Two stage approach to address the flexible job shop scheduling problem using an evolutionary algorithm considering random machine breakdowns

2020 ◽  
Author(s):  
Rylan H. Caldeira ◽  
A. Gnanavelbabu ◽  
B. Akkash ◽  
U. Avinash ◽  
T. Manojkumar ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Two Stage ◽  
Machine Breakdowns ◽  
Shop Scheduling

scholarly journals A Two-Individual Based Evolutionary Algorithm for the Flexible Job Shop Scheduling Problem

2019 ◽  
Vol 33 ◽  
pp. 2262-2271 ◽  
Author(s):  
Junwen Ding ◽  
Zhipeng Lü ◽  
Chu-Min Li ◽  
Liji Shen ◽  
Liping Xu ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
State Of The Art ◽  
Computational Time ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

Population-based evolutionary algorithms usually manage a large number of individuals to maintain the diversity of the search, which is complex and time-consuming. In this paper, we propose an evolutionary algorithm using only two individuals, called master-apprentice evolutionary algorithm (MAE), for solving the flexible job shop scheduling problem (FJSP). To ensure the diversity and the quality of the evolution, MAE integrates a tabu search procedure, a recombination operator based on path relinking using a novel distance definition, and an effective individual updating strategy, taking into account the multiple complex constraints of FJSP. Experiments on 313 widely-used public instances show that MAE improves the previous best known results for 47 instances and matches the best known results on all except 3 of the remaining instances while consuming the same computational time as current state-of-the-art metaheuristics. MAE additionally establishes solution quality records for 10 hard instances whose previous best values were established by a well-known industrial solver and a state-of-the-art exact method.

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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