Research on Job-shop Scheduling Problem Based on Bee Colony Algorithm

Long, Xiaojun; Zhang, Jingtao; Yang, Shuai; Wu, Wanying; Sun, Yunpeng; Guo, Zhanhe; Zhou, Kai

doi:10.1088/1742-6596/2033/1/012173

Cited by 4 publications

(5 citation statements)

References 2 publications

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“…Table 8 shows the optimal solutions (second) obtained by 11 algorithms to solve 10 instances. Except for the SLABC algorithm, this paper verifies the performance of ten algorithms of TS [44], HA [45], GWO [10], GA [46], SLGA [42], MA-CROG [47], MATS-PSO [48], ABC [49], ABC-S and ABC-S * Q [41] under the same experimental conditions The optimal results are marked in brackets, and HOV represents the optimal historical value of 10 calculation instances [10]. The SLABC algorithm obtains 8 optimal solutions in 10 calculation instances, indicating that the SLABC algorithm has excellent convergence performance.…”

Section: The Performance Comparison Of the Slabc Algorithmmentioning

confidence: 59%

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

et al. 2022

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To solve the problem of inserting new job into flexible job-shops, this paper proposes a dynamic self-learning artificial bee colony (DSLABC) optimization algorithm to solve dynamic flexible job-shop scheduling problem (DFJSP). Through the reasonable arrangement of the processing sequence of the jobs and the corresponding relationship between the operations and the machines, the makespan can be shortened, the economic benefit of the job-shop and the utilization rate of the processing machine can be improved. Firstly, the Q-learning algorithm and the traditional artificial bee colony (ABC) algorithm are combined to form the self-learning artificial bee colony (SLABC) algorithm. Using the learning characteristics of the Q-learning algorithm, the update dimension of each iteration of the ABC algorithm can be dynamically adjusted, which improves the convergence accuracy of the ABC algorithm. Secondly, the specific method of dynamic scheduling is determined, and the DSLABC algorithm is proposed. When a new job is inserted, the new job and the operations that have not started processing will be rescheduled. Finally, through solving the Brandimarte instances, it is proved that the convergence accuracy of the SLABC algorithm is higher than that of other optimization algorithms, and the effectiveness of the DSLABC algorithm is demonstrated by solving a specific example with a new job inserted.

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Section: The Performance Comparison Of the Slabc Algorithmmentioning

confidence: 59%

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

et al. 2022

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“…In this section, we design a large-scale brandimarte experiment to verify the effectiveness of SS-GWO algorithm for solving flexible job-shop scheduling problems. In these examples, Under the same experimental conditions, the performance of eight algorithms, namely IGWO [26], GA [28], SLGA [29], HGWO [30], ABC [31], TS [25], GWO, and SS-GWO, was verified. By solving ten brandimarte instances [25] with different scales (jobs * machines) and targeting the minimization of maximum completion time, the comparative results between the SS-GWO algorithm and other algorithms were obtained.…”

Section: Brandimarte Flexible Shop Scheduling Examplementioning

confidence: 93%

Research on Solving Flexible Job Shop Scheduling Problem Based on Improved GWO Algorithm SS-GWO

Zhou,

Tan,

Zhao

et al. 2024

Neural Process Lett

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As an important branch of production scheduling, the flexible job shop scheduling problem (FJSP) is a typical NP-hard problem. Researchers have adopted many intelligent algorithms to solve the FJSP problem, nonetheless, the task of dynamically adapting its essential parameters during the computational process is a significant challenge, resulting in the solution efficiency and quality failing to meet the production requirements. To this end, this paper proposes an adaptive gray wolf fast optimization algorithm (SS-GWO), which adopts the gray wolf algorithm (GWO) as the basic optimization method, and the algorithm adaptively selects the global search or local search according to the degree of agglomeration of individuals. Firstly, a non-linear convergence factor strategy is employed to control the global exploration and local exploitation capabilities of the algorithm at different stages. This enhances optimization precision and accelerates convergence speed, achieving a dynamic balance between the two. Secondly, the spiral search mechanism of Whale Optimization Algorithm is used in GWO to improve the exploration capability of Gray Wolf Optimization Algorithm. Finally, the effectiveness of SS-GWO model is verified by comparison experiments. The comparison demonstrates the superiority of SS-GWO over the other five state-of-the-art algorithms in solving the 22 classical benchmark test functions. SS-GWO is applied to solve FJSP by means of the standard test function bandimarte calculus. The optimal solution and performance of SS-GWO for solving FJSP are compared with other algorithms. The experimental results show that the SS-GWO algorithm has good optimization performance, and the maximum completion time is reduced by 19% and 37% compared with that of IGWO and GWO, respectively, and the proposed SS-GWO algorithm achieves a better solution effect on flexible job shop scheduling instances, which can satisfy the actual production scheduling needs.

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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%

“…and tasks can be operations in the production process, take-off and landing at airports, stages in construction projects, execution of computer programs, etc. Each task can have a different priority level [1]- [4], among other things based on the earliest possible start time or due date. Goals can also take many forms.…”

Section: Introductionmentioning

confidence: 99%

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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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Research on Job-shop Scheduling Problem Based on Bee Colony Algorithm

Cited by 4 publications

References 2 publications

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

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

Research on Solving Flexible Job Shop Scheduling Problem Based on Improved GWO Algorithm SS-GWO

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

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