scholarly journals A Dynamic Adaptive Firefly Algorithm for Flexible Job Shop Scheduling

2022 ◽  
Vol 31 (1) ◽  
pp. 429-448
Author(s):  
K. Gayathri Devi ◽  
R. S. Mishra ◽  
A. K. Madan
Keyword(s):  
Firefly Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling

A Hybrid Discrete Firefly Algorithm for Multi-Objective Flexible Job Shop Scheduling Problems with Maintenance Activity

2014 ◽  
Vol 575 ◽  
pp. 922-925 ◽  
Author(s):  
S. Karthikeyan ◽  
P. Asokan ◽  
M. Chandrasekaran
Keyword(s):  
Firefly Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Maintenance Activity ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Availability Constraints ◽  
Discrete Firefly Algorithm

This paper presents a novel hybrid discrete firefly algorithm (HDFA) for solving the multi-objective flexible job shop scheduling problem with non fixed availability constraints (FJSP-nfa) due to maintenance activity. Three minimization objectives-the maximum completion time, the workload of the critical machine and the total workload of all machines are considered simultaneously. In this study, the discrete firefly algorithm is adopted to solve the problem, in which the machine assignment and operation sequence are processed by constructing a suitable conversion of the continuous functions as attractiveness, distance and movement, into new discrete functions. In addition the decoding mechanism considering the maintenance activity is presented. A neighbourhood based local search is hybridized to enhance the exploitation capability. Representative benchmark problems are solved in order to evaluate and study the performance of the proposed algorithm.

scholarly journals The hybrid firefly algorithm with the fuzzy movement method for solving a complex scheduling problem

2021 ◽  
Vol 15 (2) ◽  
pp. 208-219
Author(s):  
Ajchara Phu-ang
Keyword(s):  
Firefly Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
The Other ◽  
Initial Population ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Data Set ◽  
Shop Scheduling

This paper proposes a hybrid algorithm that applied the firefly algorithm (FA) with the new idea labeled as the fuzzy movement method for solving a complex scheduling problem called the flexible job shop scheduling. The step of the proposed algorithm is similar to the original FA, which is based on the concept of flashing behavior to attract the other fireflies. In order to improve the efficiency of the FA algorithm for the FJSP, the proposed algorithm introduces three new ideas. In the first idea, the genetic algorithm (GA) is used to generate the high quality of the initial population. Next, the self-adaptive roulette wheel selection which embedded in the GA introduced to increase the diversity of the machine selection process. Finally, the fuzzy movement method is presented to enhance the work balancing ability between the high workload machines and the low workload machines. The proposed algorithm has been evaluated with a benchmark data set and compared to the other algorithm. The experimental results demonstrate that the proposed algorithm can effectively solve the flexible job shop scheduling problem.

scholarly journals Improved Differential Evolution Algorithm for Flexible Job Shop Scheduling Problems

2019 ◽  
Vol 24 (3) ◽  
pp. 80 ◽  
Author(s):  
Prasert Sriboonchandr ◽  
Nuchsara Kriengkorakot ◽  
Preecha Kriengkorakot
Keyword(s):  
Differential Evolution ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Differential Evolution Algorithm ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Relative Errors ◽  
Improved Differential Evolution Algorithm

This research project aims to study and develop the differential evolution (DE) for use in solving the flexible job shop scheduling problem (FJSP). The development of algorithms were evaluated to find the solution and the best answer, and this was subsequently compared to the meta-heuristics from the literature review. For FJSP, by comparing the problem group with the makespan and the mean relative errors (MREs), it was found that for small-sized Kacem problems, value adjusting with “DE/rand/1” and exponential crossover at position 2. Moreover, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 3.25. For medium-sized Brandimarte problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave a mean relative error of 7.11. For large-sized Dauzere-Peres and Paulli problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 4.20. From the comparison of the DE results with other methods, it was found that the MRE was lower than that found by Girish and Jawahar with the particle swarm optimization (PSO) method (7.75), which the improved DE was 7.11. For large-sized problems, it was found that the MRE was lower than that found by Warisa (1ST-DE) method (5.08), for which the improved DE was 4.20. The results further showed that basic DE and improved DE with jump search are effective methods compared to the other meta-heuristic methods. Hence, they can be used to solve the FJSP.

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