Solving permutation flowshop scheduling problems with a discrete differential evolution algorithm

2016 ◽  
Vol 29 (2) ◽  
pp. 269-286 ◽  
Author(s):  
Valentino Santucci ◽  
Marco Baioletti ◽  
Alfredo Milani
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Scheduling Problems ◽  
Flowshop Scheduling ◽  
Permutation Flowshop ◽  
Evolution Algorithm ◽  
Discrete Differential Evolution

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.

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