Integrating sequence-dependent setup times and blocking in hybrid flow shop scheduling to minimize total tardiness

This study addresses the minimization of total tardiness in a hybrid flow shop scheduling problem with sequence-dependent setup times and blocking constraints. Each production stage includes multiple machines, and there are no buffers between the stages. The setup time required to process a job depends on the previously processed job. Two mixed-integer linear programming models are developed to formulate the problem. Moreover, an iterative local search algorithm and hybrid genetic algorithms are proposed to have quality solutions with minimal computational efforts. Several computational tests are conducted to tune the heuristic parameters for better performance. Computational experiments are carried out to evaluate the performance of solution methodologies in terms of quality and time. The results indicate that while mixed-integer programming models can solve small-size problem instances, they are not capable of solving large-sized instances. However, the proposed heuristic algorithms find quality solutions for all instances in a very short time.

Authors

Publication Details

Journal
International Journal of Industrial Engineering Computations
Published
2024-12-06
DOI
https://doi.org/10.5267/j.ijiec.2024.10.005
Citations
2
Primary Topic
Scheduling and Optimization Algorithms
Type
article
Field-Weighted Citation Impact
0.70
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article

Integrating sequence-dependent setup times and blocking in hybrid flow shop scheduling to minimize total tardiness

Atıl Kurt
2 citations
International Journal of Industrial Engineering Computations
Scheduling and Optimization Algorithms
0.70
article

Integrating sequence-dependent setup times and blocking in hybrid flow shop scheduling to minimize total tardiness

Atıl Kurt
article en
2 citations

Abstract

This study addresses the minimization of total tardiness in a hybrid flow shop scheduling problem with sequence-dependent setup times and blocking constraints. Each production stage includes multiple machines, and there are no buffers between the stages. The setup time required to process a job depends on the previously processed job. Two mixed-integer linear programming models are developed to formulate the problem. Moreover, an iterative local search algorithm and hybrid genetic algorithms are proposed to have quality solutions with minimal computational efforts. Several computational tests are conducted to tune the heuristic parameters for better performance. Computational experiments are carried out to evaluate the performance of solution methodologies in terms of quality and time. The results indicate that while mixed-integer programming models can solve small-size problem instances, they are not capable of solving large-sized instances. However, the proposed heuristic algorithms find quality solutions for all instances in a very short time.

International Journal of Industrial Engineering ComputationsVol. 16(1)
Openalex Percentile: Top 24%
Scheduling and Optimization Algorithms
0.70
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