Improved shuffled frog leaping algorithm for permutation flow shop scheduling

2014 ◽  
Author(s):  
Zhenhao Xu ◽  
Xiao Xu ◽  
Xingsheng Gu
Keyword(s):  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Shuffled Frog Leaping Algorithm ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop ◽  
Shuffled Frog Leaping

scholarly journals A Hybrid Shuffled Frog Leaping Algorithm for Solving No_Idle Permutation Flow Shop Scheduling Problems

2011 ◽  
Vol 1 ◽  
pp. 110-115 ◽  
Author(s):  
Ya Min Wang ◽  
Yun Bao ◽  
Jing Chen ◽  
Jun Qing Li
Keyword(s):  
Flow Shop ◽  
Early Stage ◽  
Solution Space ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Shuffled Frog Leaping Algorithm ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop ◽  
Shuffled Frog Leaping

This paper presents a novel hybrid shuffled frog-leaping algorithm (HSFLA) for solving the no_idle permutation flow shop scheduling problems(NIFS) with the criterion to minimize the maximum completion time( makespan). First, the algorithm employs insert- neighborhood-based local search to enhance the searching ability. Second, it adopts roulette wheel selection operator to generate the global best frog in the early stage of the evolution which can expand the searching solution space. The experimental results show that the proposed algorithm is effective and efficient for different scale benchmarks of NIFS .

scholarly journals A cooperated shuffled frog-leaping algorithm for distributed energy-efficient hybrid flow shop scheduling with fuzzy processing time

2021 ◽  
Author(s):  
Jingcao Cai ◽  
Deming Lei
Keyword(s):  
Energy Efficient ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Distributed Energy ◽  
Shuffled Frog Leaping Algorithm ◽  
Shop Scheduling ◽  
Fuzzy Processing Time ◽  
Hybrid Flow Shop Scheduling ◽  
Shuffled Frog Leaping

AbstractDistributed hybrid flow shop scheduling problem (DHFSP) has attracted some attention; however, DHFSP with uncertainty and energy-related element is seldom studied. In this paper, distributed energy-efficient hybrid flow shop scheduling problem (DEHFSP) with fuzzy processing time is considered and a cooperated shuffled frog-leaping algorithm (CSFLA) is presented to optimize fuzzy makespan, total agreement index and fuzzy total energy consumption simultaneously. Iterated greedy, variable neighborhood search and global search are designed using problem-related features; memeplex evaluation based on three quality indices is presented, an effective cooperation process between the best memeplex and the worst memeplex is developed according to evaluation results and performed by exchanging search times and search ability, and an adaptive population shuffling is adopted to improve search efficiency. Extensive experiments are conducted and the computational results validate that CSFLA has promising advantages on solving the considered DEHFSP.

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.

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