scholarly journals Solving No-Wait Flow Shop Scheduling Problem Based on Discrete Wolf Pack Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Rongshen Lai ◽  
Bo Gao ◽  
Wenguang Lin
Keyword(s):  
Flow Shop ◽  
Optimal Solution ◽  
Solution Space ◽  
Flow Shop Scheduling ◽  
Searching Behavior ◽  
Scheduling Problem ◽  
Application Research ◽  
Shop Scheduling ◽  
No Wait ◽  
The Individual

Aiming at the no-wait flow shop scheduling problem with the goal of minimizing the maximum makespan, a discrete wolf pack algorithm has been proposed. First, the methods for solving the no-wait flow shop scheduling problem and the application research of the wolf pack algorithm were summarized, and it was pointed out that there was lack of research on the application of the wolf pack algorithm to solve the no-wait flow shop scheduling problem. According to the analysis of characteristics of the no-wait flow shop scheduling problem, the individual wolf was coded by a decimal integer; wolf searching behavior was realized through the exchange of different code bits in the individual wolf, and the continuous code segment of the head wolf was randomly selected to replace the corresponding code of the fierce wolf, by which the behaviors of wolves raiding and sieging were realized, and the population was updated according to the rule of “survival of the strong.” In particular, to fully explore the potential optimal solution in the solution space, loop operations were added to the wandering, summoning, and siege processes. Finally, based on a comparison with the leapfrog algorithm and the genetic algorithm, the effectiveness of the algorithm was verified.

scholarly journals A Reinforcement Learning Method for a Hybrid Flow-Shop Scheduling Problem

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 222 ◽  
Author(s):  
Han ◽  
Guo ◽  
Su
Keyword(s):  
Reinforcement Learning ◽  
Flow Shop ◽  
Optimal Solution ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
First Choice ◽  
Hybrid Flow Shop Scheduling

The scheduling problems in mass production, manufacturing, assembly, synthesis, and transportation, as well as internet services, can partly be attributed to a hybrid flow-shop scheduling problem (HFSP). To solve the problem, a reinforcement learning (RL) method for HFSP is studied for the first time in this paper. HFSP is described and attributed to the Markov Decision Processes (MDP), for which the special states, actions, and reward function are designed. On this basis, the MDP framework is established. The Boltzmann exploration policy is adopted to trade-off the exploration and exploitation during choosing action in RL. Compared with the first-come-first-serve strategy that is frequently adopted when coding in most of the traditional intelligent algorithms, the rule in the RL method is first-come-first-choice, which is more conducive to achieving the global optimal solution. For validation, the RL method is utilized for scheduling in a metal processing workshop of an automobile engine factory. Then, the method is applied to the sortie scheduling of carrier aircraft in continuous dispatch. The results demonstrate that the machining and support scheduling obtained by this RL method are reasonable in result quality, real-time performance and complexity, indicating that this RL method is practical for HFSP.

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