WIP Windows的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列懶人包和總整理

應用雙層基因演算法於製造工廠餘物料運輸優化設計之研究

為了解決WIP Windows的問題，作者林壽山 這樣論述：

This study aims to optimize the information logistics (IL) design of a manufacturing execution system that controls the flow of goods in manufacturing factories. Corporate management is faced with the severe challenge of fierce price competition and sharp profit declines due to rising energy costs

and supply-demand imbalances, especially in heavy industry and traditional manufacturing industries. Important decisions in the industrial world rely on expert experience and existing knowledge, which can lead to the waste of logistics costs in the absence of systematic thinking and technical applic

ation. Therefore, in this study, the AI Genetic Algorithm was applied to the informatization of the logistics mode and the manufacturing execution system (MES) of a manufacturing factory (Company A) in order to improve the accuracy of the factory logistics and accurately dispatch the existing produc

tion resources, thus reducing excess costs and optimizing the logistics supply end. In addition, the AI Genetic Algorithm was applied in by-product transporting and logistics optimization research in a steel factory (Company C) with the expectation of solving the bottlenecks of traditional decision-

making and to optimize transporting logistics decisions based on AI. In the problem formulation, considering the path information, the vehicle path systemization, and the transporting demand frequency in the factory, a model for by-product transporting and logistics in the steel factory was establis

hed. The improved variable-length chromosome termination technique and the Dual-Layer Genetic Algorithm were proposed to effectively solve the problem of transporting in different zones. The experimental results showed that the zoning result obtained by this method had a slightly shorter total trans

porting time than the existing expert-based task scheduling but had far better fairness. In addition, the decision generation speed of this method was tens of minutes, which represented a marked improvement compared to the decision generation speed in the expert-based task scheduling, which requires

days.