Nowadays, the nylon ropes have been widely applied in many different areas such as fishing, hiking, hoisting, and pull traction. Presently, there are some commercial machines available for nylon rope winding onto a shipping reel before shipment. However, they still need manual operation relying on rapid movement of the hands, also requiring full coordination with the winding action. This operation procedure makes it very difficult to continuously achieve the winding process fast and accurately due to high dependence on human operation but also some forms of diameter deviation occurred from the chemical process. In reality, even a slight human mistake may result in uneven winding outcome. Therefore, an adaptive reeling schedule method using a simple algebra computation is developed to resolve this problem. For this reason, an automatic rope winding control system to adaptively suit various rope diameters with an error involved is developed using adaptive decision-making (ADM) algorithm based on a simple arithmetic operation. First, the winding turn in each layer of drum is obtained from the quotient of the drum length divided by average rope diameter of each layer. Second, through the accumulated winding turn divided by the winding turn of a layer, the quotient represents the number of winding layers, and the remainder is the current winding turn of the layer. Third, the turning location to instantly swift to forward or backward winding direction is therefore determined when the winding turn reaches the target of each layer. The proposed model is realized on the basis of automation mechanism integrated with programming logical controller (PLC), human machine interface (HMI), optical micrometer, server motor, linear motor, tension sensor and optical motor, etc. The experimental results verify that the proposed system can reach 100% reality performance in the rope diameters of 3.8 mm, 4.0 mm, and 6.5 mm. Compared with the traditional winding method taking 15 min, the operation time is reduced to 11 min. Accordingly, it is confirmed that the proposed system is superior to traditional human-handling machines in term of rapidness, robustness, and accuracy. [ABSTRACT FROM AUTHOR]