2023
DOI: 10.1038/s41598-023-33615-z
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Minimizing the makespan and carbon emissions in the green flexible job shop scheduling problem with learning effects

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 objective of simultaneously minimizing the makespan and total carbon emissions, the green flexible job shop scheduling problem (GFJSP) is formulated as a mixed integer linear multiobjective optimization model. Then, the improved multiobjective sparrow search algor… Show more

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Cited by 4 publications
(2 citation statements)
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“…The non-dominant sorting Genetic algorithm II (NSGA-II) method was used to solve the problem. Aiming at minimizing completion time and total carbon emissions, Li [29] focused on the study of green scheduling in flexible job-shop systems. Considering energy consumption and worker learning efficiency, an improved multi-objective sparrow search algorithm (IMOSSA) is used to find the optimal solution.…”
Section: Dynamic Job Shop Scheduling Based On Conventional Methodsmentioning
confidence: 99%
“…The non-dominant sorting Genetic algorithm II (NSGA-II) method was used to solve the problem. Aiming at minimizing completion time and total carbon emissions, Li [29] focused on the study of green scheduling in flexible job-shop systems. Considering energy consumption and worker learning efficiency, an improved multi-objective sparrow search algorithm (IMOSSA) is used to find the optimal solution.…”
Section: Dynamic Job Shop Scheduling Based On Conventional Methodsmentioning
confidence: 99%
“…[ 33 ] analyze a production line in an automobile assembly plant, using simulation and dispatching rules, to define a production planning strategy for the company. [ 34 ] focus on the green scheduling problem in a flexible job shop system. The authors formulate a mixed integer linear multiobjective optimization model.…”
Section: Introductionmentioning
confidence: 99%