Shop Floor Scheduling Identical Parallel-Machine Production with Mold for Shoes Factory

• Main Authors: Yung-Lung Chen, 陳永隆
• Other Authors: Li-Chih Wang
• Format: Others
• Language: zh-TW
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/28846780288499976775

碩士 === 東海大學 === 工業工程學系 === 90 === The production process of products is becoming more and more complex, and the life cycle time of products is getting short, it makes the factors that affect the scheduling problem to be more complicated. To simplify the complicated shop floor scheduling, in recently researches, they applied the theory of constraint to develop new scheduling method — to plan and manage the bottleneck resource. There are two strategies to ship on time when the bottleneck is overloading, the first is outsourcing the process; the other is to increase the capacity for bottleneck. If there are over two machines to work with the same process, it is the parallel-machine scheduling problem. The parallel-machine scheduling problem is common in shoes factory, the most process use parallel machines or equipments to achieve the required capacity. The characteristic of shoes business is that every order contains size run, the different size use different mold for production. Every size in one order can be processed independently in any machine of some parallel machines. It is very complicated to schedule every order and every size in every parallel machine. It is hard to prioritize the orders and size. This research will provide a heuristic scheduling method — Size Critical Ratio - for solving the parallel-machine production problem in shoe factory. And dispatch the orders and jobs to every parallel machine. This research will develop a dispatching algorithm to minimize the total tardiness, and prioritize every job for production. According the algorithm, we will code a computer program to do the experiment. The experiment will give the result of scheduling for the algorithm. And the program will provide shop floor scheduling for every machine. In addition, we also use genetic algorithm (GA) to solve this problem, to compare these two different scheduling methods.