Low-carbon scheduling and estimating for a flexible job shop based on carbon footprint and carbon efficiency of multi-job processing

With the enhancement of people's environmental awareness, low-carbon and energy efficiency in manufacturing industry have been drawing much attention due to the huge consumption of raw materials and energy during machining processes. But as one of the approaches to reduce carbon emission, manufacturing shop scheduling strategies have historically emphasized the makespan, machine workload, and so on and neglected energy and environmental factors in most cases. This article presents a model of low-carbon scheduling of the flexible job shop, which considers both factors of production (i.e. makespan and machine workload) and environmental influence (i.e. carbon emission). A carbon footprint model of multi-job processing is established to quantify the carbon emission of different scheduling plans, and three carbon efficiency indicators are put forward to estimate the carbon emission of parts and machine tools, that is, processing carbon efficiency, part carbon efficiency, and machine tool carbon efficiency. To solve the proposed model, a hybrid non-dominated sorting genetic algorithm II which combines the original non-dominated sorting genetic algorithm II with a local search algorithm based on neighborhood search is proposed. Finally, test of some well-known benchmark instances is carried out to verify the effectiveness of the proposed algorithm, and an actual case is studied to demonstrate the feasibility and applicability of the proposed model.