Abstract
The distributed integration of process planning and scheduling (DIPPS) aims to simultaneously arrange the two most important manufacturing stages, process planning and scheduling, in a distributed manufacturing environment. Meanwhile, considering its advantage corresponding to actual situation, the triangle fuzzy number (TFN) is adopted in DIPPS to represent the machine processing and transportation time. In order to solve this problem and obtain the optimal or near-optimal solution, an extended genetic algorithm (EGA) with innovative three-class encoding method, improved crossover, and mutation strategies is proposed. Furthermore, a local enhancement strategy featuring machine replacement and order exchange is also added to strengthen the local search capability on the basic process of genetic algorithm. Through the verification of experiment, EGA achieves satisfactory results all in a very short period of time and demonstrates its powerful performance in dealing with the distributed integration of fuzzy process planning and scheduling (DIFPPS).
Highlights
Nowadays in manufacturing fields, there are desperate needs for the integration of process planning and scheduling and distributed manufacturing
The rest of the paper is arranged as follows: in Section 2, we review the related work addressing integrated processing planning and scheduling (IPPS) and fuzzy processing time in manufacturing; in Section 3, the DIFPPS model is proposed, and the creative extended genetic algorithm (EGA) is constructed to deal with it; in Section 4, a two-part experiment with a case study and several comparisons is conducted to demonstrate the capability of EGA in solving DIFPPS; in Section 5, we draw our conclusion
The flexible job shop scheduling problem (FJSSP) which is more complicated than traditional JSSP in allowing an operation to be processed on any of the machines in a corresponding set along different routes is gradually equipped with fuzzy processing time [22]
Summary
There are desperate needs for the integration of process planning and scheduling and distributed manufacturing. The conventional job shop scheduling problem (JSSP) is still a hotspot in the studies of manufacturing researchers and has great potential to make progress, its separation from process planning inevitably obstructs the improvement of manufacturing efficiency and disables it in tackling complicated manufacturing environment [1]; on the other hand, the distribution feature of resources such as raw materials, manpower, and technologies propels the enterprise to operate in a more decentralized way and has gradually drawn the attention of many researchers [2,3,4] Besides these two concerns, there is an urge to represent the processing time in manufacturing with fuzzy values. The rest of the paper is arranged as follows: in Section 2, we review the related work addressing IPPS and fuzzy processing time in manufacturing; in Section 3, the DIFPPS model is proposed, and the creative EGA is constructed to deal with it; in Section 4, a two-part experiment with a case study and several comparisons is conducted to demonstrate the capability of EGA in solving DIFPPS; in Section 5, we draw our conclusion
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