A General Variable Neighborhood Search Algorithm for the No-Idle Permutation Flowshop Scheduling Problem

2013 ◽  
pp. 24-34 ◽  
Author(s):  
M. Fatih Tasgetiren ◽  
Ozge Buyukdagli ◽  
Quan-Ke Pan ◽  
Ponnuthurai Nagaratnam Suganthan
Keyword(s):  
Variable Neighborhood Search ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Permutation Flowshop ◽  
Flowshop Scheduling Problem ◽  
Neighborhood Search Algorithm

A Hybrid Differential Evolution Algorithm Based on Dynamic Variable Neighborhood Search for Permutation Flowshop Scheduling Problem

2016 ◽  
Vol 835 ◽  
pp. 847-857 ◽  
Author(s):  
Wen Bo Liu
Keyword(s):  
Differential Evolution ◽  
Variable Neighborhood Search ◽  
Differential Evolution Algorithm ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Dynamic Variable ◽  
Permutation Flowshop ◽  
Evolution Algorithm ◽  
Flowshop Scheduling Problem

Permutation flowshop scheduling problem (PFSP) is a classical NP-hard combinatorial optimization problem, which provides a challenge for evolutionary algorithms.Since it has been shown that simple evolutionary algorithms cannot solve the PFSP efficiently, local search methods are often adopted to improve the exploitation ability of evolutionary algorithms. In this paper, a hybrid differential evolution algorithm is developed to solve this problem. This hybrid algorithm is designed by incorporating a dynamic variable neighborhood search (DVNS) into the differential evolution. In the DVNS, the neighborhood is based on multiple moves and its size can be dynamically changed from small to large so as to obtain a balance between exploitation and exploration. In addition, a population monitoring and adjusting mechanism is also incorporated to enhance the search diversity and avoid being trapped in local optimum.Experimental results on benchmark problems illustrated the efficiency of the proposed algorithm.

Neighborhood Search Heuristic for 2-Machine Flowshop Scheduling Problem with Limited Waiting Times

2013 ◽  
Vol 411-414 ◽  
pp. 1894-1897 ◽  
Author(s):  
Bai Lin Wang ◽  
Tie Ke Li ◽  
Heng Bo Ge
Keyword(s):  
Waiting Time ◽  
Search Algorithm ◽  
Waiting Times ◽  
Time Constraints ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Effectiveness And Efficiency ◽  
Flowshop Scheduling Problem ◽  
Neighborhood Search Algorithm

The flowshop scheduling problem with limited waiting time constraints widely exists in the production process featured by high temperature and continuity. The constraints require that the waiting time of any job between two consecutive machines is not greater than a given upper bound. In this paper, the problem with two-machine settings and the objective of makespan is studied. First, a lower bound and some characters of minimum makespan are analyzed. Further, a solving idea is suggested by a transformation into an asymmetry TSP. Based on these characteristics and the solving idea, a neighborhood search algorithm embedding a modified Lin-Kernighan heuristic is presented for the problem. Numerical results demonstrated the effectiveness and efficiency of the algorithm.

scholarly journals Synergy of Genetic Algorithm with Extensive Neighborhood Search for the Permutation Flowshop Scheduling Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Rong-Chang Chen ◽  
Jeanne Chen ◽  
Tung-Shou Chen ◽  
Chien-Che Huang ◽  
Li-Chiu Chen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Neighborhood Search ◽  
Local Optimum ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Permutation Flowshop ◽  
Hybrid Genetic Algorithms ◽  
Combinatorial Optimization Problems ◽  
Flowshop Scheduling Problem

The permutation flowshop scheduling problem (PFSP) is an important issue in the manufacturing industry. The objective of this study is to minimize the total completion time of scheduling for minimum makespan. Although the hybrid genetic algorithms are popular for resolving PFSP, their local search methods were compromised by the local optimum which has poorer solutions. This study proposed a new hybrid genetic algorithm for PFSP which makes use of the extensive neighborhood search method. For evaluating the performance, results of this study were compared against other state-of-the-art hybrid genetic algorithms. The comparisons showed that the proposed algorithm outperformed the other algorithms. A significant 50% test instances achieved the known optimal solutions. The proposed algorithm is simple and easy to implement. It can be extended easily to apply to similar combinatorial optimization problems.

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