Minimizing the Makespan and the Carbon Emissions in Green Flexible Job Shop Scheduling Problem with Learning Effect

Abstract

Abstract One of the most difficult challenges for modern manufacturing is reducing carbon emissions. This paper focuses on the green scheduling problem in a flexible job shop system, taking into account energy consumption and worker learning effects. With the objectives to simultaneously minimze the makespan and total carbon emissions, the green flexible job shop scheduling problem (GFJSP) is formulated as a mixed integer linear multi-objective optimization model. Then the improved multi-objective sparrow search algorithm (IMOSSA) is developed for the optimal solution. Finally, this paper conducted computational experiments, including the the comparison between IMOSSA and the algortithm of GA and Jaya. The resluts demonstrate that the IMOSSA has a high precision, good convergence and excellent performance to solve the GFJSP in low-carbon manufacturing systems.