An Improved NSGA-II with local search for multi-objective energy-efficient flowshop scheduling problem

• URL: http://arxiv.org/abs/2503.00588v1
• Date: Sat, 01 Mar 2025 18:56:06 GMT
• Title: An Improved NSGA-II with local search for multi-objective energy-efficient flowshop scheduling problem
• Authors: Vigneshwar Pesaru, Venkataramanaiah Saddikuti,
• Abstract summary: Energy consumption in manufacturing industries is directly related to efficient schedules.<n>The proposed algorithm achieved 47% and 15.44% average improvement in FT and EC minimization respectively on the benchmark problems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There has been an increasing concern to reduce the energy consumption in manufacturing and other industries. Energy consumption in manufacturing industries is directly related to efficient schedules. The contribution of this paper includes: i) a permutation flowshop scheduling problem (PFLSP) mathematical model by considering energy consumed by each machine in the system. ii) an improved non-dominated sorted genetic algorithm with Taguchi method with further incorporating local search (NSGA-II_LS) is proposed for the multi-objective PFLSP model. iii) solved 90 benchmarks problems of Taillard (1993) for the minimisation of flowtime (FT) and energy consumption (EC). The performance of the proposed NSGA_LS algorithm is evaluated on the benchmark problems selected from the published literature Li et. al, (2018). From these results, it is noted that the proposed algorithm performed better on both the objectives i.e., FT and EC minimization in 5 out of 9 cases. On FT objective our algorithm performed better in 8 out of 9 cases and on EC objective 5 out of 9 cases. Overall, the proposed algorithm achieved 47% and 15.44% average improvement in FT and EC minimization respectively on the benchmark problems. From the results of 90 benchmark problems, it is observed that average difference in FT and EC between two solutions is decreasing as the problem size increases from 5 machines to 10 machines with an exception in one case. Further, it is observed that the performance of the proposed algorithm is better as the problem size increases in both jobs and machines. These results can act as standard solutions for further research.

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