scholarly journals MULTI-OPERATOR HYBRID GENETIC ALGORITHM-SIMULATED ANNEALING FOR REENTRANT PERMUTATION FLOW-SHOP SCHEDULING

2021 ◽  
Vol 11 (3) ◽  
pp. 109-126
Author(s):  
Achmad Pratama Rifai ◽  
Putri Adriani Kusumastuti ◽  
Setyo Tri Windras Mara ◽  
Rachmadi Norcahyo ◽  
Siti Zawiah Md Dawal
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Flow Shop ◽  
Hybrid Genetic Algorithm ◽  
Flow Shop Scheduling ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

scholarly journals Multimodal Optimization of Permutation Flow-Shop Scheduling Problems Using A Clustering-Genetic-Algorithm-Based Approach

2021 ◽  
Vol 11 (8) ◽  
pp. 3388
Author(s):  
Pan Zou ◽  
Manik Rajora ◽  
Steven Y. Liang
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Flow Shop ◽  
Optimal Schedule ◽  
Flow Shop Scheduling ◽  
Multimodal Optimization ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop ◽  
Global Optima

Though many techniques were proposed for the optimization of Permutation Flow-Shop Scheduling Problem (PFSSP), current techniques only provide a single optimal schedule. Therefore, a new algorithm is proposed, by combining the k-means clustering algorithm and Genetic Algorithm (GA), for the multimodal optimization of PFSSP. In the proposed algorithm, the k-means clustering algorithm is first utilized to cluster the individuals of every generation into different clusters, based on some machine-sequence-related features. Next, the operators of GA are applied to the individuals belonging to the same cluster to find multiple global optima. Unlike standard GA, where all individuals belong to the same cluster, in the proposed approach, these are split into multiple clusters and the crossover operator is restricted to the individuals belonging to the same cluster. Doing so, enabled the proposed algorithm to potentially find multiple global optima in each cluster. The performance of the proposed algorithm was evaluated by its application to the multimodal optimization of benchmark PFSSP. The results obtained were also compared to the results obtained when other niching techniques such as clearing method, sharing fitness, and a hybrid of the proposed approach and sharing fitness were used. The results of the case studies showed that the proposed algorithm was able to consistently converge to better optimal solutions than the other three algorithms.

scholarly journals An improved simulated annealing algorithm based on residual network for permutation flow shop scheduling

2020 ◽  
Author(s):  
Yang Li ◽  
Cuiyu Wang ◽  
Liang Gao ◽  
Yiguo Song ◽  
Xinyu Li
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Structure Design ◽  
Flow Shop Scheduling ◽  
Residual Network ◽  
Design Algorithm ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

Abstract The permutation flow shop scheduling problem (PFSP), which is one of the most important scheduling types, is widespread in the modern industries. With the increase of scheduling scale, the difficulty and computation time of solving the problem will increase exponentially. Adding the knowledge to intelligent algorithms is a good way to solve the complex and difficult scheduling problems in reasonable time. To deal with the complex PFSPs, this paper proposes an improved simulated annealing (SA) algorithm based on residual network (SARes). First, this paper defines the neighborhood of the PFSP and divides its key blocks. Second, the Residual Network (ResNet) is used to extract and train the features of key blocks. And, the trained parameters are stored in the SA algorithm to improve its performance. Afterwards, some key operators, including the initial temperature setting and temperature attenuation function of SA algorithm, are also modified. After every new solution is generated, the parameters trained by the ResNet are used for fast ergodic search until the local optimal solution found in the current neighborhood. Finally, the most famous benchmarks including part of TA benchmark are selected to verify the performance of the proposed SARes algorithm, and the comparisons with the-state-of-art methods are also conducted. The experimental results show that the proposed method has achieved good results by comparing with other algorithms. This paper also conducts experiments on network structure design, algorithm parameter selection, CPU time and other problems, and verifies the advantages of SARes algorithm from the aspects of stability and efficiency.

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