| dc.description.abstract | The case company studied in this thesis is the industry′s index elevator brand, and it still maintains excellent sales performance and develops more models that cater to customers′ cost needs under the changing market pattern. As a long-established and long-established elevator brand, the case company also faces the decline in labor benefits brought by the aging of direct personnel, and the chaos of production scheduling caused by diversified models.
In the past ten years, the case company successfully reduced inventory pressure and product costs under various system and management attempts, but the relative situation is that the shipment of arrears and delays in customer urgent needs frequently occur, but additional Generate unnecessary shipping burden.
For the above production scheduling problem, some scholar sages conducted various heuristic production scheduling studies on the traditional industry of the zero-work production type, such as Tong Weixin′s genetic algorithm research on elevator production scheduling. The study used genetic algorithms to target the research company ’s standard process for workstations during a specific period of time and performed calculations to obtain significant reductions in man-hours. However, in reality, customers will not adjust the construction progress according to the factory′s schedule, so if they can learn the production and shipment experience of specific models in the past through deep learning, they will be able to provide a certain amount of advance notice to the production materials personnel and carry out Workstation progress adjustment.
This paper uses the actual shipment date and workstation progress date of all the elevators produced by the case company from 2015 to 2020 in the factory. After the delayed shipment and the normal shipment through the artificial label, the depth of the learning accuracy is as high as 52% after listing the elevator characteristics. This means that the complicated zero-work scheduling problem can get a reference direction through deep learning, and it also means that the traditional industry′s past reliance on experience to schedule production schedules will not face difficulties with personnel changes. | en_US |