Minimizing Total Flow Time in a Flow Shop with Blocking Using a Hybrid Variable Neighborhood Search and Simulated Annealing Algorithm

2014 ◽  
Vol 631-632 ◽  
pp. 57-61 ◽  
Author(s):  
Zhan Peng Xie ◽  
Chao Yong Zhang ◽  
Xin Yu Shao ◽  
Yong Yin

In this paper, a hybrid methodology that incorporates a simulated annealing (SA) approach into the framework of variable neighborhood search (VNS) is proposed to solve the blocking flow shop scheduling problem with the total flow time minimization. The proposed hybrid algorithm adopts SA as the local search method in the third stage of VNS, and uses a perturbation mechanism consisting of three neighborhood operators in VNS to diversify the search. To enhance the intensification search, best-insert operator is adopted to generate the neighbors in SA. To evaluate the performance of the proposed hybrid algorithm, computational experiments and comparisons were conducted on the well-known Taillard’s benchmark problems. The computational results and comparisons validate the effectiveness of the proposed algorithm.

2015 ◽  
Vol 766-767 ◽  
pp. 989-994 ◽  
Author(s):  
M. Saravanan ◽  
S. Joseph Dominic Vijayakumar ◽  
R. Srinivasan ◽  
S. Paul Singarayar

The Permutation Flow shop Scheduling Problem is a typical combinational optimization problem and has been proved to be strongly NP-hard. This paper deals with B-GRASP algorithm meta-heuristic in finding the solution and in analyzing the best the optimal schedule, thus minimizing the bi-objectives such as weighted makespan and total flow time. The proposed approach is evaluated using benchmark problems taken from Taillard and compared with simulated annealing algorithm. Computational experiments indicate that the proposed B-GRASP algorithm is a feasible and effective approach for the multiobjective problem.


2021 ◽  
pp. 275-288
Author(s):  
Hanan Ali Chachan ◽  
Faez Hassan Ali

A hybrid particulate swarm optimization (hybrid) combination of an optimization algorithm of the particle swarm and a variable neighborhood search algorithm is proposed for the multi-objective permutation flow shop scheduling problem (PFSP) with the smallest cumulative completion time and the smallest total flow time. Algorithm for hybrid particulate swarm optimization (HPSO) is applied to maintain a fair combination of centralized search with decentralized search. The Nawaz-Enscore-Ham )NEH) heuristic algorithm in this hybrid algorithm is used to initialize populations in order to improve the efficiency of the initial solution. The method design is based on ascending order (ranked-order-value, ROV), applying the continuous PSO algorithm to the PFSP, introducing the external archive set storage Pareto solution, and using a hybrid strategy that combines strong dominance and aggregation distance to ensure the distribution of the solution set. We adopted the Sigma method and the roulette method, based on the aggregation distance, to select the global optimal solution. A variable neighborhood search algorithm was proposed to further search the Pareto solution in the external set. The suggested hybrid algorithm was used to solve the Taillard test set and equate the test results with the SPEA2 algorithm to check the scheduling algorithm’s efficacy.


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