formulations to coordinated efficiency–energy–carbon objectives, and from stand-alone rules towards combinations of data, models and domain knowledge. Yet the evidence remains uneven. Data–model coupling is often weak, transfer across production settings is limited, and genuinely closed-loop industrial validation is rare. These limitations make explainable decision-making, cross-scenario adaptation and digital twin-enabled closed-loop optimisation central priorities for subsequent research.