An adaptive multi-population genetic algorithm to solve the multi-objective group scheduling problem in hybrid flexible flowshop with sequence-dependent setup times

2010 ◽  
Vol 22 (6) ◽  
pp. 979-989 ◽  
Author(s):  
M. Zandieh ◽  
N. Karimi
Keyword(s):  
Population Genetic ◽  
Setup Times ◽  
Group Scheduling ◽  
Scheduling Problem ◽  
Flexible Flowshop ◽  
Sequence Dependent Setup ◽  
Population Genetic Algorithm ◽  
Dependent Setup Times ◽  
Hybrid Flexible Flowshop ◽  
Sequence Dependent Setup Times

scholarly journals Bi-objective unrelated parallel machines scheduling problem with worker allocation and sequence dependent setup times considering machine eligibility and precedence constraints

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Reza Alizadeh Foroutan ◽  
Javad Rezaeian ◽  
Milad Shafipour
Keyword(s):  
Precedence Constraints ◽  
Setup Times ◽  
Scheduling Problem ◽  
Multi Objective ◽  
Worker Allocation ◽  
Sequence Dependent ◽  
Machine Eligibility ◽  
Sequence Dependent Setup ◽  
Dependent Setup Times ◽  
Sequence Dependent Setup Times

<p style='text-indent:20px;'>In today's competitive world, scheduling problems are one of the most important and vital issues. In this study, a bi-objective unrelated parallel machine scheduling problem with worker allocation, sequence dependent setup times, precedence constraints, and machine eligibility is presented. The objective functions are to minimize the costs of tardiness and hiring workers. In order to formulate the proposed problem, a mixed-integer quadratic programming model is presented. A strategy called repair is also proposed to implement the precedence constraints. Because the problem is NP-hard, two metaheuristic algorithms, a multi-objective tabu search (MOTS) and a multi-objective simulated annealing (MOSA), are presented to tackle the problem. Furthermore, a hybrid metaheuristic algorithm is also developed. Finally, computational experiments are carried out to evaluate different test problems, and analysis of variance is done to compare the performance of the proposed algorithms. The results show that MOTS is doing better in terms of objective values and mean ideal distance (MID) metric, while the proposed hybrid algorithm outperforms in most cases, considering other employed comparison metrics.</p>

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