scholarly journals A hybrid multi-objective teaching-learning based optimization for scheduling problem of hybrid flow shop with unrelated parallel machine

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Cunli Song
Keyword(s):  
Flow Shop ◽  
Parallel Machine ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Unrelated Parallel Machine ◽  
Multi Objective ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Diversified teaching-learning-based optimization for fuzzy two-stage hybrid flow shop scheduling with setup time

2021 ◽  
pp. 1-15
Author(s):  
Deming Lei ◽  
Bingjie Xi
Keyword(s):  
Flow Shop ◽  
Setup Time ◽  
Distributed Scheduling ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Hybrid Flow Shop Scheduling ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Distributed scheduling has attracted much attention in recent years; however, distributed scheduling problem with uncertainty is seldom considered. In this study, fuzzy distributed two-stage hybrid flow shop scheduling problem (FDTHFSP) with sequence-dependent setup time is addressed and a diversified teaching-learning-based optimization (DTLBO) algorithm is applied to optimize fuzzy makespan and total agreement index. In DTLBO, multiple classes are constructed and categorized into two types according to class quality. Different combinations of global search and neighborhood search are used in two kind of classes. A temporary class with multiple teachers is built based on Pareto rank and difference index and evolved in a new way. Computational experiments are conducted and results demonstrate that the main strategies of DTLBO are effective and DTLBO has promising advantages on solving the considered problem.

Study on Multi-Objective Optimization of Hybrid Flow Shop Scheduling Problem

2014 ◽  
Vol 1082 ◽  
pp. 529-534
Author(s):  
Zheng Ying Lin ◽  
Wei Zhang
Keyword(s):  
Flow Shop ◽  
Economic Benefits ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Hybrid Flow Shop Scheduling

Due to several mutual conflicting optimized objectives in the hybrid flow shop scheduling problem, its optimized model, including three objectives of make-span, flow-time and tardiness, was firstly set up, instead of the single optimized objective. Furthermore, in order to improve the optimized efficiency and parallelism, after comparing the normal multi-objective optimized methods, an improved NSGA-II algorithm with external archive strategy was proposed. Finally, taking a piston production line as example, its performance was tested. The result showed that the multi-objective optimization of hybrid flow shop scheduling based on improved NSGA-II provided managers with a set of feasible solutions for selection in accordance to their own preference. Therefore the decision could be made more scientific and efficient, and thus brings to the factory more economic benefits.

scholarly journals An Improved Genetic Algorithms for Multi-objective Hybrid Flow-shop Scheduling Problem

2019 ◽  
Vol 95 ◽  
pp. 03008
Author(s):  
Zhao Zijin ◽  
Wang aimin ◽  
Ge Yan ◽  
Lin Lin
Keyword(s):  
Genetic Algorithms ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Production Cycle ◽  
Local Optimum ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Hybrid Flow Shop Scheduling

To deal with the multi-objective hybrid flow Shop Scheduling Problem (HFSP), an improved genetic algorithms based on parallel sequential moving and variable mutation rate is proposed. Compared with the traditional GA, the algorithm proposed in this paper uses the two-point mutation rule based on VMR to find the global optimum which can make the algorithm jump out of the local optimum as far as possible, once it falls into the local optimum quickly. Decoding rules based on parallel sequential movement ensures that the artifact can start processing in time, so that the buffer between stages in the flow-shop is as little as possible, and the production cycle is shortened. Finally, a program was developed with the actual data of a workshop to verify the feasibility and effectiveness of the algorithm. The result shows that the algorithm achieves satisfactory results in all indexes mentioned above.

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