A simulated annealing algorithm approach to hybrid flow shop scheduling with sequence-dependent setup times

One of the scheduling problems with various applications in industries is hybrid flow shop. In hybrid flow shop, a series of n jobs are processed at a series of g workshops with several parallel machines in each workshop. To simplify the model construction in most research on hybrid flow shop schedu...

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Bibliographic Details
Published in:Journal of intelligent manufacturing 2011-12, Vol.22 (6), p.965-978
Main Authors: Mirsanei, H. S., Zandieh, M., Moayed, M. J., Khabbazi, M. R.
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Business and Management
Control
Flow
Genetic algorithms
Heuristic
Hybrid flow shop
Industrial Economics and Management
Industriell ekonomi och organisation
Integer programming
Intelligent systems
Job shops
Linear programming
Literature reviews
Machines
Makespan
Manufacturing
Mechatronics
Meta-heuristic
Processes
Production
Robotics
Scheduling
Sequence-dependent setup times
Simulated annealing
Studies
Traveling salesman problem
Workshops
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Summary:One of the scheduling problems with various applications in industries is hybrid flow shop. In hybrid flow shop, a series of n jobs are processed at a series of g workshops with several parallel machines in each workshop. To simplify the model construction in most research on hybrid flow shop scheduling problems, the setup times of operations have been ignored, combined with their corresponding processing times, or considered non sequence-dependent. However, in most real industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacturing, hybrid flow shop problems have sequence-dependent setup times (SDST). In this research, the problem of SDST hybrid flow shop scheduling with parallel identical machines to minimize the makespan is studied. A novel simulated annealing (NSA) algorithm is developed to produce a reasonable manufacturing schedule within an acceptable computational time. In this study, the proposed NSA uses a well combination of two moving operators for generating new solutions. The obtained results are compared with those computed by Random Key Genetic Algorithm (RKGA) and Immune Algorithm (IA) which are proposed previously. The results show that NSA outperforms both RKGA and IA.
ISSN:0956-5515
1572-8145
1572-8145
DOI:10.1007/s10845-009-0373-8