A novel shuffled frog leaping algorithm for low carbon flexible job shop scheduling
AI Zi-yi
LEI De-ming
Abstract：In this paper low carbon flexible job shop scheduling problem (FJSP) is considered. A new shuffled frog leaping algorithm (SFLA) is proposed to minimize total carbon emission, in which memory is used to store best solutions. Population division is done by using population and memory. Some new strategies such as cooperation of global search and local search are applied to realize the search in the memeplex. Population shuffling is deleted to simplify the algorithm. We compared hybrid genetic algorithm and teaching-learning-based optimization algorithm, which also considered the combination of local search and global search. Extensive experiments are conducted on a number of instances and result analyses show that SFLA has strong search ability and competitiveness for low carbon FJSP.
Keywords：flexible job shopcarbon emissionshuffled frog leaping algorithmmemory
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 1361-1368 )