Intelligent Design of Scheduling in Dynamic Environments of Flexible Flow Shop
A production system that is constructed by multiple production lines, machines, single or multi-stage flow shop facilities is classified as a flexible flow shop system (FFS). Flexible flow shop scheduling offers the flexibility of production and job sequencing with more than one machine in a single...
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| Format: | Thesis |
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2019
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| Summary: | A production system that is constructed by multiple production lines, machines, single or multi-stage flow shop facilities is classified as a flexible flow shop system (FFS). Flexible flow shop scheduling offers the flexibility of production and job sequencing with more than one machine in a single stage. The duplication of the machines in certain stages can enhance the overall capacities, improve flexibility and reduce bottlenecks in some productions. Scheduling involves allocating resources to tasks over a specific period in order to fulfil one or more objectives. This process involves a search for job order and job sequencing to obtain an optimal schedule with the highest utilisation and efficiency. However, many industries are facing high tardiness and high makespan scheduling problem. They have a significant impact on manufacturing costs. The scheduling problems can be solved by considering three aspects, namely the definition of scheduling objectives, the selection of a scheduling method and the identification of scheduling environment. One of the drawbacks of most published methods is that they are ineffective in solving multi-objectives scheduling problem in a real life dynamic industrial environment. Hence, one of the main objectives of this research is to design an intelligent optimisation model by combining both metaheuristic and dispatching rules method for FFS scheduling in static and dynamic environments. The proposed model is designed to include both time-related and jobrelated objectives to handle FFS scheduling problems. In addition, the modelling of the FFS under different dynamic environments is proposed. In the model, the resources and job related are included. In the resources related events such as machine breakdown condition is selected due to the fact that it has a great impact on the production schedule. On the other hand, for the jobs related events such as the change of processing time and due date are considered because these two events are most frequently happened in production lines. Furthermore, dynamic demand which involves the change of the job quantity, namely lot-sizing, is also included in the model. The model is equipped with a capability of searching for an optimal schedule and providing this optimal schedule instantly in a real-life production environment. |
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