Abstract
Nowadays, manufacturing industry is under increasing pressure to save energy and reduce emissions, and thereby enhancing the energy efficiency of the machining system (MS) through operational methods on the system-level has attracted more attention. Energy-efficient scheduling (ES) has proved to be a typical measure suitable for all shop types, and an energy-efficient mechanism that a machine can be switched off and back on if it waits for a new job for a relatively long period is another proven effective energy-saving measure. Furthermore, their combination has been fully investigated in a single machine, flow shop and job shop, and the improvement in energy efficiency is significant compared with only applying ES for MS. However, whether such two energy-saving measures can be integrated in a flexible job shop environment is a gap in the existing study. To address this, a scheduling method applying an energy-efficient mechanism is proposed for a flexible job shop environment and the corresponding mathematical model, namely the energy-efficient flexible job shop scheduling (EFJSS) model, considering total production energy consumption (EC) and makespan is formulated. Besides, transportation as well as its impact on EC is taken into account in this model for practical application. Furthermore, a solution approach based on the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted, which can avoid the interference of subjective factors and help select a suitable machine for each operation and undertake rational operation sequencing simultaneously. Moreover, experimental results confirm the validity of the improved energy-efficient scheduling approach in a flexible job shop environment and the effectiveness of the solution.
Highlights
Manufacturing industry consumes enormous amounts of energy to transform resources into products or services, which increases the competition for energy resources and brings huge pressure to the environment
Due to significant flexibility in alternative machines and operation sequencing in flexible job shops, it is a great challenge to reduce total production energy consumption (EC) as well as maintain good performance for traditional scheduling objectives
An approach based on non-dominated sorting genetic algorithm-II (NSGA-II) was applied to solve it and experiments were conducted to illustrate the effectiveness of the proposed scheduling method as well as the model solving method
Summary
Manufacturing industry consumes enormous amounts of energy to transform resources into products or services, which increases the competition for energy resources and brings huge pressure to the environment. As the countries with the most energy-related CO2 emissions, China and the United States almost account for 40% of the world’s total CO2 emissions, and energy-related CO2 emission accounts for about 80% of all CO2 emissions in the United States [2] It is a huge challenge for manufacturing enterprises to reduce EC and enhance sustainability. The energy-saving methods for MS follows two types, namely energy-efficient process planning and energy-efficient scheduling [5] The former mainly pays attention to the machining EC of machines while the latter aims to minimize non-value added EC in manufacturing process. In view of this, considering transportation time, a new general ES model for flexible job shops with two performance criteria, total production EC and makespan, is proposed in this paper.
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