scholarly journals Improved Q ‐learning algorithm for solving permutation flow shop scheduling problems

2021 ◽  
Author(s):  
Zimiao He ◽  
Kunlan Wang ◽  
Hanxiao Li ◽  
Hong Song ◽  
Zhongjie Lin ◽  
...  
Keyword(s):  
Flow Shop ◽  
Learning Algorithm ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Q Learning ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

scholarly journals Hybrid Flow Shop Scheduling Problems Using Improved Fireworks Algorithm for Permutation

2020 ◽  
Vol 10 (3) ◽  
pp. 1174 ◽  
Author(s):  
Xuelian Pang ◽  
Haoran Xue ◽  
Ming-Lang Tseng ◽  
Ming K. Lim ◽  
Kaihua Liu
Keyword(s):  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Gaussian Mutation ◽  
Fireworks Algorithm ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Hybrid Flow Shop Scheduling ◽  
Permutation Flow Shop

Prior studies are lacking which address permutation flow shop scheduling problems and hybrid flow shop scheduling problems together to help firms find the optimized scheduling strategy. The permutation flow shop scheduling problem and hybrid flow shop scheduling problems are important production scheduling types, which widely exist in industrial production fields. This study aimed to acquire the best scheduling strategy for making production plans. An improved fireworks algorithm is proposed to minimize the makespan in the proposed strategies. The proposed improved fireworks algorithm is compared with the fireworks algorithm, and the improvement strategies include the following: (1) A nonlinear radius is introduced and the minimum explosion amplitude is checked to avoid the waste of optimal fireworks; (2) The original Gaussian mutation operator is replaced by a hybrid operator that combines Cauchy and Gaussian mutation to improve the search ability; and (3) An elite group selection strategy is adopted to reduce the computing costs. Two instances from the permutation flow shop scheduling problem and hybrid flow shop scheduling problems were used to evaluate the improved fireworks algorithm’s performance, and the computational results demonstrate the improved fireworks algorithm’s superiority.

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

2021 ◽  
Vol 16 (3) ◽  
pp. 269-284
Author(s):  
J.F. Ren ◽  
C.M. Ye ◽  
Y. Li
Keyword(s):  
Large Scale ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Batch Size ◽  
Scheduling Problem ◽  
Q Learning ◽  
Shop Scheduling ◽  
Model Free ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

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.

Sign in / Sign up

Export Citation Format

Share Document