Hybrid flowshop scheduling with batch-discrete processors and machine maintenance in time windows

This research considers a two-stage hybrid flowshop scheduling problem with challenging characteristics substantiated by the complexity of the problem in a collaborating company. Multiple (three) parallel machines are involved in the first stage and only one machine in the second stage. Stage 1 para...

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Bibliographic Details
Published in:International journal of production research 2011-03, Vol.49 (6), p.1575-1603
Main Authors: Luo, Hao, Huang, George Q., Feng Zhang, Ying, Yun Dai, Qing
Format: Article
Language:English
Subjects:
Algorithms
Analogies
Applied sciences
blocking scheduling
Buffer storage
Collaboration
Complex systems
Complexity
Exact sciences and technology
genetic algorithm
Genetic algorithms
Inventory control, production control. Distribution
Job shops
Maintenance
Mathematical models
multistage flowshop scheduling
Operational research and scientific management
Operational research. Management science
Operations research
Processors
Production scheduling
Schedules
Scheduling
Scheduling, sequencing
Studies
Windows (intervals)
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Summary:This research considers a two-stage hybrid flowshop scheduling problem with challenging characteristics substantiated by the complexity of the problem in a collaborating company. Multiple (three) parallel machines are involved in the first stage and only one machine in the second stage. Stage 1 parallel machines are able to process multiple jobs simultaneously but the jobs must be sequentially setup one after another with the loading time depending on the processing time of the stage 2 machine. A blocking environment exists between the two stages with no intermediate buffer storage. In order to reduce the complexity, multiple simultaneous jobs are grouped into batches according to their similarities. Batches can then be considered as basic units for scheduling to determine which stage 1 machine and in what sequence batches are loaded. A genetic algorithm is used to obtain near-optimal schedules mainly by minimising the makespan. The proposed model and solution algorithm are applied to solve the problem in the collaborating company under a set of complicated rules and constraints. Comprehensive studies are conducted with real-life data. The results are consistent with the company's operational principles and are superior compared with the manual schedules.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207541003610262