Pareto-based Hybrid Multi-Objective Evolutionary Algorithm for Flexible Job-shop Scheduling Problem

2013 ◽  
Vol 9 (1) ◽  
pp. 36-45 ◽  
Author(s):  
Keyword(s):  
Evolutionary Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Multi Objective

Multi-objective flexible job shop scheduling problem using variable neighborhood evolutionary algorithm

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740072 ◽  
Author(s):  
Chun Wang ◽  
Zhicheng Ji ◽  
Yan Wang
Keyword(s):  
Evolutionary Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Environmental Selection

In this paper, multi-objective flexible job shop scheduling problem (MOFJSP) was studied with the objects to minimize makespan, total workload and critical workload. A variable neighborhood evolutionary algorithm (VNEA) was proposed to obtain a set of Pareto optimal solutions. First, two novel crowded operators in terms of the decision space and object space were proposed, and they were respectively used in mating selection and environmental selection. Then, two well-designed neighborhood structures were used in local search, which consider the problem characteristics and can hold fast convergence. Finally, extensive comparison was carried out with the state-of-the-art methods specially presented for solving MOFJSP on well-known benchmark instances. The results show that the proposed VNEA is more effective than other algorithms in solving MOFJSP.

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