A new solution to distributed permutation flow shop scheduling problem based on NASH Q-Learning

Aiming at Distributed Permutation Flow-shop Scheduling Problems (DPFSPs), this study took the minimization of the maximum completion time of the workpieces to be processed in all production tasks as the goal, and took the multi-agent Reinforcement Learning (RL) method as the main frame of the solution model, then, combining with the NASH equilibrium theory and the RL method, it proposed a NASH Q-Learning algorithm for Distributed Flow-shop Scheduling Problem (DFSP) based on Mean Field (MF). In the RL part, this study designed a two-layer online learning mode in which the sample collection and the training improvement proceed alternately, the outer layer collects samples, when the collected samples meet the requirement of batch size, it enters to the inner layer loop, which uses the Q-learning model-free batch processing mode to proceed, and adopts neural network to approximate the value function to adapt to large-scale problems. By comparing the Average Relative Percentage Deviation (ARPD) index of the benchmark test questions, the calculation results of the proposed algorithm outperformed other similar algorithms, which proved the feasibility and efficiency of the proposed algorithm.

Interactive Evolutionary Computation Approach to Permutation Flow Shop Scheduling Problem

Artificial intelligence and its subfields have be-come part of our everyday lives and eÿciently solve many problems that are very hard for us humans. But in some tasks, these methods strug-gle, while we, humans, are much better solvers with our intuition. Because of that, the ques-tion arises: why not combine intelligent methods with human skills and intuition? This paper pro-poses an Interactive Evolutionary Computation approach to the Permutation Flow Shop Schedul-ing Problem by incorporating human-in-the-loop in MAX-MIN Ant System through gamification of the problem. The analysis shows that combin-ing the evolutionary computation approach and human-in-the-loop leads to better solutions, sig-nificantly when the complexity of the problem in-creases.