A Memetic Gravitation Search Algorithm for Solving Permutation Flow Shop Scheduling

2014 ◽  
Vol 1079-1080 ◽  
pp. 626-630
Author(s):  
Ko Wei Huang ◽  
Jui Le Chen ◽  
Chu Sing Yang
Keyword(s):  
Flow Shop ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Method ◽  
Flow Shop Scheduling ◽  
Swarm Optimization ◽  
Shop Scheduling ◽  
Combinatorial Optimization Problems ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

The permutation flow-shop scheduling problem (PFSP) is an non-deterministic polynomialtime (NP) hard combinatorial optimization problems and has been widely researched within thescheduling community. In this paper, a memetic gravitation search algorithm (MGSA) is proposedto solve the PFSP for minimizing the makespan measure. The smallest position value (SPV) rule isutilized for converting the continuous number to job permutations for determining the most suitablethe proposed MGSA for the PFSP. The proposed MGSA uses a Nawaz-Enscore-Ham (NEH) heuristicalgorithm for initialization of population, and a simulated annealing (SA) is coupled with the variableneighborhood search (VNS) as the local search method to balance exploitation and exploration. Toverify the robustness of the MGSA, it is compared with three particle swarm optimization (PSO) algorithmson the basis of 12 PFSP instances with different job sizes ranging from 20 to 500. The resultsdemonstrate that the proposed MGSA can outperform other compared algorithms.

scholarly journals A Hybrid Crow Search Algorithm for Solving Permutation Flow Shop Scheduling Problems

2019 ◽  
Vol 9 (7) ◽  
pp. 1353 ◽  
Author(s):  
Ko-Wei Huang ◽  
Abba Girsang ◽  
Ze-Xue Wu ◽  
Yu-Wei Chuang
Keyword(s):  
Local Search ◽  
Flow Shop ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Flow Shop Scheduling ◽  
Neighborhood Search ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

The permutation flow shop scheduling problem (PFSP) is a renowned problem in the scheduling research community. It is an NP-hard combinatorial optimization problem that has useful real-world applications. In this problem, finding a useful algorithm to handle the massive amounts of jobs required to retrieve an actionable permutation order in a reasonable amount of time is important. The recently developed crow search algorithm (CSA) is a novel swarm-based metaheuristic algorithm originally proposed to solve mathematical optimization problems. In this paper, a hybrid CSA (HCSA) is proposed to minimize the makespans of PFSPs. First, to make the CSA suitable for solving the PFSP, the smallest position value rule is applied to convert continuous numbers into job sequences. Then, the HCSA uses a Nawaz–Enscore–Ham (NEH) technique to create a population with the required levels of quality and diversity. We apply a local search to enhance the quality of the solutions and avoid premature convergence; simulated annealing enhances the local search of a method based on a variable neighborhood search. Computational tests are used to evaluate the algorithm using PFSP benchmarks with job sizes between 20 and 500. The tests indicate that the performance of the proposed HCSA is significantly superior to that of other algorithms.

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