Research On The Optimization Of Mass Customazation Scheduling

Manufacturing industry came to Mass Customization (MC) production because of the intense market competition and individual customer demand. Compared with the Make-to-Stock Strategy of mass production (MP), Mass Customization adopted the Make-to-Order Strategy, and it has the challenges of cost and delivery time, so how to response the customization demands of client quickly with low cost is a basic task of MC, and the scheduling problem is critical one to actualize low cost and quick response to the customization demands. Therefore, this paper took MC scheduling problem as the main research direction and it's optimize problem as the core content in mass customization production, synthetically used the optimize technique of Artificial Intelligence (AI), attempted to make contributions to practice scheduling theory in mass customization production.Based on the analyses of basic content and it's fulfill pattern of MC, the paper reformed the manufacture system of MC, according to the CODP (Customer order discoupling point), suggest to divided the system into two parts, one is the part process system and the other is fast assembly system faced to customization demands. There are material limits and capacity limits in MC production, based the class of MC production, the paper modeled the scheduling problem of MC production, put forward that the buffer should be take into account according to the methods of job family when scheduling the parts process, and the material limits of customization should be consider with capacity limits together when scheduling the assembly production.The paper improved the Genetic Algorithm (GA), then, based on the plat of matlab designed the scheduling optimize problem of MC. At last, the paper emulated the design of MC scheduling optimize, the result proved that the Model of MC scheduling optimize problem is logical and solved the diplex limits, material limits and capacity limits, of MC production, it was helpful to exactly deicide the deadline of delivery and lower the cost of production.