Abstract
The flexible job shop scheduling problem has always been the focus of research in the manufacturing field. However, most of the previous studies focused more on efficiency and ignored energy consumption. Energy, especially non-renewable energy, is an essential factor affecting the sustainable development of a country. To this end, this paper designs a flexible job shop scheduling problem model with energy consideration more in line with the production field. Except for the processing stage, the energy consumption of the transport, set up, unload, and idle stage are also included in our model. The weight property of jobs is also considered in our model. The heavier the job, the more energy it consumes during the transport, set up, and unload stage. Meanwhile, this paper invents an adaptive population non-dominated sorting genetic algorithm III (APNSGA-III) that combines the dual control strategy with the non-dominated sorting genetic algorithm III (NSGA-III) to solve our flexible job shop scheduling problem model. Four flexible job shop scheduling problem instances are formulated to examine the performance of our algorithm. The results achieved by the APNSGA-III method are compared with five classic multi-objective optimization algorithms. The results show that our proposed algorithm is efficient and powerful when dealing with the multi-objective flexible job shop scheduling problem model that includes energy consumption.
Highlights
Facing the global competition of the integration of the world economy, if the manufacturing industry wants to stand out from the cruel survival of the fittest, it must accelerate its response to external changes, improve product quality and performance, reduce various costs in the process, and provide customer-based personalized service on-demand [1,2].At the same time, affected by the deterioration of the climate and the greenhouse effect, society and the country have put forward higher and higher requirements for the green production of enterprises [3,4]
This paper considers various energy consumption components in the manufacturing industry that have not been considered in many previous studies but cannot be ignored, and designs the multiobjective flexible job shop scheduling problem (FJSP) considering the energy consumption of the transport, set up, processing, unload, and idle phase
In order to further improve the solution ability of NSGA-III to prevent it from falling into the local optimum, and to enhance the diversity of the population, this paper proposes a dual control strategy (DCS) for multi-objective optimization and combines it with NSGA-III to avoid precocity and enhance the optimization capability of NSGA-III on solving combinatorial optimization problems such as FJSP
Summary
Facing the global competition of the integration of the world economy, if the manufacturing industry wants to stand out from the cruel survival of the fittest, it must accelerate its response to external changes, improve product quality and performance, reduce various costs in the process, and provide customer-based personalized service on-demand [1,2]. With the continuous progress of the industrialization process, establishing an efficient scheduling model that contains more detailed energy consumption is an inevitable trend of development [2] In this case, some minor energy consumption considerations become important for saving costs and preventing environmental pollution [34]. Some minor energy consumption considerations become important for saving costs and preventing environmental pollution [34] For this reason, this paper considers various energy consumption components in the manufacturing industry that have not been considered in many previous studies but cannot be ignored, and designs the multiobjective FJSP considering the energy consumption of the transport, set up, processing, unload, and idle phase.
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