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

Rongshen Lai,Bo Gao,Wenguang Lin,Yi-Zhang Jiang

doi:10.1155/2021/4731012

Rongshen Lai, Bo Gao + Show 2 more

Open Access

https://doi.org/10.1155/2021/4731012

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Journal: Scientific Programming	Publication Date: Dec 21, 2021
Citations: 1	License type: CC BY 4.0

Affiliation: Xiamen University of Technology

Abstract

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.

Highlights

Production scheduling is a key link to ensure the efficient and orderly development of the manufacturing process
Based on the inspiration of the marvelous group phenomenon in nature, researchers have proposed many effective swarm intelligence optimization algorithms to solve this problem, such as genetic algorithm [2], particle swarm algorithm [3], ant colony algorithm [4], etc. e development of swarm intelligence optimization algorithms is in the ascendant, providing many options for solving complex optimization problems. e wolf pack algorithm (WPA) is a group intelligence optimization algorithm obtained by simulating the hunting activities of the wolf pack
In order to verify the feasibility of the improved discrete wolf pack algorithm (IDWPA) designed in this paper in solving the no-wait flow shop scheduling problem (NWFSP) problem, this paper uses 5 sets of calculation examples to compare the algorithm with leapfrog algorithm (LFA) and genetic algorithm (GA) [24]

Summary

Introduction

Production scheduling is a key link to ensure the efficient and orderly development of the manufacturing process. E wolf pack algorithm (WPA) is a group intelligence optimization algorithm obtained by simulating the hunting activities of the wolf pack It has the advantages of strong global search ability, good generalization ability, and easy operation. There are few research reports on the application of the wolf pack algorithm to the NWFSP To this end, this paper combines the implementation process of the wolf pack algorithm and the feature analysis of NWFSP and proposes an improved discrete wolf pack algorithm and proves its effectiveness through a practical example and comparison with the leapfrog algorithm (LFA) and the genetic algorithm (GA).

Research Status

Performance Verification

Conclusions

Conflicts of Interest