Dynamic Self-Learning Artificial Bee Colony Optimization Algorithm for Flexible Job-Shop Scheduling Problem with Job Insertion

Long, Xiaojun; Zhang, Jingtao; Zhou, Kaixiong; Jin, Tianguo

doi:10.3390/pr10030571

Cited by 22 publications

(7 citation statements)

References 45 publications

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“…To verify the effectiveness of the proposed QL-AO, it is compared with the most classic algorithms, GA and PSO, three novel algorithms, ABC, CS and JAYA, and the basic AO. In addition, Q-learning-based genetic algorithm (QL-GA) and Q-learning-based artificial bee colony algorithm (QL-ABC), proposed by Chen et al [35] in 2020 and Long et al [36] in 2022, respectively, are selected as comparison algorithms. In fairness, the competitive algorithms used the same heuristic initialization method as QL-AO.…”

Section: Algorithm Comparisonmentioning

confidence: 99%

Integrated Optimization of Blocking Flowshop Scheduling and Preventive Maintenance Using a Q-Learning-Based Aquila Optimizer

Ge,

Wang

2023

Symmetry

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In recent years, integration of production scheduling and machine maintenance has gained increasing attention in order to improve the stability and efficiency of flowshop manufacturing systems. This paper proposes a Q-learning-based aquila optimizer (QL-AO) for solving the integrated optimization problem of blocking flowshop scheduling and preventive maintenance since blocking in the jobs processing requires to be considered in the practice manufacturing environments. In the proposed algorithmic framework, a Q-learning algorithm is designed to adaptively adjust the selection probabilities of four key population update strategies in the classic aquila optimizer. In addition, five local search methods are employed to refine the quality of the individuals according to their fitness level. A series of numerical experiments are carried out according to two groups of flowshop scheduling benchmark. Experimental results show that QL-AO significantly outperforms six peer algorithms and two state-of-the-art hybrid algorithms based on Q-Learning on the investigated integrated scheduling problem. Additionally, the proposed Q-learning and local search strategies are effective in improving its performance.

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Section: Algorithm Comparisonmentioning

confidence: 99%

Integrated Optimization of Blocking Flowshop Scheduling and Preventive Maintenance Using a Q-Learning-Based Aquila Optimizer

Ge,

Wang

2023

Symmetry

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“…The parameters of the original ABC algorithm are the number of food sources (SN), which is equal to the number of employed or spectator bees [4], [17], the number of attempts after which a food source must be abandoned (limit), and the criterion of termination. The main phases of the original algorithm as shown in Figure 2.…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

A Good Relative Percentage Increase (RPI) of Variant Job Scheduling Using Artificial Bee Colony (ABC)

Apnena¹,

Faujiyah

2022

JAIS

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Artificial Bee Colony (ABC) which is a development of the intelligent swarm model and is a branch of artificial intelligence based on self-organization systems. Artificial Bee Colony (ABC) is an intelligent algorithm that is inspired by the food search process carried out by bees. This is like what is done when there are many jobs that need to find the optimal value, where each job to be processed has a specific route of operations to be performed on a set of machines, and a different flow shop and variant: all jobs follow the same machine sequences. We will focus on the latter. In this study, ABC is implemented to optimize work scheduling, in this case 7 different variations are used with mxn values between 10x3 to 40x15 on 10 to 40 jobs. To evaluate the results, the Relative Percentage Increase (RPI) has been used in the test with an achievement range between 1.9 to 18.9.

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“…More and more scholars have found that meta-heuristic algorithms, due to their simplicity, efficiency, and wide applicability, show significant advantages in solving combinatorial optimization problems such as the MTSP [17][18] and the JSP [19][20], which provide the feasibility and effectiveness of the efficient solution of such complex problems. In terms of algorithm design, scholars have proposed hybrid meta-heuristic algorithms combining techniques such as the dragonfly algorithm (DA), firefly algorithm (FA), and genetic algorithm (GA) [21][22].…”

Section: Introductionmentioning

confidence: 99%

Grid Operation and Inspection Resource Scheduling Based on an Adaptive Genetic Algorithm

Bingnan Tang,

Jing Bao,

Nan Pan

et al. 2024

Int. j. eng. technol. innov.

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Grid operation and inspection a key links to ensure the safe operation of the power system, which requires efficient task allocation and resource scheduling. To address this problem, this paper proposes a resource scheduling model for grid operation and inspection based on bi-level programming. Firstly, the O&I process is analyzed and defined as a combined optimization problem of the multiple traveling salesman problem (MTSP) and the job-shop scheduling problem (JSP). Secondly, a bi-level programming model of MTSP and JSP is established according to the characteristics of the problem. Finally, an adaptive genetic algorithm is used to solve the problem. The feasibility of the model and the advancement of the algorithm are verified through the simulation of real scenarios and a large number of tests, which provide strong support for the sustainable development of the power system.

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Dynamic Self-Learning Artificial Bee Colony Optimization Algorithm for Flexible Job-Shop Scheduling Problem with Job Insertion

Cited by 22 publications

References 45 publications

Integrated Optimization of Blocking Flowshop Scheduling and Preventive Maintenance Using a Q-Learning-Based Aquila Optimizer

Integrated Optimization of Blocking Flowshop Scheduling and Preventive Maintenance Using a Q-Learning-Based Aquila Optimizer

A Good Relative Percentage Increase (RPI) of Variant Job Scheduling Using Artificial Bee Colony (ABC)

Grid Operation and Inspection Resource Scheduling Based on an Adaptive Genetic Algorithm

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