Treffer: Energy-efficient multi-objective distributed assembly permutation flowshop scheduling by Q-learning based meta-heuristics.

This study addresses energy-efficient multi-objective distributed assembly permutation flowshop scheduling problems with minimisation of maximum completion time, mean of earliness and tardiness, and total carbon emission simultaneously. A mathematical model is introduced to describe the concerned problems. Five meta-heuristics are employed and improved, including the artificial bee colony, genetic algorithms, particle swarm optimization, iterated greedy algorithms, and Jaya algorithms. To improve the quality of solutions, five critical path-based neighborhood structures are designed. Q-learning, a value-based reinforcement learning algorithm that learns an optimal strategy by repeatedly interacting with the environment, is embedded into meta-heuristics. The Q-learning guides algorithms intelligently select appropriate neighborhood structures in the iterative process. Then, two machine speed adjustment strategies are developed to further optimize the obtained solutions. Finally, extensive experimental results show that the Jaya algorithm with Q-learning has the best performance for solving the considered problems. • DAPFSP with the maximum completion time, mean of earliness and tardiness, and total carbon emission objectives is firstly considered and modeled. • Five meta-heuristics are employed and improved to solve the concerned problems. • Five critical path-based neighborhood structures are designed. • Q-learning is embedded into meta-heuristics to select premium neighborhood structures at the iterations of meta-heuristics. [ABSTRACT FROM AUTHOR]