The development of integrated planning and scheduling framework for dynamic and reactive environment of complex manufacturing problem
Flexible manufacturing system (FMS) is a manufacturing system in which there is some amount of flexibility which allows the system to react in the case of changes, whether predicted or unpredicted. Two major activities in manufacturing system are process planning and production scheduling. The curre...
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| Main Authors: | , , , |
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| Format: | Monograph |
| Language: | English |
| Published: |
Faculty of Computer Science and Information System
2008
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/6701/1/79105.pdf http://eprints.utm.my/id/eprint/6701/ http://www.penerbit.utm.my |
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| Summary: | Flexible manufacturing system (FMS) is a manufacturing system in which there is some amount of flexibility which allows the system to react in the case of changes, whether predicted or unpredicted. Two major activities in manufacturing system are process planning and production scheduling. The current trends in present manufacturing industries require the ability to quickly integrate process plans for new orders into the existing production schedule to best accommodate the current load of the facility, the status of machines, and the availability of raw materials. The goal of this project is to propose an integrated planning and scheduling system for a flexible and complex manufacturing environment. Firstly, in Chapter 1, we give an overview of the real problem occurred in the field of dynamic scheduling. A hybrid genetic algorithm (HGA) for solving the dynamic job shop problem is proposed to solve the dynamic scheduling. Secondly, in Chapter 2 we described the modeling of the real world manufacturing processes using Petri Nets. We present two models of manufacturing process, namely machine model and process model. The goals of these models are to understand the behavior of the machine and to demonstrate the dynamic behavior of production processes, respectively. Next, multi-population directed genetic algorithms (MDGA) have been used to generate a number of optimal operation sequences for a real world manufacturing problem which is elaborated in Chapter 3. Then, in Chapter 4, a modified particle swarm optimization (MPSO) has been used to generate a feasible operation sequence for a real world manufacturing problem. Lastly, in Chapter 5, we investigate the problem of integrating new rush orders into the current schedule of a real world FMS. The aim is to introduce match up strategy with genetic algorithms (GA) that modify only part of the schedule in order to accommodate new arriving jobs. |
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