Reentrant hybrid flow shop scheduling problem with non-anticipatory sequence-dependent setup time

2020 ◽  
Vol 36 (4) ◽  
pp. 560
Author(s):  
Lu Chen ◽  
Jinfeng Wang ◽  
Yi Zhu
Keyword(s):  
Flow Shop ◽  
Setup Time ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Sequence Dependent ◽  
Sequence Dependent Setup Time ◽  
Hybrid Flow Shop Scheduling ◽  
Sequence Dependent Setup

Comparison of Heuristic for Flow Shop Scheduling Problems with Sequence Dependent Setup Time

2011 ◽  
Vol 339 ◽  
pp. 332-335 ◽  
Author(s):  
Parinya Kaweegitbundit
Keyword(s):  
Flow Shop ◽  
Memetic Algorithm ◽  
Setup Time ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Sequence Dependent ◽  
Sequence Dependent Setup Time ◽  
Sequence Dependent Setup ◽  
Makespan Criterion

This paper considers flow shop scheduling problems with sequence dependent setup time. The makespan criterion has been considered. In this paper presented a comparison of three heuristics for solves this problem. The memetic algorithm, genetic algorithm and NEH heuristic have been compared. In the experimental, the result from memetic algorithm is maximum the best solution. Therefore, the MA heuristic outperforms other heuristic.

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.

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